小举人

How Coffee Futures Came to Be

2026-10-12T00:00:00Z

Opening: A Latte That Got More Expensive

Hi everyone, welcome to Cold-Knowledge Blind Box.

I've been buying lattes downstairs in the morning lately, and I noticed the price went up again. I muttered to myself: have coffee beans gotten expensive again?

I looked it up when I got home, and yes, they have. Over the past couple of years, New York arabica coffee futures climbed to all-time highs. In October 2025, they touched about $4.38 a pound intraday.

So I got curious. Coffee beans grow on hillsides in Brazil and Colombia. Why is their price set on an electronic screen in New York?

What's even stranger is that the market behind that screen wasn't created for speculation in the first place. Quite the opposite. It was set up by a group of merchants who had been badly burned by speculation, and who came together to fix it.

Let's start the story there.

The Years Before Futures

Let's begin with a question. Before futures existed, how was coffee bought and sold?

Simple: you paid cash and took the goods. An importer ordered a shipload of beans from Brazil. The ship spent a few weeks at sea, arrived in New York Harbor, and then the importer sold the beans to wholesalers.

Coffee being loaded at the port of Santos, Brazil, around 1880. Photo by Marc Ferrez, public domain, via Wikimedia Commons

The problem lies in those few weeks.

When the ship set off, the beans might have been worth one price. By the time it reached port, the price could have dropped a lot. If you're holding a whole shipload and the price slips even a little, you lose a fortune. Back then, nobody could help you share that risk. Every rise and fall was yours alone to bear.

So someone thought: since prices are set by supply, if I buy up all the beans on the market, won't I be the one who decides the price?

Yes. And more than one person thought so.

1880: Three Men and a Corner

In the 1870s, three major American coffee merchants were later nicknamed the "Triumvirate." One was Kimball, a young man from Boston. The other two were New York merchants Arnold and Dyer. Let's just call Arnold "Old A" for short, since at the time he was known as the "Coffee King."

What these three did is called a "corner" in trader jargon: buying up stock frantically, controlling inventory, and driving prices up.

At first it went smoothly. But here's the thing about coffee: when the price goes up, Brazil plants and ships like crazy.

In 1880, beans kept pouring in. The port warehouses were stacked full, and no matter how hard they pushed, prices wouldn't rise. One account says that during the first eleven months of that year, New York coffee prices slid from 24 cents a pound to around 16 cents.

Then something dramatic happened. On December 4, Kimball suddenly died. He reportedly passed away after a card game. He was only 42.

Once he was gone, the company fell apart. Creditors saw trouble and came knocking. Three days later, on December 7, Old A's company declared bankruptcy. When the books were examined, he owed more than $2 million, and his assets covered only about half of that.

Then came a chain of collapses. For a while, the entire New York coffee trade nearly ground to a halt.

The Victims Open an Exchange

Here's the twist.

The people hit hardest by all this decided that they needed a market where everyone could lock in prices in advance, so that no single person could ever again choke the whole market.

The meeting took place in Old A's former office at 166 Pearl Street in New York.

On December 7, 1881, exactly one year after Old A's company went bankrupt, the New York Coffee Exchange was incorporated. Its first president? None other than the freshly bankrupt Coffee King himself, Old A.

On March 7, 1882, the exchange officially opened.

Hanover Square in New York in 1936, with the area near the entrance of the later New York Coffee Exchange building on the right. Photo by Berenice Abbott, New York Public Library collection, public domain, via Wikimedia Commons

When it first opened, nobody trusted it. One old merchant recalled that the exchange became "the laughingstock of the entire trade," with almost no business. But slowly, importers discovered that the thing actually worked well.

So What Is a Futures Contract?

Where's the usefulness?

Say you're still that importer, with a shipload of beans on the ocean. You're worried the price will fall by the time it arrives. So today, on the exchange, you sell a contract for future delivery, locking in the price now. If the spot price really does fall, the contract earns back your loss. The two offset each other, and in effect you've bought insurance on the price.

Then who takes the other side of your trade? Speculators. They bet that prices will rise. They might make a fortune or lose everything, but without them, nobody would be selling you that insurance.

So the essence of futures comes down to one line: moving price risk from people who don't want to bear it to people who are willing to.

There's a delicious irony here. The exchange was created to prevent a monopoly, so what happened? In 1904, an American novelist wrote a book called The Corner in Coffee. To research it, he interviewed a number of brokers, and in the preface he joked at his own expense: after learning about coffee speculation, he swore he would never touch coffee again except to drink it.

The exchange never eliminated the gambling. It just gave the gambling some rules.

The State Steps In: Brazil Hoards Coffee

Okay, the era of private corners was over. But soon a much bigger player entered the game.

Not a merchant. A country.

Brazil.

At the start of the twentieth century, most of the world's coffee came from Brazil. In 1906, Brazil foresaw a massive bumper harvest. Think about it: a bountiful harvest should be good news for farmers, right?

But for coffee, a bumper harvest means prices crash.

So in February 1906, Brazil's three largest coffee states, São Paulo, Minas Gerais and Rio de Janeiro, signed an agreement in a small town called Taubaté. The core of it was simple: the government would borrow foreign money to buy up the surplus coffee and hold it off the market, keeping prices up.

Historically, this is known as the "coffee valorization scheme."

A woodcut of a Brazilian coffee plantation from around 1839. Original by Johann Jacob Steinmann, public domain, via Wikimedia Commons

Look at the logic: it's exactly the same as what Old A and his two partners did. Hoarding, controlling supply. The only difference is that this time, the one doing the hoarding was a country.

And at first it worked. Prices were held up, coffee plantation owners in São Paulo made fortunes, and much of that money was later invested in São Paulo's industry.

The Great Depression: Brazil Burns Coffee

But, friends, can you hold prices up for one year? Ten years? Through a Great Depression?

In 1929, the New York stock market crashed, and nobody in the world had spare money. Coffee wouldn't sell, and the warehouses piled up.

What came next is the most surreal fact we'll talk about today.

Brazil started burning coffee.

Not a little. They burned it in huge batches, dumped it into the sea, and some accounts even say it was used as fuel for trains. According to a US law journal of the time, between June 1931 and August 1941, Brazil officially destroyed more than 72 million bags of coffee, each weighing 60 kilograms. A scholar's estimate puts the total at about 78 million bags by the time the policy ended in 1944, equal to roughly three years of world coffee exports at the time.

The world was struggling through hunger and depression, while on the other side of the ocean, one country was burning coffee to protect prices. That's what supply control looks like when it runs into a dead end.

The International Coffee Agreement and 1989

So what do you do when one country can't hold the line?

You bring in more countries.

In 1962, the International Coffee Agreement was signed. Coffee-producing and coffee-consuming countries sat down at one table and assigned export quotas to each exporting country. You can sell this much this year, and nobody sells more.

Why could it be signed at all? There's an important backdrop: the Cold War. The US didn't want to see Latin American countries plunged into unrest by coffee-driven poverty, so it was willing to back this quota system.

The system lasted nearly thirty years. One Yale economist estimated that during the agreement, coffee prices were on average pushed up by about 73 percent.

Then came 1989.

Think about it: what else happened in 1989? The Berlin Wall fell, and the Cold War was winding down. The reason the US had been propping up this system was gone.

At the same time, consumer tastes were changing. People increasingly wanted milder, higher-quality beans, but the quotas were still divided the old way. Brazil refused to give up its share, and the US pushed for rule changes. The talks dragged on for a year and collapsed.

On July 4, 1989, the International Coffee Organization announced the suspension of export quotas.

The floodgates opened, and beans poured out. The ICO's indicator price was around 116 US cents a pound in May 1989. A year later it had fallen to 73, and by May 1992 it was only 50 cents. It was cut in half in three years, and then some.

People later called this the "coffee crisis." Many coffee farmers never returned to good times after that.

So you see: from Old A to the Brazilian government, and then dozens of countries joining hands, humanity spent a century trying to control coffee prices by controlling supply. In the end, every attempt failed.

Today's Screen: ICE Coffee C

So if quotas are gone, who sets the price?

The answer: the market. That is, the screen we started with.

The New York Coffee Exchange from back then kept merging with other exchanges. In 1914 it added sugar and became the Coffee and Sugar Exchange; in 1979 it merged in cocoa and became the Coffee, Sugar and Cocoa Exchange; in 1998 it was absorbed into the New York Futures Exchange; and in 2007 it was acquired by the Intercontinental Exchange, ICE.

Today, the global benchmark price for arabica coffee is an ICE contract called "Coffee C." One contract covers 37,500 pounds of green beans, roughly 280 bags, and is priced in US cents per pound. The other main type of bean, robusta, trades mainly in London and is priced in US dollars per ton.

Here's a detail many people don't know.

Although most futures are bought and sold on screens, "Coffee C" can actually be delivered physically. Beans eligible for delivery must first be sent to warehouses certified by the exchange, and must pass professional cupping and grading. Only beans that qualify can be called "certified stock."

And different origins carry different premiums and discounts. For example, under the exchange's current rules, Colombian beans carry a premium for delivery, while Brazilian beans are discounted. The world's largest coffee producer delivers its beans at a discount in the very contract that prices its own coffee. That alone is a pretty fun fact.

Black Frost and Panic Buying

Okay, now that the rules are clear, let's look at a few of the most dramatic moments on this screen.

First, the "black frost."

In July 1975, southern Brazil was hit by the worst frost in fifty years. In Paraná state, it even snowed. According to a New York Times report at the time, about 1.5 billion coffee trees were killed or damaged by the cold.

Here's the key: the frost didn't damage the fruit already hanging on the trees. It damaged the trees' ability to bear fruit in the following year and the year after. So the real drop in output came one or two years later. The New York Times reported that in 1976, Brazil had expected to harvest 22 million bags, but in the end it harvested only 6 million.

How did prices move? Before the frost, Brazil's export price was under $70 a bag. By January 1977, it had climbed to $320, and it was still rising.

There's a particularly funny detail here. TIME magazine once wrote that Brazilian coffee farmers had a "traditional ritual" every winter: spreading frost rumors to push up prices on the exchange. And in 1975, the boy who cried wolf finally saw the wolf come.

The second moment is the last couple of years.

In 2024 and 2025, Brazil's weather kept going wrong. On top of tariffs and tight supply, arabica kept setting new records. In February 2025, Reuters used the phrase "panic buying" to describe the rally, which set new records on thirteen consecutive trading days. In October it hit another record, touching about $4.38 a pound intraday.

And this year? The situation has flipped. Brazil is having a record bumper harvest, and the ICO says its composite indicator price in September fell by almost 9 percent from August.

But one number has kept traders up at night: certified stocks. In mid-September this year, certified arabica inventory on the New York exchange dropped to just over 210,000 bags, reportedly the lowest since 1999. Brazil's September exports hit a 20-month high, yet inventories stayed low. Why? Because beans sold directly to roasters don't automatically end up in exchange warehouses.

So there's an awkward situation: lots of beans on the outside, very few beans eligible for delivery. In early October, arabica briefly climbed back above $3 a pound, then fell back again within a week.

Back to That Latte

Okay, we've covered a lot. Let's go back to the latte we started with.

Roasted arabica coffee beans from Minca, Colombia. Photo by Roger Burger, CC0, via Wikimedia Commons

When futures rise, does the coffee shop raise its prices right away? When futures fall, does it cut prices right away?

Friends, most likely not.

Think about it. In a latte, the coffee beans are only part of the cost. There's also milk, the cup, rent, labor, electricity, and the brand. If beans double in price, the cup price doesn't double along with them.

Large roasters and chains, meanwhile, often use futures to lock in prices months or even longer in advance. That's exactly the purpose the exchange was invented for. So if futures rise today, your latte may not go up until six months from now. If futures fall today, your latte may not fall at all, because the shop has locked in last year's high price.

The few extra dollars I paid for my latte that morning might have been the echo of last February's "panic buying."

Ending: Buying Insurance on Prices

So you see, coffee futures were invented more than 140 years ago by a group of merchants ruined by speculation, to guard against speculation. Later, Brazil tried to control prices through hoarding and burning coffee, and dozens of countries tried to join forces. Each attempt failed. In the end, prices returned to that screen, where they are set by weather, frost, inventories, and countless people betting on tomorrow.

What people can do is never control coffee prices. What they can do is buy a little insurance on them.

The next time you pick up a cup of coffee, you might think: this drink is a tree on a Brazilian hillside, plus a gamble on a New York screen.

Alright, that's it for this episode of Cold-Knowledge Blind Box. See you next time!

References

The 1880 corner, Kimball's death, Arnold's bankruptcy, the exchange's incorporation on December 7, 1881 with Arnold as its first president, the "laughingstock" recollection, and the 1904 novel The Corner in Coffee: Uncommon Grounds (Mark Pendergrast), Chapter 6: link
The meeting at 166 Pearl Street and trading starting March 7, 1882: Ukers, All About Coffee, Chapter 30: link
The 1880 price drop from 24 to 16 cents, Kimball's death at 42 after a card game, Arnold's debt of $2.1 million: Corner sur le café de 1880: link
The exchange's mergers (sugar added in 1914, cocoa in 1979, NYBOT in 1998, ICE in 2007): Wikipedia, Coffee, Sugar and Cocoa Exchange: link
Coffee C contract specifications (37,500 lb, US cents per pound, physical delivery, origin premiums and discounts): ICE: link 1; link 2
Thirteen consecutive record days in February 2025, "panic buying": Reuters: link
The record of $4.3795 a pound in October 2025: Bloomberg: link
The Taubaté Agreement (February 1906): Wikipedia: link
June 1931 to August 1941, 72,583,000 bags destroyed: Duke Law, Law and Contemporary Problems, "The Inter-American Coffee Agreement": link
About 78.2 million bags through 1944, roughly three years of world exports: Journal of Economic History, "Coffee cartel 1930–1940": link
The 1962 International Coffee Agreement, the suspension of quotas on July 4, 1989, and why the talks collapsed: Wikipedia; LA Times, July 5, 1989: link 1; link 2
ICO indicator prices of 115.9, 73.3 and 50.0 US cents: Gilbert, The Long Run Impact of the Ending of Coffee Control: link
The agreement's price increase of about 73 percent and the Cold War backdrop: Igami (Yale): link
The 1975 black frost: 1.5 billion trees, 22 million to 6 million bags, $70 to $320 a bag: New York Times, January 12, 1977: link
The frost-rumor "ritual" and snow in Paraná: TIME, "Coffee Nerves": link
September 2026: ICO indicator price down 8.7 percent, record Brazilian crop, certified stocks of 217,600 bags (lowest since 1999): ICO monthly report; Comunicaffe: link 1; link 2
October 2026: arabica back above $3, Brazil's September exports at a 20-month high, and the difference between exports and certified stocks: Cocoa Intel; World Coffee Index: link 1; link 2

Image Sources

Cafe porto Santos 1880.jpg: Marc Ferrez, public domain
Hanover Square, Coffee Exchange, and 2nd & 3rd Ave. El (NYPL).jpg): Berenice Abbott, 1936, public domain
Johann Jacob Steinmann - Plantação de café.jpg: public domain
Roasted arabica coffee beans Minca.jpg: Roger Burger, CC0

Airlines Don't Make Money on Tickets. So How Do They Survive?

2026-09-02T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box.

A while ago, I flew to Singapore.

It was a flight of a few hours, and the ticket wasn't cheap. After landing, I looked at that ticket and suddenly wondered: how much money did Singapore Airlines actually make on this trip?

The answer is probably: none.

In the quarter ending June this year, the Singapore Airlines Group reported record revenue of S$5.7 billion. More people flew, more tickets were sold, and yet the group ended up with a net loss of S$76 million.

One reason was that fuel costs rose sharply within a single quarter. However many tickets you sell, if fuel, labor and airport charges all climb together, the profit can vanish in an instant.

This is what makes the airline business a bit awkward: the ticket is its most visible product, but it isn't necessarily its most reliable source of money.

So what do airlines actually live on?

I looked into it, and found that three things are really valuable.

The first is slots.

Here, a "slot" isn't a time on the clock. It's a permit: whether you have the right to land at a certain airport at a certain time, and then take off from there.

Busy airports have limited runways and gates, and the most valuable morning and evening times are a scramble for a limited number of spots. London Heathrow is the most typical example. Its capacity has been close to saturation for a long time. If a newcomer wants to add a flight in a prime time slot, it usually can't just buy another airplane. It has to get a slot first.

What's more, slots come with "grandfather rights." Put simply, as long as an airline keeps using a slot and meets the required usage rate, it usually keeps the slot for the next season. Those who arrived first not only got the spot, they can keep renewing it.

So a pair of prime-time slots can be astonishingly expensive.

In 2016, Oman Air reportedly paid US$75 million for a pair of takeoff and landing slots at Heathrow so it could add an early morning flight there. Earlier, American Airlines had bought a pair from SAS for as much as US$60 million.

What they bought wasn't a slab of concrete on the runway. It was the right to enter the most valuable airline window in the world.

Because the same airplane is worth completely different amounts depending on whether it flies in the morning or at midnight. Whether business travelers are willing to sit on it, whether connections can be made, whether the plane can fly one more round a day, all depend on the slot.

So the first truly scarce asset an airline has is not the aircraft. It's position. Airplanes can be bought, but prime slots are not always for sale.

But position alone isn't enough.

The second asset an airline has is its network.

Aviation has a peculiar pattern: the more destinations you have, the faster the number of possible itineraries grows.

Three airports can form up to six directional connections. Ten airports can form ninety. Every new city adds more than one route. It can connect with the other cities already in the network.

This is the value of a hub airport.

Airlines first bring passengers from various places to the same hub, then send them on to different destinations from there. Two routes that couldn't fill a plane on their own can be joined together through the hub and fill an entire aircraft.

By common sense, neither Singapore Airlines nor Emirates has an obvious advantage.

Singapore is a city-state with almost no domestic market. Since Singapore Airlines began operating independently in 1972, it has faced almost entirely international routes. Dubai's local population is also limited, and it has no domestic hinterland big enough to support a vast route network.

But they came up with the same solution: since local customers aren't enough, bring the travelers passing through the whole world to their own doorstep.

In the industry, this model is called the "sixth freedom of the air."

The name sounds complicated, but the principle is simple. Say you want to fly from London to Sydney. You don't have to take a direct flight by British Airways or Qantas. You can take Emirates to Dubai, then transfer there to Sydney.

Emirates effectively packages the "London to UAE" and "UAE to Australia" legs into one "London to Sydney" product.

At Changi Airport, Singapore Airlines does the same thing. It doesn't sell "where Singaporeans want to go." It sells "where people all over the world can go by way of Singapore."

About seven in ten Emirates passengers are only transferring in Dubai. Based on rough estimates from booking data, that could mean more than 30 million transfer passengers a year, the equivalent of about 100,000 people a day passing through Dubai.

It doesn't have a hinterland, but it borrows everyone else's hinterland and weaves it into its own network.

What's more interesting is that this network started out very small.

In 1985, Dubai decided to set up its own airline. The startup capital was only US$10 million, preparation took five months, and the first two airplanes it used were leased from Pakistan International Airlines.

On October 25 of that same year, Emirates' first flight took off from Dubai for Karachi.

Forty years later, its routes cover more than 150 cities, and its profit in the most recent fiscal year was about US$5.4 billion, a record high. It didn't join Star Alliance, oneworld or SkyTeam. Instead, it started from Dubai and wove a global network on its own.

Singapore Airlines took a different path.

It turned Changi Airport into a transfer factory. Singapore Airlines serves the premium market, Scoot serves the low-cost market, and through Star Alliance it brings passengers from other airlines into its own network.

Having no domestic market was a weakness. But because there was no way back, transfers became the main business.

Having reached this point, I thought the business logic of airlines was clear: secure scarce slots, then build a big enough network around a hub.

But there's a third thing, one that seems even less like the airline business than the first two.

That's miles.

We usually think of miles as a perk airlines give passengers: you fly, you earn points, and once you've collected enough, you can trade them for a ticket or an upgrade.

But the real big business of loyalty programs isn't giving points to passengers. It's selling points to banks.

Banks buy airline miles and package them as credit card rewards. Every time you swipe a co-branded card, the bank pays the airline some money, and the airline credits the corresponding miles to your account.

For airlines, this business has two advantages.

First, passengers may redeem their miles half a year or even several years later, but the bank's money has already arrived.

Second, some miles will never be used at all. Even when passengers do redeem them, the airline often provides only seats that might have gone empty on the flight anyway, so the actual cost isn't necessarily equal to the ticket price.

Take Delta's SkyMiles as an example. In 2019, it earned about US$6.1 billion in cash from selling miles. More than three-quarters of that came from third-party partners, and the biggest buyer was American Express, which contributed about US$4 billion that year.

United Airlines even once pledged its MileagePlus frequent flyer program as collateral and raised US$5 billion.

At that point, miles are no longer just points. They are a financial asset that can be sold, financed, and turned into cash ahead of time.

Singapore Airlines' KrisFlyer works the same way.

The program started in 1999. By March 2025, its membership had passed 10 million, and it had more than 1,700 partner brands where members could earn or spend miles on the ground. The most important category among them is banks and credit card issuers.

You might think you're just swiping a card to earn a free flight someday. In fact, every time you swipe, the bank may already have paid the airline some money on your behalf.

So the cheaper the ticket, the more the airline looks like a transport company. The stronger the loyalty program, the more it looks like a financial company.

Back to the original question: if tickets don't make money, how do airlines survive?

The answer can be condensed into three sentences.

Hold golden slots, so you have the right to appear in the most valuable places.

Build a network around a hub, so the travelers passing through the world become your passengers.

Sell miles to banks, so the cash comes back before the passenger ever boards the plane.

Of course, airlines can't do without ticket revenue. It's just that the ticket isn't the whole story. It's more like an admission pass: it brings the passenger into the network, gets them transferring, earning miles, using credit cards, and keeps the whole system running.

Later, I looked at that ticket to Singapore again.

What I paid for was, of course, a stretch of a journey. But what supports Singapore Airlines' long-term competitiveness isn't just how much money one flight made. It's whether it can hold its slots, expand its network, and tie together its 10 million members and more than 1,700 partner brands.

Singapore Airlines and Emirates have proven the same thing:

What's truly scarce in the aviation industry has never been just the seats on the airplane.

It's whether you can hold your position, weave a network, and turn a single flight into a business that happens over and over again.

It doesn't matter if few people live at your doorstep.

As long as enough people pass by.

Well, that's where this episode of Cold-Knowledge Blind Box ends. See you next time!

Using Agents to Make a Show, Not a Demo

2026-08-30T00:00:00Z

The Hard Part Is the Handoff

On DeepSeek Harness, the common use of agent teams is writing code. I'm using it to run a content factory: downloading, transcribing, revising scripts, and laying out pages, with the work divided among different agents. The difficulty isn't whether the model can write Chinese. The difficulty is the handoff. Where does the transcript come from? Where does the style card live? Does a draft count as published? Where does the audio go? If none of this is written down, a multi-agent setup just copies one person's verbal chaos into several people's verbal chaos.

Capabilities Are a Means

On dsh.fish, people are sharing plugins. Plugins can add many capabilities: search, image viewing, sidebars, cost tracking. My own bottleneck isn't a missing plugin. It's that after installing a lot of them, I still haven't reliably produced one episode that I can take away and deliver. Capabilities are a means. The show is the deliverable.

A Repeatable Output

So my requirements for agents are now very narrow. Don't help me build the factory into something that looks more like a factory. Help me get the shortest path working, and make sure it still works next time. Demos look good. A repeatable output is what counts as a work record.

Only One Entrance

2026-08-30T00:00:00Z

One Root Folder Was Already Too Many

I used to lay out materials, transcripts, other people's reference episodes, my own drafts, and tool scripts all in a single root directory. Two weeks later I couldn't tell which file was meant to air and which was just fuel. The more folders there are, the more every decision turns into "what is this folder for?" At that point I was managing tools, not making content.

Two Rules

Later I kept only two rules. New things only go into "Materials." Things I want to take out only come from my own show's directory. The transcription factory, the Gangqun character profiles, and the voice-cloning setup all run in the background. Not because they don't matter, but because I shouldn't have to open them every day.

Watch Only the Entrance and the Exit

The mistake people starting a channel most easily make is treating the factory tour as the product. Listeners don't need to know who handles transcription or who edits the drafts. They only hear one episode. Internally it should work the same way: as the boss, watch only the entrance and the exit. If the entrance is messy, even the smartest digital employees behind it are just sorting garbage. If the exit isn't clear, finishing the work is no better than not finishing it.

Don't Leave Things on the Desktop

This isn't an architectural principle. It's just admitting that I can't keep an eye on everything. Whatever choices you can cut down, don't leave on the desktop.

Now That Gangqun Is Running

2026-08-30T00:00:00Z

Rehearsal

I wrote full character profiles for the digital staff: who takes the job, who transcribes, who drafts the outline, who writes the finished text, and who prepares the paste-ready copy. The roles were complete, and the handoff diagram looked good. Only once it actually ran did two things become clear. Transcription has two sets of tools, and publishing has no API, so the pipeline can only produce a copy you paste in by hand. The finished audio follows a separate path. With those two lines unconnected, Gangqun stalls at "looking like it's working."

A Character Profile Is Not a Product

Digital staff are very easy to turn into role-play. The more complete the persona, the more it looks already delivered. A character profile is not a product. If you can't say clearly where the files go, what counts as done, and where the next step picks up, the pipeline is just a rehearsal.

The Shortest Path

My standard now is pretty plain. Can a piece go from raw material to a manuscript you can take away, or to audio that's ready to air? If it can't get there, then however busy Gangqun gets, it's still a rehearsal. So first open the shortest path: a link comes in, a manuscript goes out. Other capabilities can come later. Pile on too many abilities and the entrance gets muddy again. It's the same disease as the directory problem.

Style Is a Person, Not a Genre

2026-08-30T00:00:00Z

Remember Style by Person

Someone once asked me to sort drafts into narrative, practical and commentary. That didn't work well. Later I switched to remembering by blogger: one person uses business battles to narrate history, another keeps asking "and then what?" in class, and another dissects organizations using the chapter titles of a military strategy book. When mixing styles, name "whose which move" instead of imitating one person across the whole piece.

Borrowed Skeletons Go Hollow

I once tried putting a story about an airline, which had already been told clearly, into a different set of chapter titles. Yuyan, Faxin, Dingwei, Junzheng: the structure was complete, but the result was poor. The original had already finished explaining "cognitive warfare." Adding another layer of chapter titles is like rewriting the menu for a dish that is already well seasoned. Readers still taste the same dish, they just get tripped up by the menu first.

A Catalog of Moves, Not a Costume Shop

What you can borrow are specific moves: a number, a follow-up question, one strong collision at the opening. What you can't borrow is the whole person's skeleton. That skeleton grew out of their own material. Change the material and the skeleton goes hollow. An empty structure is noisier than having no structure at all.

So for me, a style library is not a costume shop. It's a catalog of moves you can name. Pick too much of one person and the article feels fake. Pick too little and it's just showing off. The middle ground has to be found by testing against a real draft, not by imagining it from a classification chart.

How Much Can You Make Delivering Food in Australia in a Day?

2026-08-18T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. Today we're talking about a story of delivering food in Australia.

"Worst case, I'll just go deliver food." You've definitely heard that one before.

We always assume delivery work has a low barrier to entry, is easy to pick up, and still pays well, so it's everyone's fallback plan. The protagonist of this episode, Hengrui in Australia, didn't buy it. Is there any job in this world that you can do well without any research and that still pays a high income?

He searched online and noticed a strange pattern: the people who had actually done delivery work never wrote about how it really felt, and the people who did write about it had never actually done delivery work.

So he made a decision: worst case, I'll go deliver food for one day.

Before setting off, Hengrui puffed out his chest and promised all his coworkers: "I'll use what I earn delivering food to buy every one of you a bubble tea."

Nobody could have guessed that after this one day, he would realize the industry was nothing like what he had imagined.

II. The Mentor

To keep his first delivery from going badly, a colleague at the company who had spent three years as an executive at a food delivery platform found Hengrui an experienced rider as a mentor.

The mentor arrived right on time at nine in the morning. The first thing Hengrui noticed was his dark, sun-tanned skin and his few words. He looked a bit reserved. But after the mentor said just one thing, Hengrui stopped underestimating him.

Before he went abroad, this mentor had racked up debts worth six figures back home. Over the past two years, delivering food in Australia one order at a time, he had paid every last cent back.

In delivery circles, many riders call him a "master," because he often pulls in more than US$10,000 in a single month.

Hengrui's heart skipped a beat. This ordinary-looking delivery guy had a story he could never have imagined.

But what surprised him even more was that the first thing the mentor did was not teach him how to accept orders. Instead, he started inspecting Hengrui's gear.

III. Lesson One: Your Gear Is Survival

The mentor first checked whether the electric scooter was fully charged, whether the helmet was buckled, whether the headlight worked, and whether the insulated box could be secured. He went through the power bank, the Bluetooth earphones and the water. He even checked whether the navigation app on Hengrui's phone had voice guidance turned on.

Hengrui had prepared well for those. But when the mentor asked, "Where are your gloves, your phone mount, and the big lock for your bike?", he was completely lost.

He hadn't thought of the gloves or the phone mount. But why a big lock? The bike is right under your butt. Who's going to steal it?

The mentor looked at him as if he were an idiot, then said something Hengrui would remember all day:

"You have to remember every single thing I'm telling you right now. Every item I'm checking with you is a hardship I went through in the past."

The phone mount is a must. When you're delivering, you have to watch the road and the traffic lights, keep the best route in your head, and many reroutes come with voice prompts from your phone. You can't ride while staring down at a screen.

Gloves: when you drive a car, you barely notice the weather. But when you're riding an electric scooter through the streets of Melbourne with cold wind hitting both hands, you'll be shivering. And when you instinctively want to shove your hands into your pockets, the chance of falling goes up.

As for why you need a lock, the mentor simply said, "You'll find out soon enough."

That was the first lesson in delivery work, and it was entirely about gear. Over the rest of that day, Hengrui found that every item and every rule the mentor had mentioned played out like a prophecy, each one teaching him a harsh lesson.

When the gear wasn't complete, the mentor went home to fetch the rest. Before leaving, he told Hengrui: "Don't sit around while I'm gone. Start trying to take orders."

As soon as the mentor was gone, Hengrui eagerly opened the delivery app and waited for his first order to show up.

IV. Standing Under the Office Tower, Not One Order

He stood by the curb holding his phone. One minute passed. Nothing happened.

He kept picking up his phone, putting it down, then picking it up again, afraid of missing an app notification. Five more minutes went by, still no order. He began to wonder. He was standing downstairs from his office, in a cluster of office towers, and he could see a few riders riding past. One of them even walked straight into his building.

A thought came to him: it's not that there are no orders, it's that the orders don't want to come to him.

When ten more minutes passed without a single order, Hengrui started to panic. He suspected that because his was a new account, the platform didn't trust him enough to assign orders, so it was deliberately holding them back. He opened the app again and again to check. Every time, his status showed he was accepting orders. So why wasn't a single order being assigned to him?

Just as he was starting to lose faith in everything, the mentor came back with the gear.

When he heard that Hengrui had been waiting for orders downstairs from his own office, the mentor was first stunned, then shook his head. "If you wait here, you might never get an order in your whole life."

Hengrui found this strange. He pointed at the riders who had just passed by. "Clearly there are plenty of riders bringing orders into our building. Why don't I get any?"

The mentor explained the core logic of the food delivery platform:

"The order isn't assigned to the rider closest to the customer. It's assigned to the rider closest to the merchant."

Where you're standing is far away from the delivery heat map. To really boost your order efficiency, you have to get into the center of the heat map, and that area is where the merchants are most concentrated. Platforms never care whether you're close to the customer. What they actually care about is whether you're close to the merchant, because every delivery starts the moment you pick up the food. Whoever is closer to the merchant, the platform sees as more efficient.

Hengrui began to understand: the first mistake he had made was already locked in the moment he opened the app. Even if he waited under that office building until the end of time, he might never get his first order.

This seemingly zero-threshold industry had a whole business logic behind it.

V. Milk Tea Street, Not Chinatown

"Hurry up and come with me," the mentor said.

Hengrui put his phone away, got on his scooter, and followed the mentor toward the real battlefield. Riding behind the mentor, he immediately felt his efficiency go up.

On the way to the city center, though, he had a question: "Why did you take me to the milk tea street instead of the area in Chinatown where the restaurants are most concentrated?"

The mentor smiled and said, "Guess."

Only later did Hengrui realize that "Guess" was the mentor's catchphrase. Having trained so many delivery apprentices, he had carved this coaching-style, question-first approach into his bones. And honestly, the method really did help Hengrui quickly grasp how the delivery business works.

He thought about it for a while, then admitted: "Honestly, I don't know."

The mentor looked helpless and stared at him as if to say, "Come on, figure it out." "Think about it. It's an hour and a half before lunch. The restaurants in Chinatown haven't even fired up their kitchens yet, so orders come out slowly. Even if someone places an order right now, you'd have to wait a long time for it."

"But this street is different. It's mostly coffee shops and milk tea shops, plus lots of offices. At this time of day, many office workers have just finished their second or third morning meeting and need a milk tea or a coffee to perk up. So this is peak time for coffee and milk tea."

"It's only when lunchtime comes that you'll see the heat map really shift to the areas around the restaurants."

Hengrui was deeply impressed. Isn't this what everyone in business always says: wherever there's demand, there's a market? The delivery industry had studied market demand to the extreme. The algorithm forces you to understand what kind of product the market needs at different points in the day.

Just as the words left his mouth, ding-dong: he finally received his first order.

Just as the mentor had said, the first order was a coffee. Hengrui almost held his breath as he tapped the screen, afraid his finger would slip and he'd lose the order.

VI. The Science of a Cup of Coffee

While coaching him through taking orders, the mentor reminded him: be careful with the famous coffee shops. Their baristas are short-staffed. They have to handle the customers inside, the delivery counter, and the platform orders all at once. At places like that, a single coffee can take twenty minutes or even half an hour. If the shop is famous, steer clear of it.

After picking up the coffee, the mentor reminded him to check it carefully. Many shops don't put the lids on properly, and some coffee shops with no delivery experience don't provide cup holders. Food can shake a bit without much harm, but if the coffee sloshes around a few times on the road, by the time it reaches the customer it may be only half full.

Hengrui worried: "What if I spill it?"

The mentor calmly replied: "So what if you spill it? Everyone's had that experience. But if you spill it, you might get a complaint, and once you get a bad review, it hurts your rating. If your rating drops, your order efficiency and your future income can both suffer."

VII. Threading Between Steel Beasts

To earn his first delivery fee, Hengrui didn't hesitate and followed the navigation straight to the destination.

As he zipped between buildings on his scooter, a strong feeling rose up in him: delivery looks dangerous from the outside, but in reality it's not safe at all.

What terrified him most was riding through the passages in central Melbourne. He had to stay on the line between the parking lane and the car lane. On one side, you worry that a car in the parking lane will suddenly fling its door open, so you want to stay as far from them as possible. But when you get too far from them, you drift too deep into the car lane.

On one side are cars that could knock you flying if a door swings open. On the other are small cars speeding past. When you're squeezed in the middle, there's no sense of safety at all.

And to keep up your efficiency, you often have to share the road with buses and trams. When a tram or bus rolls past right next to you, it feels like a steel beast. If it turns even slightly, it can pull you under.

But delivery riders need efficiency. To go faster, you have to keep overtaking in the city center. When you're just starting out and don't know the roads yet, you can't stop to look around. Your eyes have to watch the navigation, the traffic lights, the cars and pedestrians, and you still have to keep glancing at the orders on your phone to see whether anything has changed.

Once you lose focus for even a second, an accident can happen at any time.

According to data cited in the video, in Melbourne alone, there have been more than 917 crashes involving delivery riders over the past seven years. Across Australia, 20 delivery riders have died in recent years. (Note: these figures were cited verbally by the video host and have not been independently verified.)

Even more heartbreaking, because these riders are not employees of the delivery platforms, many accidents go unnoticed. Their families end up receiving only a pitifully small amount in compensation.

Right after the day's deliveries wrapped up, the mentor sent Hengrui a video. In their riders' group chat, someone had posted footage of a rider being knocked down by a car at a traffic light that day. Hengrui watched it and felt a deep sadness.

VIII. Tickets and Thieves

Traffic isn't the only danger. Delivery riders have plenty of enemies.

Take tickets, for example. The mentor warned him again and again: the scooter feels fast, but the moment you reach a traffic light or a pedestrian crossing, you must get off and push it. If the police catch you riding an electric scooter on the footpath, you'll get an A$400 fine. The mentor said that even someone as careful as him had been fined twice.

Besides the police, thieves are a lifelong enemy for many delivery riders. Australia seems like a high-welfare country, so you'd expect very little theft. In reality, though, the thieves who specialize in targeting delivery riders are hard to guard against.

Almost every delivery rider has had an electric scooter stolen. Why? Because many riders take a gamble when they pick up or drop off food. They think, "I'll only be inside for a few dozen seconds, there's no way it'll happen to me." But they don't know there's a thief outside, watching riders exactly like them. The moment you walk in, he rides off with your bike.

Some riders even find their scooter still parked where they left it, only to discover the battery has been pried out, because an electric scooter battery can fetch several hundred Australian dollars. On his delivery route, Hengrui even came across a stripped, half-destroyed bike that had just been stolen.

The most ironic part is that these riders are both victims of the thieves and customers of the thieves. Electric scooters are essential to every delivery rider, so thieves have found that every new scooter they steal and post online can be sold at a high price within a day or two.

IX. First Earnings: A$4.5

After twenty minutes of bumbling along, Hengrui finally earned his first delivery income: A$4.5.

He was overjoyed, because the money had been so hard to earn.

But after delivering that order, he quietly did the math in his head. He had worked for more than an hour and earned only A$4.5. At this rate, he would never earn over US$10,000 a month in his whole life.

So he couldn't help asking the mentor again: "What exactly do I have to do to earn over US$10,000 a month from deliveries, like you?"

The mentor smiled mysteriously and told him: to become a high-income delivery rider, you have to learn to do "stacked orders."

X. Stacked Orders: The MBA of Food Delivery

What are stacked orders? If you've ever ordered food, you've probably had this odd experience: the app says your rider has already picked up your food, but he just won't come, and he dawdles around over there. You might even suspect he's deliberately dragging out your wait.

Hengrui had wondered about this too. Then he saw with his own eyes several riders with two or three orders lined up by their feet, and he finally understood: they weren't dawdling. They were improving efficiency by taking several orders and delivering several orders at once. That's what stacked orders are.

True experts even keep accepting new orders while they're on the road, using each new order to plan their next route.

Soon enough, Hengrui got his own first stacked order. He had just picked up the xiaolongbao a customer had ordered from a restaurant and was about to start the scooter when, ding, a new order popped up on his phone. His heart skipped: What's this? Is it urging me to hurry up and finish this one so I can go grab that one?

The mentor immediately warned him: "Don't even think about it. Accept it right away."

The mentor explained that when a stacked order like this appears, it's because the system already knows where you're headed next. It found an order whose route roughly overlaps with yours, and it pushed that order to you first.

Half-believing, Hengrui tapped the app to accept it. Sure enough, the next pickup point was right on the way to his drop-off. So instead of going straight to deliver the food he was already carrying, he stopped at the next pickup point and grabbed the second order.

With the second meal in hand, the mentor gave him another lesson. He glanced at the delivery address and immediately said, "No need to ride. Just walk over and deliver it."

Hengrui was puzzled. But when he looked at the address, he froze: the delivery address was less than 200 meters away, across the street.

He thought to himself: if it's this close, why even order delivery? Would it kill you to walk downstairs?

When they got to the building, the person who came out to collect the food was a young man. One look and you could tell he was an international student. Hengrui sighed: today's international students really aren't like they used to be. They're absurdly rich. Of course, he was also curious: if the parents knew how their child was spending money overseas, how would they feel?

But the mentor leaned in and said quietly: "International students are really important for the delivery business. Many times, once they go home for the holidays, order volume drops off a cliff."

How Did the US Railroad Bubble Drag Down the Whole Economy?

2026-08-03T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box.

How are your stocks doing lately? Today we're talking about another bubble in history that burst: the US railroad bubble of 1873.

If you only look at the ending, you might think it was a bunch of crooks using a new technology to fleece people. But the problem is, railroads are not air. They really did connect America. They really made goods travel faster, cities grow, and the whole country change.

So if the technology was real and the demand was real, why did it still turn into a bubble?

This question may be more worth discussing than "why do bubbles burst?"

May 10, 1869. Promontory Summit, Utah Territory.

Two railroad construction crews finally met in the wilderness. The Union Pacific Railroad from the east and the Central Pacific Railroad from the west joined here. On site was a golden spike, with telegraph lines connected across the country. When the last hammer blow fell, the news reached major cities across the nation almost at once.

All of America was ecstatic.

In the past, traveling from the East Coast to the West Coast meant riding in a stagecoach, on horseback, sailing around South America, or risking your life crossing the wilderness, which could take months. Now a railroad spanning the continent compressed the journey to about a week.

Friends, this was not a PowerPoint slide or a concept video. It was a technological revolution you could see with your own eyes.

Railroads brought more than just speed. They could carry wheat from the Midwest to New York, bring machines from the East into the interior, and link mines, ranches, ports and factories into a single network. In the past, whether a place had business depended on how close it was to a river. From now on, whether a place could thrive depended on whether the trains stopped there.

So it was not foolish for people at the time to believe in railroads.

The real trouble was that they soon quietly swapped one true statement, "railroads will change America," for another: "anything that is a railroad will definitely make money."

These two sentences seem to differ by only a few words, but between them lies a graveyard.

To build a railroad across the continent, the biggest problem was not whether you had a dream, but who would pay for it.

Railroads were extremely expensive. You had to buy steel rails, wooden ties and locomotives, blast through mountains, build bridges, and hire thousands of workers. Even worse, before a railroad was finished, it earned almost nothing. And once it was finished, nobody knew whether there would be enough people and freight along the line.

Private capital did not dare bet on it alone, so the US government stepped in.

In 1862, Lincoln signed the Pacific Railway Act. The government supported the projects with bonds, and at the same time handed over large amounts of public land along the railroad routes to the companies. For some projects, the incentive could be understood this way: for every mile you laid forward, you got not only financing support but also large tracts of land on both sides of the line.

This design had its logic. Wild land was worth little at the time. Once the railroad was running, the land could be sold, so the government didn't need to hand over all the cash right away, yet it could still entice private companies to complete a national-scale project.

But it also created a very dangerous temptation.

How was a railroad company supposed to make money in the first place? Obviously by carrying people and goods from point A to point B, and collecting fares and freight charges.

But now, a company that hadn't yet hauled many cars of cargo could already get land, bonds and construction contracts simply by building track. So the question managers cared about most slowly shifted from "does this line have passengers?" to "can this line lay another fifty miles?"

The railroad turned from a transportation business into a race for mileage.

And the more elaborate maneuvers came later.

A group of insiders at the Union Pacific Railroad Company controlled a construction contractor called the Credit Mobilier. The name is long, so all you need to remember is that it was like a shadow double of the railroad company.

The left hand was the railroad company, responsible for raising money from the government and investors. The right hand was the construction company, responsible for quoting the price of building the track. Very often, the owners of both hands were the same people.

The result was quite imaginative.

The right hand inflated the construction costs, and the left hand accepted every bill. The railroad company carried a growing burden of costs on its books, while the shareholders of the construction company could pocket their profits in advance. Whether the trains would make money in the future was a future problem; whether the contractor could make money today was the real problem.

Later, a congressional investigation found that the company not only charged the railroad and the government excessive fees, but also gave profitable shares to some members of Congress, in an attempt to keep Washington from asking too many questions.

You see, before the railroad had even carried the nation's people to their destinations, part of the nation's wealth had already been delivered to a few individuals.

III

Still, government subsidies and insider deals alone could not inflate a nationwide bubble. What truly spread the railroad frenzy was bonds.

People building railroads needed money today, and promised to repay it with decades of future freight revenue. Investors who bought railroad bonds were effectively betting on one thing: wherever the tracks were laid, people, farms, mines and cities would follow. As long as America kept expanding westward, a line crossing the wilderness today would become a golden corridor tomorrow.

This story was very seductive, because it explained "there is no demand yet" as "demand is on its way."

The man who sold this story best was Jay Cooke. We'll call him "Old Cooke" from here on.

Old Cooke was not an ordinary banker. During the American Civil War, he helped the federal government sell war bonds to the public. His reputation, distribution network and credibility were all top-tier. Put simply, other people sold bonds on interest, while Old Cooke sold bonds on faith.

After the war ended, he set his sights on an even grander project: the Northern Pacific Railroad.

The line was planned to run from the Great Lakes region, across the northern prairies and the Rocky Mountains, all the way to the Pacific coast. On a map it looked like a zipper pulled open across the land, stitching together the whole northern part of the country.

The problem was that a line on a map is thin, but a railroad in reality is very expensive.

The route would pass through large areas with very few people. There were no big cities to provide passengers, and not enough freight revenue to pay off the debt right away. It had to burn money to lay track first, then wait for migrants, farms and commerce to slowly appear.

In other words, the Northern Pacific was not selling a railroad that was already making money. It was selling a world that would be created because of the railroad.

In 1870, construction officially began. Old Cooke's bank underwrote the bonds, then sold them through a vast sales network to investors in America and Europe. Every time the railroad extended forward, another batch of investors had to keep buying.

As long as the bonds sold well, the construction could continue. As long as construction continued, everyone believed even more that the bonds would keep selling.

It was a perfect cycle.

Until it stopped being a cycle.

In 1873, the European financial markets ran into trouble first.

In May, the Vienna stock market crashed. By September, European investors had begun selling American railroad securities and pulling their money back. The funds that had been flowing steadily into American railroads suddenly began to ebb.

By then the Northern Pacific had been built as far as the area of today's North Dakota, but its costs kept running over budget, and the income along the line was far from enough. What it lacked most was not track, but the next person willing to take over its bonds.

On September 18, 1873, Old Cooke's bank announced it had suspended payments.

For Americans at the time, the shock was roughly like waking up in the morning to find that the nation's most reputable financial star, the man who could sell government bonds better than anyone, had announced in the afternoon: "Sorry, we can't get the money together."

Panic spread at once.

Investors rushed to banks and brokerages, and banks scrambled to call in their loans from railroad companies. The railroad companies had been relying on new money to finish old projects. Once financing stopped, half-laid rails stopped, workers went unpaid, and equipment suppliers could not collect their payments. What collapsed was not a single company, but a whole chain of companies that owed each other money.

On September 20, the New York Stock Exchange was forced to suspend trading. It was the first time in its history that it had closed its doors because of a financial panic.

Many railroad projects halted, banks and brokerage firms went bankrupt, and workers lost their jobs. The crisis was later known as the "Panic of 1873."

So when the American railroad bubble truly burst, it was not because one day everyone suddenly discovered that trains were useless.

Quite the opposite: trains were very useful.

What collapsed was something else: the unified vision of the future in which every line was worth building, every debt could be repaid, and every stretch of wilderness would become a city on schedule.

After the railroad bubble, did America stop building railroads?

Of course not.

The Northern Pacific later changed hands, construction continued, and it eventually linked up with the Pacific coast. America's railroad network kept expanding after that, and it truly shaped a national market.

This is what makes the story so interesting.

When a scam collapses, usually nothing is left behind. But when an infrastructure bubble bursts, the rails are still there, the stations are still there, and the bridges and tunnels are still standing. Later people could even take over assets that earlier investors had built at great cost, for a very low price.

Failed companies may have paved the way for a successful era.

But that is no comfort to the investors of the time. Because "society eventually benefits" and "you make money" have never been the same thing.

A railroad could be enormously important to a country and still leave its earliest shareholders and creditors with nothing, because it cost too much, the routes overlapped, demand arrived too late, and the debts were too heavy.

A technology could change the world beyond doubt and still fail to guarantee that every company flying its flag made a profit.

The biggest misunderstanding of the railroad bubble was that people treated technological progress, corporate profits and asset prices as one and the same thing.

In fact, they are three different things.

Technological progress answers: is this thing useful?

Corporate profit answers: who can use it to keep making money?

Asset prices answer: how much did we pay today for this future?

The first two answers can both be "yes," and the third can still be "it's too expensive."

So today, when we look at artificial intelligence, chips, data centers, or any technology called the "railroad of a new era," the question we really should ask may not be whether it will change the world.

It most likely will.

The harder questions are these: Who is building the tracks? Who is selling the shovels? Who is collecting the tolls? Who can only survive by constantly raising new money? And when everyone believes the future is coming, have we already paid for decades of good news up front in today's price?

The Americans of 1873 did not misjudge the railroads.

They simply imagined a long, winding future that had to be weeded out over time as a ticket that could be cashed in right away.

The railroads were real.

The bubble was real too.

And the most expensive lessons in history are often hidden exactly where both of these statements are true at once.

Well, that's the end of this episode of Cold-Knowledge Blind Box. See you next time!

Love Lasts Forever, But Do Diamonds?

2026-07-05T00:00:00Z

Diamonds Are Forever, De Beers Not Necessarily

Hi everyone, welcome to Cold-Knowledge Blind Box.

Today we're talking about a topic that fits wedding season very well: diamonds.

If you went back five years, when a couple was getting ready to marry and buying rings, they would very likely have run into a soul-searching question.

Should we bite the bullet and get a diamond ring?

If you say no, it feels like you're missing the sense of ceremony.

If you say yes, your wallet quietly sounds the alarm.

But today, this question suddenly doesn't feel so agonizing. Many people now think: a gold ring is good too. It holds its value, you can see it and touch it, and it won't leave you wondering whether marketing got the better of you the moment you bought it.

What's more interesting is that just as gold prices keep climbing and people start to trust gold again, diamond prices are falling steadily in the other direction.

Who is the most embarrassed by this?

It isn't the jewelry stores.

It isn't the wedding companies.

It's the supergiant that once almost defined the modern diamond industry: De Beers.

You may not know the name, but you've certainly heard its most successful line.

A diamond is forever.

Friends, that line is not an ordinary ad slogan.

It works more like a spell.

It turned a piece of transparent stone into proof of love. It turned a mining product into a ticket to marriage. It turned something that could be traded into an emotional keepsake you'd better never try to resell.

De Beers has used this magic for nearly a century.

But now, the magic is starting to fail.

In recent years, De Beers' revenue and profits have both been falling, and it is still holding more than US$2 billion of rough diamond inventory. At some auctions in 2025, buyers even refused to take their goods.

What does that mean?

It means that where people used to line up to grab goods, now you put them on display, and people take one look and say: sorry, I don't want them.

For a company that lives by controlling supply, this isn't an ordinary slowdown.

It's a crack in the faith.

So how did De Beers end up here?

The story starts more than a century ago, in South Africa.

In the second half of the nineteenth century, diamond mines were discovered in the Kimberley region of South Africa.

At first, everyone was of course excited.

Diamonds sound like wealth, rarity, a lottery ticket of destiny that suddenly pops up from deep underground.

But the problem soon came.

There were too many mines.

Capital poured in, miners poured in, and machines poured in too. Diamond mining turned from small-scale digging into industrial production. Output surged in a short time, and prices began to plunge.

Pay attention here, because there is something very counterintuitive.

The real enemy of diamonds has never been poverty.

The real enemy of diamonds is abundance.

As soon as people realize this thing isn't as scarce as they imagined, its sense of sacredness drops away at once.

It was at this moment that a British businessman appeared.

His name was Cecil Rhodes.

His name is a bit long, so we'll just call him "Old Rhodes" from here on.

Old Rhodes understood one thing: if you want to make big money from diamonds, you can't do it by digging harder.

Because if everyone digs more, prices will collapse.

The real way to make money is to get hold of all the taps.

So when other small mine owners were being beaten down by low prices to the brink of bankruptcy, Old Rhodes didn't panic with the market. He began buying up these mines at low prices.

One today.

Another one tomorrow.

Finally, in 1888, he pulled his assets together and founded the De Beers Consolidated Mines company.

From the day it was born, De Beers was never an ordinary mining company.

It was more like the central bank of the diamond world.

An ordinary company asks: how do I sell my product?

De Beers asks: how do I decide how many diamonds the whole world gets to see?

To do this, it built a very domineering central selling system.

Simply put, the rough diamonds produced by mines around the world could not be sold to whoever you liked, nor at whatever price you liked. They had to enter De Beers' system, where they were graded, priced and allocated centrally.

The core logic of this system is very simple.

Diamond prices cannot be left to the free market.

Because the free market is too honest.

Once supply grows, it lowers prices.

Once consumers stop buying, it lowers prices too.

De Beers' goal was to separate diamonds from the free market.

It was like building a dam between the mines and the consumers.

Upstream, it knew how much water there was.

Downstream, it decided how much water to release.

If a mine owner refused to cooperate and tried to bypass the system to sell cheaply, De Beers could use its own stockpile to launch a price war and bring you to heel.

That wasn't all.

De Beers also invented a system of sightholders.

What is a sightholder?

You can think of it as a diamond wholesale customer certified by De Beers.

But this certification was not meant to make you a god.

Quite the opposite. It was meant to make you obedient.

At fixed times and fixed places, De Beers put a packet of diamonds in front of you and told you: these goods cost this much, and this is the quantity.

You could look.

But you basically couldn't bargain.

You also couldn't say, "I only want the good ones, not the bad ones."

Even less could you refuse them when the market was weak.

If you didn't cooperate, the next time you might not be on the list. And getting high-quality diamond supply would become very hard.

Friends, this isn't an ordinary business.

It's more like a club.

And De Beers was the person at the door holding the guest list.

If you want to get in, you have to follow the rules.

If you don't follow the rules, don't play.

Through this system, De Beers at one point controlled more than 80% of the world's diamond distribution.

What does that mean?

It doesn't simply sell diamonds.

It defines diamonds.

It defines which diamonds are valuable.

It defines when goods are released.

It defines who is qualified to buy.

It even defines how consumers should understand diamonds.

But controlling supply alone was not enough.

Because diamonds have a built-in problem.

They are not like food, which you starve without eating today.

They are not like a house, which people have to live in.

Nor are they like gold, which for thousands of years people have accepted as a symbol of wealth.

For diamonds to sell at high prices, they must answer one question: why do you absolutely have to buy one?

De Beers' answer was: because of love.

In 1947, that famous slogan appeared.

A diamond is forever.

World Cup: How Does a Small Ball Move the Big Earth?

2026-06-18T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. Have you been watching the World Cup? It has only been going for a couple of days, and already there has been a very special match: Iran against New Zealand.

On the scoreboard, it was just a group-stage game that ended 2-2. But once you take in the background outside the stadium, it's anything but ordinary. One of the host countries this time is the United States, and the Iranian team was playing on American soil.

In an ordinary year, that might just be a matter of visas, security and diplomatic protocol. But today, it has become very sensitive. The United States and Iran have been tangled up in sanctions, negotiations and military pressure for decades. This year, relations between the two have been stretched to the limit. Yet against this backdrop, Iranian players still got their visas, walked into a stadium in Los Angeles, and finished a match in front of cameras around the world.

That is the strangest thing about football.

It's only a game. Twenty-two people chase one ball. When it goes in, people cheer; when it doesn't, people sigh. But sometimes it is given a very serious role: when politics can't reach agreement, when diplomacy is stuck, when war has blocked every channel, football can still bring hostile sides, for at least ninety minutes, under the same set of rules.

Today's episode, using this World Cup news as a starting point, will briefly talk about: Where did football come from? Why is it always tangled up with war, conflict and peace? How was the modern World Cup born? And why did this sport, which claims to belong to all of humanity, end up becoming a huge business?

First, the origins of football.

If you trace history, you'll find an interesting phenomenon: in many places around the world, in eras when there was no contact between them, people invented similar "ball games."

The ancient Greeks had a game called Episkyros, which we can simply call "Old E." A group of young men split into two teams and fought over a ball sewn from leather on the sand. Both hands and feet were allowed. The ancient Romans had a similar game called Harpastum, which we can call "Old H." This one was more like military training: soldiers pushed, collided and grabbed in the mud, training their physical fitness and their combat skills.

In Han dynasty China there was cuju, which was popular in both the imperial court and the army. Indigenous peoples of North America had the ancestors of lacrosse. And the Maya of Mesoamerica went even further. They had a ball game in which hands and feet could not be used at all. Players had to strike the ball toward stone rings using their hips and elbows.

You see, these civilizations had no WeChat, no flights, no World Cup broadcasts back then. Yet they all, without any coordination, did one same thing: they set a round object on the ground, or in the middle of a field, and then used their bodies to fight over it.

What does that tell us? It suggests that ball games were probably not invented suddenly by one genius nation, but are a very basic human instinct. We like to chase, we like to compete, and we like to squeeze chaotic force into a set of rules. The reason football later swept the world is exactly this: it is simple enough and primal enough. You don't need complicated equipment. An empty field, a ball and a few people, and you can start.

But precisely because it is so primal, its relationship with war has always been very subtle.

The most famous story happened at Christmas in 1914.

It was not long after World War I had begun. On the Western Front in Europe, British and German soldiers lay in their own trenches, with a no man's land between them. That ground was full of barbed wire, shell craters, and corpses that had died and had not yet been collected.

On the evening of December 24, small candle lights suddenly appeared in the German trenches. Someone began singing "Silent Night." The British soldiers were stunned at first, and then they joined in, singing the English version. The next morning, several German soldiers came out of their trenches without weapons, hands raised, calling out in broken English: "Merry Christmas, we have no weapons."

Then something incredible happened. More and more British and German soldiers climbed out of their trenches and walked into no man's land. They shook hands, traded cigarettes, chocolate and buttons, and showed each other photos of their families. Some of them buried their dead comrades together. Later, someone brought out a football, and the two sides played an impromptu match between the shell craters.

It's hard to say how formal that match was, and accounts of the score differ. But the event itself is shocking enough: people who had been shooting at each other only days earlier, because of Christmas and because of a ball, briefly saw each other as human beings again.

Of course, a war cannot stop because of one football match. Higher commands soon ordered that private contact between enemies be strictly forbidden. In the following Christmases, there was never again such a large-scale ceasefire on the front lines. But the football that rolled across no man's land in 1914 remained in human memory. It proved one thing: even inside the most brutal war machine, humanity has not completely disappeared.

The second story happened in Kyiv in 1942.

At the time, Nazi Germany occupied Kyiv. Many Ukrainian players had either died in battle or become prisoners of war, and some of them, to survive, were doing hard labor in a local bakery. Later, in order to dress up the occupied city as "normal life," the German forces had these bakery workers form a football team to play against German military teams.

The team was called FC Start, which we can call "Starting Line."

The Germans did not take them seriously at first. But Start beat the German air force team 5-1. The Nazi officers felt humiliated and arranged a rematch. Before the rematch, everyone knew this was no ordinary game. If Start won again, the consequences could be very serious.

But they won anyway, 5-3.

Afterward, some of the Start players were arrested by the Gestapo, and some died in concentration camps. There have been films about this history, and there are many disputes over details, such as whether they were really executed right after the win. But the core fact has not changed: in an occupied city, a group of men without guns used football to carry out a public act of resistance.

In 1914, football let enemies embrace briefly. In 1942, football let the occupied keep their dignity. You can see that the most special thing about football is not that it can end war. It's that, inside war, it can preserve a little space where people are still willing to follow the rules and still willing to acknowledge each other's existence.

This leads us to the birth of the modern World Cup.

FIFA was actually founded in 1904, but at first its influence was quite limited. The person who really pushed the World Cup forward was the Frenchman Jules Rimet. The name is a bit of a mouthful, so we'll call him "Old Rimet" from here on.

Old Rimet had lived through World War I. After he saw Europe torn apart by war, he began to think about a question: must humanity's instinct to compete be released through war? Could that competition be placed inside a set of rules, so that nations compete through matches rather than through guns and cannons?

That is the original ideal of the World Cup. It did not exist from the start to sell broadcasting rights, sponsorship or tickets. Its underlying idea was to translate conflict into a match, to compress violence into ninety minutes, and to put national emotion onto a patch of grass.

In 1930, driven by Rimet, the first World Cup was held in Uruguay. Conditions were very shabby. European teams disliked the long journey, and many countries did not want to take a boat to South America. In the end, only 13 teams took part. But once this mechanism started, it was hard to stop.

Because the World Cup satisfies a very deep need: every country wants to be seen, and every ordinary person wants, in a safe setting, to cheer once for "us."

Later, television broadcasting appeared, globalization arrived, and commercial sponsorship came too. The World Cup gradually changed from an idealistic international event into the largest attention machine in the world.

The business model of this machine is actually quite simple.

The host country pays. It builds stadiums, subways and airports, handles security, receives tourists, and bears the public pressure and operational risk. FIFA controls the core assets: the event brand, broadcasting rights, sponsorship rights, licensed merchandise, and also the rules of the game and eligibility to compete.

So you see a very strange phenomenon: host countries often don't make money, and sometimes lose a great deal. Brazil spent a lot of money hosting the World Cup, and some of its stadiums were later barely used. Qatar's investment was astronomical, and ticket sales and tourism simply could not recoup it.

But FIFA makes money. It does not have to bear the same costs for every subway line, every stadium and every security operation, yet it takes the bulk of the revenue from global broadcasting, advertising and sponsorship.

So the question is: since host countries know they may lose money, why do they fight to host?

The answer is that they are not buying football itself. They are buying national image.

Qatar hosted the World Cup to make the world see it anew: not just a rich small energy country, but a diplomatic, commercial and cultural center in the Middle East. Saudi Arabia wants to host one too, with similar logic: it wants to repackage itself from a traditional oil giant into a center of modern tourism, entertainment and investment.

So the World Cup is certainly controversial. It has been dogged by rumors of corruption, labor problems, human rights disputes, enormous waste, and the power of FIFA as a super-organization. But at the same time, it is still one of the few shared events in the world today that can draw the attention of billions of people at once.

That is the most complicated thing about the World Cup.

On one side, it is very commercial, even very realistic. On the other side, it genuinely still holds a trace of idealism.

Just like Iran's team standing on an American pitch this time. You can say there is politics in it, there is calculation, there are FIFA's interests and the host's pressure. But you also cannot deny this: when the whistle blows, the rules on the pitch temporarily outweigh the other rules. At least in those ninety minutes, people are not watching missiles, sanctions and negotiating tables. They are watching passes, shots, saves and the score.

That may be the reason football has survived to this day.

Humans invented many things, and then those things turned around and shaped humans. We invented writing, and writing gave us history. We invented money, and money changed exchange and relationships. We invented football, which was originally only for play, and it slowly became a ritual shared by all of humanity.

A small ball being kicked back and forth looks very light. But it has rolled across ancient grass, rolled across the no man's land of World War I, rolled through occupied Kyiv, and it has also rolled into today's World Cup stadiums. It has no power to bring peace to the world. But at least it reminds us: as long as people are still willing to play a match under the same set of rules, humanity has not completely given up on one another.

So football is, of course, just football. But sometimes, it is also more than football.

Well, that's the end of this episode of Cold-Knowledge Blind Box. See you next time!

Devouring Hermès (Part 1)

2026-05-20T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. Today we're talking about Devouring Hermès: the vanished fortune of an heir, and a war over the empire of luxury.

Part One: Old Pi's Vanished Fortune

The story begins with a truly absurd question.

If you had €10 billion, would you check your account every day?

A normal person might check more than once. They'd want to look before bed and again after waking up.

But one heir of the Hermès family took this to the other extreme. He owned six million Hermès shares, worth about €10 billion, and those shares vanished from his world for more than a decade. He didn't realize it until very late.

His name is Nicolas Puech.

His name is too long, so from here on we'll call him Old Pi.

Old Pi is not an ordinary tycoon. He is one of the fifth-generation heirs of the Hermès family. The family started out making saddles, then moved into leather goods and silk scarves and handbags, growing step by step from a French handcraft workshop into one of the toughest luxury brands in the world.

So when Old Pi was born, the ticket he held was not an ordinary entry pass. It was a ticket that practically said "top European family assets" on it.

He worked at Hermès when he was young, and even served as an executive.

But he left soon afterward.

He didn't seem to be the kind of person who had to sit on the board and control everything. Later, he ran his own fashion company, worked as a water-skiing instructor, and then, eventually, moved to Switzerland and lived a country-gentleman life in the European countryside, living off dividends from his Hermès shares.

Picture this.

The Alps. A manor house. An old villa. Far from the family's headquarters in Paris, far from the family council meetings, far from the daily clashes of the luxury industry.

For most people, this would be the end of the story.

But for Old Pi, this was exactly where the disaster began.

Because he simply didn't pay attention to money.

He handed almost everything related to his assets over to one person.

Éric Freymond.

From here on we'll call him Old Fu.

Old Fu was a figure in Geneva's financial circles. Through marriage he had entered a prominent local family, and through connections at a private bank he met Old Pi. The two had known each other for many years, and the relationship ran deep. How deep?

In Old Pi's eyes, Old Fu was not an ordinary financial adviser.

He was a friend, a steward, a secretariat. He was the person who handled the outside world for Old Pi.

Old Fu opened his letters, answered his calls, signed his checks, and even taught him how to communicate with the Hermès family.

Pay attention here. This is the first warning sign.

A billionaire had outsourced almost his entire wealth system to a single person.

It's like a company handing over its finance permissions, contract permissions, email permissions and board communications all to one outside consultant.

If the consultant is reliable, that's convenience.

If the consultant isn't, that's like handing your safe key to a locksmith.

What made Old Fu truly powerful was that he didn't just manage the money. He controlled the gateways.

He held the gateway between Old Pi and the bank.

He held the gateway between Old Pi and the lawyers.

He held the gateway between Old Pi and the Hermès family.

Slowly, Old Pi's financial world was enclosed behind thick glass. Outsiders couldn't see inside, and those inside could only look out through one window: Old Fu.

Then two critical moves took place.

The first was blank signatures.

Old Pi would sign blank documents in advance. Old Fu could then fill in the numbers, the text, and the transaction details, and act in Old Pi's name.

Everyone, this is no longer ordinary authorization.

It's more like handing a stack of blank contracts with the company seal already stamped on them to someone, and trusting that they'll only use them to pay the property management fee.

The second was bearer shares.

Old Fu persuaded Old Pi to change registered shares, which were relatively easy to track, into bearer shares.

Registered shares are like a property certificate with your name written on it. Whoever holds them, whoever transfers them, and when changes happen are fairly easy to check.

Bearer shares are like a safe-deposit key with no name written on it. Whoever holds it has very wide freedom to dispose of what's inside.

You could argue that in a place like Switzerland, where many wealthy people live, bearer shares offer privacy and tax conveniences.

But they also carry a fatal problem.

Once shares become bearer shares, the flow of assets is like walking from daylight into thick fog. If you want to trace where they've gone, the difficulty rises sharply.

So at this point, the conditions for the crime were basically in place.

Old Pi trusted Old Fu completely.

Old Fu held a large number of powers of attorney.

The shares had become harder to trace.

And Old Pi himself rarely looked closely at the accounts.

This was not ordinary carelessness.

This was a vacuum of control.

On paper, the wealth belonged to you. In practice, the wealth had already begun to stop listening to you.

What finally woke Old Pi up was not a bank, not a lawyer, and not Hermès headquarters.

It was the gardener's wife.

During the pandemic, Old Pi had grown very close to the family of the Moroccan gardener on his estate. He treated the gardener, the gardener's wife, and their children as family.

He even planned to leave a large share of his fortune to the gardener's family.

That alone already sounds like a glamorous film about the aristocracy.

An heir to one of Europe's top luxury families, far away from his original family system, growing ever closer to the gardener's family on his estate, and even wanting to change his inheritance arrangements.

But the real explosion didn't come from his will. It came from a bank transfer.

At the end of 2022, Old Pi asked Old Fu: "The one million Swiss francs I asked you to transfer to the gardener's family, how did that go?"

Old Fu hesitated.

After Old Fu left, the gardener's wife told Old Pi: "We never received that money."

Everyone, many big cases are not broken by a brilliant detective. They are broken by the simplest question.

Where's the money?

Only then did Old Pi begin to feel that something was wrong.

He hired lawyers, hired consulting firms, and began a full check of his financial situation.

When the results came in, he was stunned.

The accounts were already empty.

Ninety percent of the Hermès shares had been sold back in 2008.

In other words, while Old Pi still believed he was the largest individual shareholder in Hermès, his wealth base may have been hollowed out years earlier.

It's like someone living in a mansion, looking at the crystal chandeliers, oil paintings and fireplace every day, and believing the house still belongs to them.

But in fact, the property deed was transferred away many years earlier.

In a fit of rage, Old Pi fired Old Fu and sued him in Geneva, accusing him of using decades of friendship to secretly sell a huge amount of Hermès shares behind Old Pi's back.

But at first, the Geneva court did not take Old Pi's side.

The court's logic was cold.

You were extremely indifferent to your own financial situation. In front of Old Fu, you almost completely gave up control. And now the evidence isn't clear enough, so it's hard for the court to step in.

That sounds like legal language. Translated into plain words, it means this:

Your money is gone, and you may have been deceived.

But you were deceived to this extent because you yourself opened the door.

Old Pi didn't give up.

Over the following years, he continued to bring civil and criminal lawsuits in France. At first Old Fu repeatedly denied it, saying he had never touched Old Pi's shares.

But later in the investigation, Old Fu finally admitted that those shares had been sold more than a decade earlier.

Only, he said Old Pi had known about it.

Old Pi, of course, refused to accept that.

The judges didn't fully believe Old Fu either, because Old Pi produced documents showing that Old Fu had repeatedly assured him his wealth was intact.

So Old Fu was provisionally charged with forgery, use of forged documents, and aggravated abuse of trust.

His bank accounts were frozen.

His art was frozen.

Sculptures and furniture were frozen too.

Then the story suddenly turned into a black-and-white thriller.

In July 2025, shortly after being questioned by judicial authorities, Old Fu was struck by a train near a holiday chalet in the Swiss Alps and died.

The police ruled it a suicide.

The key person was gone.

But he took with him not only his own life, but also many details of this unsolved fortune case that may never be explained.

So where did Old Pi's shares actually go?

If only Old Fu had pocketed the money on his own, the story would already be strange enough.

Ask 'And Then What?': Thinking Beyond the First Stage

2026-04-21T00:00:00Z

Intro: Beyond the First Stage

Hi everyone, welcome to Cold-Knowledge Blind Box. When was the last time you asked "and then what?" Three simple words, yet they are a super prompt that can spark your thinking.

Today we're talking about a book called Then What? The Ripple Effects of Economic Policy, by the famous economist and thinker Thomas Sowell. That's the Chinese title. The English original, translated literally, is Applied Economics: Thinking Beyond Stage One. Sowell is also the author of A Conflict of Visions, which we covered on this channel before.

Sowell's book walks through what may be the most important habit in economics, and that's the idea in the subtitle: "thinking beyond stage one."

So what does "beyond stage one" mean? Take any public policy or regulation. Say the government introduces price controls so rent can't go up. Or it provides free healthcare for everyone. Or it passes a law to protect the employment of people with disabilities. At the moment such a policy is announced, which is the "first stage," it usually looks wonderful. It seems reasonable, and everybody cheers.

Rent isn't going up. Everyone can see a doctor. People with disabilities have the law on their side. Who wouldn't welcome a policy like that?

But if you follow the chain forward and look at the knock-on effects, the second stage and the third stage, you often find the outcome is the opposite of what was intended. The people who end up hurt the most are frequently the very people the policy was meant to protect.

Here's a classic example. The cities where it's hardest in the world to rent an apartment are New York, San Francisco and Paris. Housing is scarce, buildings are old and falling apart, landlords can't be bothered to make repairs, and many people have to slip the landlord an envelope of cash just to get on a waiting list. The irony is that all three cities have had strict rent control laws for a long time.

Sowell's Moment of Clarity

Sowell's habit of thinking beyond the first stage came from a moment of clarity when he was young.

When he was an undergraduate at Harvard, in an economics class, his professor, Arthur Smithies, asked him what he thought of a certain policy. Sowell immediately launched into a passionate speech about how much good the policy would do. The professor neither praised him nor argued with him. He simply asked, very calmly, three words: And then what?

Sowell was stumped. He started to work through the "and then what," and discovered that the policy he had been so confident about reversed its conclusion entirely once you followed it into the second and third stages.

"And then what?" is the most concise summary of thinking beyond the first stage, and it is the important habit of thought this book wants to pass on to readers.

I've grouped the policies in the book that only look at the first stage into three typical patterns: price controls, protection that turns into harm, and risk shifting.

Pattern One: Price Controls

The Ripple Effects of Rent Control

Whenever something gets more expensive, from politicians to ordinary people, the first answer that comes to mind is to control the price, stop it from rising, freeze it. Wouldn't that make everyone happy? But that's only the first-stage effect.

Let's use the most classic example, rent control, and keep asking "and then what?"

First stage: The government says rent may not exceed a certain figure. Tenants who already live in the apartments are of course happy, and so are those who haven't found a place yet, at least at first.

Second stage: Developers stop building new housing. They do the math: at the controlled rent, they can't recover their costs, so why build? After World War II, Melbourne, Australia, had very strict rent controls, and for a full nine years not a single new apartment building was completed. More landlords left the rental market than entered it, so the number of units available to rent kept shrinking. Prices didn't rise, but renting became harder, and the apartments people could rent got older. A survey in San Francisco found that more than three-quarters of rent-controlled apartments were over 50 years old, and 44% were more than 70 years old.

Third stage: People already living in these apartments start to waste the space. If you live alone and the rent is at market price, you'll probably choose a small apartment. But when the rent has been artificially pushed down, living alone in a big apartment doesn't cost much, so why move? A 2001 survey in San Francisco found that nearly half of the rent-controlled apartments surveyed were occupied by just one person. This is the most basic principle in economics: when the price falls, people consume more. Each person takes up more housing space, which leaves less for everyone else, so the scarcity gets worse.

Fourth stage: The chain reaction reaches the landlords. They find that repairing buildings no longer pays. In an open market with plenty of supply, a broken boiler gets fixed quickly, or the tenant moves out. But now supply is short, tenants are queuing for a place, and whether you repair or not, someone will rent it. So maintenance declines, and the buildings get more and more rundown and dilapidated. Some landlords can't even afford to keep up the minimum, because maintenance costs have long exceeded the income from controlled rents. Every calculation shows a loss, so they simply abandon the buildings. The New York City government ended up taking over thousands of abandoned buildings, many of which were boarded up with plywood. In some New York neighborhoods, this has become a familiar street sight. The number of abandoned housing units in New York far exceeded the number of homeless people sleeping on its streets.

Fifth stage: A black market appears. Housing is scarce, but there is always someone willing to pay a high price, so money starts flowing under the table. People slip landlords cash, bribe building superintendents, and do all sorts of gray deals. The New York rental market is full of this kind of thing.

Sixth stage (the most ironic step): Developers realize that ordinary apartments don't make money, so they build only luxury apartments, because luxury apartments aren't subject to controls. And so a city that claimed to be protecting the housing of the poor ends up with streets full of housing the poor can't afford. New York has the strictest rent control law in the United States, and it also has the highest rents in the country. That's no coincidence.

In sharp contrast is Massachusetts, not far from New York. After the state repealed local rent controls, some communities started building new housing for the first time in decades. Prices were freed, and they actually fell.

The logic of price controls runs like this: lower prices lead to shortages and declining quality, which leads to a thriving black market and misallocated resources, and in the end prices are actually higher. Every step requires no advanced economic theory at all, just the simple question "and then what?"

Nixon's Wage and Price Controls

In 1971, inflation in the United States was severe. Nixon ignored the objections of economists and announced a full freeze on wages and prices. The first-stage effect was very good. The media applauded, and ordinary people felt the president had finally acted to deal with those greedy bosses and profiteers. Nixon rode this wave of popularity to win reelection in 1972 by an overwhelming margin.

But then what? Herbert Stein, one of Nixon's economic advisers, later recalled: "We found out later that cows were being held back on farms and wouldn't be sold, chickens were drowned, and store shelves were emptied. Controls led suppliers to refuse to ship goods at prices below cost, so shortages set in." It was the classic script again. Only these were second-stage consequences, which slowly showed up after Nixon had already been reelected.

Drug Pricing

Developing a new drug usually takes more than ten years and costs hundreds of millions of dollars, and most research projects fail. A pharmaceutical company may synthesize thousands of compounds a year, and only a handful, maybe one or two, will ever get through approval and onto the market. The few successful drugs have to earn back the full cost of all that research, or no one would bother developing new drugs at all.

But what politicians see is this: the raw materials for this pill cost 25 cents, and you sell it for two dollars. That's pure profiteering, and we can't let these greedy drug companies get away with it. Canada's public health system buys large quantities of American-made drugs at very low prices, pricing them solely on manufacturing cost, without covering any share of research and development.

If nobody pays for that enormous research bill, drug companies have no incentive to develop the next generation of medicines. But the consequences don't show up right away, because a new drug usually takes more than ten years to develop. Ten years later, people notice there are still no new drugs for Alzheimer's, cancer or AIDS, and by then the politicians who pushed for price controls have long since left office and are happily writing their memoirs. Nobody has to answer for this second-stage consequence.

Sowell says: For politicians, killing the goose that lays the golden eggs is a perfectly feasible and perfectly rational political strategy, as long as the goose doesn't die before my next election.

In 1990, European and American drug companies each held one-third of the global pharmaceutical market. By the time Sowell wrote this book, Europe's share had fallen to 21% while America's had risen to 50%. European drug companies moved their research centers to the United States, because only American prices let them recover their research costs.

The Second Pattern: Protection That Turns into Harm

Disability Laws

In 1992, the United States passed the Americans with Disabilities Act, which requires employers to provide various reasonable accommodations for disabled employees, such as making sure wheelchairs can get into the office. The first-stage effect looked good. People with disabilities had legal protection, and many companies did improve their working conditions because of the law.

But then what? An economic study found that after the act was passed, employment among people with disabilities actually fell. The reason is simple: faced with the extra compliance costs and the risk of lawsuits, employers decided not to hire disabled people in the first place. The law protected disabled people's right not to be discriminated against, but it took away their chance to be hired.

This case also reveals a deeper economic principle: people's behavior tends to follow costs, not intentions. Change the cost structure, and you change the behavior.

How the Free Market Erodes Discrimination

Turn that around. If the cost of discrimination goes up, does discrimination simply decline? Something naturally raises the cost of discrimination, and that's free-market competition.

In apartheid-era South Africa, the government passed many laws to systematically discriminate against Black people. For example, many respectable jobs could not legally be given to Black workers. But even under this notorious system, the cost mechanism of the free market quietly eroded the foundations of apartheid. White builders, to save money and meet deadlines, secretly hired Black workers on a large scale. That broke the apartheid laws, but these businessmen weren't going to lose money over their principles. The government ended up having to fine several hundred construction companies.

On Broadway in New York in the 1920s, there were already Black stars. But in the same decade, Black people couldn't even join the U.S. Navy, and Black women couldn't even work as telephone operators for the phone company. Why was discrimination so different within the same era and the same country? Because Broadway was a fiercely competitive industry where you could win big or lose big, and a producer who turned away a Black performer who could fill a whole theater would pay a steep price. The telephone company, on the other hand, was a government-licensed monopoly. Its discrimination costs could be passed on to all its customers, so management could act on its racial preferences without paying anything for it.

California's Land Conservation Policies and Soaring Housing Prices

California is one of the places with the highest housing prices in the United States, but that isn't because California homes are especially luxurious. The problem isn't the construction cost of the houses themselves. It's the price of the land.

Starting in the 1970s, the California coast implemented a wide range of land restrictions: open-space laws, historic preservation laws, farmland protection laws, building permit approvals and more. The motives varied. Some were about protecting the environment, some about preserving the look of the city, and some simply because longtime residents wanted their houses to appreciate in value. But the effect was the same: a large part of a city was declared off-limits for building.

In San Mateo County, next to San Francisco, more than two-thirds of the land was designated as so-called open space, where the law forbids building anything. Doesn't the remaining third's price skyrocket?

In 1969, the median home price in San Jose, the heart of Silicon Valley, was 2.2 times the local median household income. A family could pay off a mortgage in 12 years by spending a quarter of its income. By 1979, the ratio had risen to 4 times, meaning 40% of income for 30 years. By 2005, the ratio had reached an astonishing 7.5 times.

These policies protected the space of the city and the longtime residents who already lived there, because their houses did appreciate. But for the young people, the poor, the teachers, nurses and police officers who wanted to move in, sorry, you can't afford it, please get out. In the Palo Alto area, only 7% of police officers could afford to buy a home in the city they worked in, and nearly a quarter of police officers lived on the other side of San Francisco Bay.

California real estate agents have a saying: "Drive until you can afford it." It means you drive inland on the highway, all the way out to the place where you can finally afford a house, and that's where you live.

From 1990 to 2006, the Black population of San Francisco fell by 35%, and in neighboring San Mateo County it fell by 33%. This wasn't because of racism. It was because they simply couldn't afford to live there. People talk about needing diversity and affordable housing, but their land conservation policies push poor people and minorities out to 100 miles away.

The most interesting contrast with the California coast is Las Vegas. Over the last two decades of the 20th century, the population of Las Vegas nearly tripled, yet the median home price, adjusted for inflation, didn't change. Because there were almost no land conservation policies there, the supply of new housing kept up with demand. Later, some environmental groups succeeded in blocking the sale of federal land to developers, and only then did Las Vegas's home prices take off. Same script again.

FDA Approval Incentives

The U.S. Food and Drug Administration (FDA) is responsible for approving new drugs in the United States. Its primary mission is to protect the public from dangerous drugs. The intention is certainly not a problem, but Sowell says this is only the first stage.

The trouble is that the officials who approve new drugs face wildly asymmetric incentives:

If an official approves a new drug, and it later turns out to have side effects that kill 1,000 people, those 1,000 people all have names. Their families will appear on television in tears, Congress will hold hearings, and that official's career is over, perhaps even with prison time.
But if the official doesn't approve the drug, 10,000 people might die because they can't get treatment. Those people, however, are often just a number in a mortality statistics table.

A former FDA commissioner sighed: "Congress has held so many hearings on FDA approvals that went wrong that we ourselves have lost count. But in all of history, we have never once been called to a hearing because we failed to approve a new drug."

The most classic example may be aspirin. Medical research had long shown that taking a low dose of aspirin every day could cut the risk of a heart attack in middle-aged men by nearly 50%. But at a 1988 meeting, the FDA clearly told all aspirin manufacturers: you may not advertise this use, or we will sue you. One study estimated that this ban led to tens of thousands of people each year who could have avoided heart attacks by taking aspirin, but didn't.

Medical Malpractice Lawsuits

The purpose of medical malpractice lawsuits is to let victims of medical errors receive compensation and to make doctors more careful, which protects patients to the greatest possible extent.

But then what? In some U.S. states, obstetricians pay nearly $40,000 a year in malpractice insurance premiums (and of course, that cost ends up being passed on to the patients). As many as 76% of American obstetricians have been sued at least once, so more and more obstetricians choose to leave places where lawsuits are easy to file.

The book describes a pregnant woman in Nevada, 13 weeks along, who called 28 obstetricians and not one would see her. The litigation risk in that area was so high that nearly all the obstetricians had left.

Even when doctors did accept patients, they practiced so-called "defensive medicine," meaning that whenever they could over-treat, they never played it safe. Among Pennsylvania obstetricians, 54% frequently ordered tests they themselves considered unnecessary, and 38% performed invasive procedures that weren't needed. None of this was because medicine required it. Doctors were protecting themselves from being sued.

A law meant to protect patients ended up loading them with extra risk and cost, and in some cases made it impossible for them to find a doctor at all.

The Third Pattern: Risk Shifting

Insurance for Hurricane Country

Some coastal areas of the United States get hit by hurricanes regularly. If you want to build a house in such a place, private insurers will demand very high premiums or simply refuse to cover you. The market is telling the truth. It's warning you: living here is dangerous and expensive, so think it over.

But politicians step in and say this is too cold-hearted. So they pass laws that forbid insurers from pricing according to the real risk, and even have the government act as the insurer of last resort, using taxpayers' money as a backstop.

The first-stage effect is certainly good. Coastal residents keep their homes, premiums become "affordable," and politicians win votes.

But then what? Because the cost of living in these dangerous zones has been artificially lowered, even more people build houses in hurricane country. When the next hurricane arrives, whole neighborhoods of houses are blown down again, and the government launches disaster relief, using taxpayers' money to help the victims rebuild. But many people rebuild on the very same spot, in the same place that keeps getting hit, because they know that next time the government will come to help again.

A small number of people keep up their high-risk behavior, politicians win hearts, and taxpayers pick up the bill.

Universal Free Healthcare

The government promises that everyone can afford to see a doctor. The first-stage effect looks wonderful: when you get sick, you don't pay out of pocket, the state covers the cost, and personal financial risk is shifted to all taxpayers.

But once seeing a doctor no longer hurts your wallet, what happens? There's an old French saying: every healthy French person has a patient inside them, longing to be diagnosed. If medical care is free, then a headache you could have beaten with a painkiller can now become a trip to the clinic. A minor ailment not worth a special trip to the hospital suddenly seems worth checking out, because it doesn't cost anything anyway. Why not?

It's not just patients whose behavior changes. Doctors change too. Since the bill is guaranteed by the government, doctors have no qualms about prescribing expensive drugs and ordering all kinds of tedious tests. Some even have an incentive to split a shot that could be given in one go into two, so they can bill two visits.

So a huge number of minor problems pour into the healthcare system, and doctors have no incentive to hold back. The truly urgent medical needs are completely swamped by a flood of non-urgent ones. And since price can no longer be used to screen patients, something else has to do the screening, and that thing is time: long waiting lines.

In countries that run universal free healthcare, such as Britain and Canada, patients often wait more than half a year for life-saving elective surgery such as heart bypass or joint replacement. The same operations in the United States involve almost no wait. Many seriously ill patients have let their conditions deteriorate beyond saving while they waited.

A better healthcare system would let patients feel the sting of paying for minor illnesses while being protected against major ones. That might create more sensible incentives.

Conclusion: Politics Is Where First-Stage Thinking Loves to Hide

The three patterns are not identical in mechanism, but in the first chapter of the book, Sowell points out that they share a structural cause: politicians' terms are too short, and the policy consequences take too long to appear.

The benefits of a policy show up before the politician's next election. Its costs erupt many years after the vote. That is the fundamental reason first-stage thinking can keep succeeding in democratic societies.

Nixon himself wrote in his memoirs that he knew wage and price controls were wrong in the long run, but that they were politically necessary and very popular in the short run. His economic adviser Herbert Stein also confirmed that from the start, the White House never expected these controls to actually curb inflation.

So in many cases, politicians aren't unaware that some policies have consequences beyond the first stage. Politics itself is a game played only at the first stage.

Sowell says that economics has an old nickname: "the dismal science." It sounds gloomy, not sunny enough, not positive enough. Sowell thinks the nickname is quite accurate, because economics really doesn't offer feel-good answers. What it offers is trade-offs. It tells you that nothing in this world is free, every choice has a cost, and every cost is borne by someone, though that someone may not be you, and may not even be born yet.

Politics, on the other hand, excels at offering the illusion of costless solutions. A politician can tell you without any burden: we can shut down every coal power plant and lower electricity prices at the same time. So politics is where first-stage thinking loves to hide.

Well, that's where we'll stop for this episode of Cold-Knowledge Blind Box. See you next time!

WWII Stories (Part 3): Heroes in Twilight, Honor in Silence

2026-04-08T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. In the previous two episodes we covered the prequel to The Imitation Game, the story of how Polish codebreakers were the first to take on the German intelligence black box, the Enigma machine, years ahead of Britain and France. Polish mathematicians proposed turning code-breaking into a quantifiable mathematical algorithm problem, which pushed Britain and France into modern intelligence work.

On the eve of the nation's fall, Poland passed this whole secret system against Nazi Germany to Britain and France. In the end it inspired Turing to lead countless women workers at Bletchley Park in building humanity's first computer, laying the foundation for the information revolution.

This time we're focusing on what happened next to the three Polish mathematicians who inspired that pioneering work.

Ending: Turing Changed the Second Half of the War, the Poles Opened Up the First Half

So if you watch The Imitation Game again today, you'll still be moved by Turing.

You'll still think it's a remarkable film.

It's just that you'll know:

it tells the main story.

It is not the whole story.

The prequel that was truly missing is about three young Polish men.

It's about a country on the verge of collapse, which in its final hour handed its most valuable secret to the people who came after.

It's also about the most secret and yet most costly contest in a war: who could first tear open the enemy's nervous system.

If I had to sum up this missing prequel in one sentence, it would be this:

Turing changed the second half of the war. The Poles opened up the first half.

Epilogue: Where Did the Man Who First Pushed the Door Open End Up?

If this were a standard film, the story would be over by now.

The German black box has been cracked open.

Turing and Bletchley Park took up the torch.

The Allied intelligence machine began to run.

The course of World War II was quietly rewritten.

The camera pulls back, the credits roll, and the audience gets up and leaves.

But the cruelest thing about real history is this:

a film can stop at its most powerful moment,

but a person cannot.

Because when the main feature of The Imitation Game begins, the people who actually wrote that prequel did not receive any mythical fate.

Instead, their lives later came to resemble a long film with no audience.

The small team of the Polish Cipher Bureau did not disappear once their country fell.

They did not get wiped off the map with Poland in September 1939.

Instead, they kept working in exile.

In France, they moved into an intelligence station in the Vichy-controlled zone and formed what was called the Z Group. They kept breaking Enigma, kept improving their prototype machine, which they called "the bomb," and held on for another two years, grinding it out.

During those two years, they were not reminiscing about past glory.

They were still working.

And not on small matters.

By one count, during this time they broke more than nine thousand German intelligence messages.

These messages directly or indirectly affected German operations in Yugoslavia, Greece and the Soviet Union, and supported the Allied battle plans that later opened the front in North Africa.

In other words, the Polish story did not end with "handing over the baton in 1939 and leaving the stage of history."

Not at all.

Even after losing their country, they kept settling the accounts of this war.

Only now, they no longer had a nation.

They only had their missions.

Then fate began to take them, one by one.

On January 9, 1942, one of the members, Jerzy Różycki, died at sea when the ship he was on, returning from Algeria to France, was involved in an accident.

If you pull this detail out on its own, it feels strange.

A person who had helped rewrite the structure of wartime codes did not die in some heroic battle, nor in the kind of thrilling gunfight you'd see in an intelligence film. He died in a maritime accident.

That is the real coldness of history.

It never wraps up its characters the way literature wants.

It doesn't give someone a dignified exit just because they once did something important.

Rejewski and Zygalski did not escape danger because of this, either.

After Germany invaded France, their situation grew more and more dangerous.

Staying meant waiting to die.

So they had no choice but to go into exile again.

On November 9, 1942, the day after the Allies landed in North Africa, they set out on another escape.

By then they were no longer just a few young men studying combinatorics at university.

They were people carrying a huge secret, without a homeland and without a safe rear.

On January 29, 1943, they tried to cross the Pyrenees from France into Spain.

They were caught at the border by Spanish security police and sent to a refugee camp.

It's hard not to picture that scene.

The mathematician who had been the first to tear open a crack in Germany's most secret system,

the man who had made the first move in the code war and changed the fate of an entire hidden front,

was now freezing, falling ill and staying silent in a refugee camp, not daring to reveal his true identity.

Later, Rejewski developed rheumatism.

Throughout all of this, they never revealed to anyone who they really were or what they had done.

That part is especially painful.

Because you realize that many people who truly got things done in history were not recognized by the world right away.

Sometimes it's just the opposite.

While they were alive, the most important part of themselves had to be hidden first. Like Van Gogh. Like Kafka, who entrusted his unpublished manuscripts to a friend but told him to "burn everything." Like Tesla, whose late years were misunderstood by his era and swallowed by commercial narratives. Like the many nameless codebreakers and engineers who took part in decisive work but had to bury their names in archives during the most critical years and leave their résumés blank, because in that world, exposing your identity did not mean winning applause. It was far more likely to mean losing your way out.

Later, they were finally released and made their way to Gibraltar, then sailed to England.

But when they arrived in England, there was none of the "heroes return home" scene so common in literature.

The British, of course, knew what they had done and knew their real role in breaking Enigma.

But they were not pushed back to the center of the stage because of it.

Instead, the British deliberately kept them out of the inner circle.

They were not put back on the main battlefield of Enigma.

They were only asked to analyze a different German cipher system, the SS code.

This arrangement is worth thinking about.

It shows that war has never been simply "whoever contributes most gets the biggest stage."

War is also a combination of systems, organization, secrecy, national interests and the power to control the narrative.

A spark may come from the edge,

but the person standing beside the furnace at the end may not be the one who first lit the fire.

After World War II ended, Rejewski finally returned to Poland.

That was already 1946.

He went back to reunite with his wife and two children.

There was no triumphant homecoming.

No nationwide welcome.

No grand ceremony recognizing that "you changed the course of the war."

He later worked in Poznań.

Some accounts say he did administrative work at a university, and others say he later worked as an ordinary accountant at a factory.

No matter which version is true, the meaning is much the same:

the man who had been among the first to see through the essence of Enigma went on to live an almost ordinary life, so ordinary that the times seemed ready to swallow it.

And he stayed silent for a long time.

About what he had done before and during the war, he said nothing.

He did not talk about it.

He did not explain.

It was as if he had pressed the brightest and heaviest part of his life back into darkness.

In 1967, he retired.

On February 13, 1980, Rejewski died of a heart attack in Warsaw at the age of 75.

He was later buried in a military cemetery in Poland, with full military honors.

That is certainly far more fortunate than many people who were completely forgotten.

WWII Stories (Part 2): The Secret Polish Front Nobody Talks About

2026-03-26T00:00:00Z

If you've seen The Imitation Game, you probably remember an image like this.

England. Bletchley Park. A group of eccentrics. A young mathematician named Turing. A huge machine. And a line that has since been turned into almost a myth:

Breaking Enigma changed World War II.

That's certainly true. The trouble is, it only tells the second half of the story.

Because before Turing walked into Bletchley Park, before the more famous British "bomb" started humming, before Britain turned code-breaking into a full national project, someone had already pushed that door open a crack.

And it wasn't the British.

It was the Poles.

The same Poland that, in the autumn of 1939, was caught between Germany and the Soviet Union, torn apart within a few weeks, and later often written up as a "tragic supporting character."

But if today we're not talking about the front lines, and instead about cryptography, intelligence and information warfare, Poland was anything but a supporting character. It was the lead of the prequel.

───

I. What the Germans really feared was not just tanks

When most people think of World War II, they picture Blitzkrieg, tanks, and how fast the German army advanced.

All of that is true. But if you look at it from the Allied point of view at the time, you'll find that the most frightening thing about the Germans may not have been their steel or their aircraft, but their communication system.

Put simply, in the most dangerous years of the war, the German advantage that felt like cheating was that they believed every word they said was something no one else could understand.

That's a serious problem.

Because in war, the most expensive thing has never been a tank or an airplane. The most expensive thing in war is a wrong decision.

Take the Battle of the Atlantic. Britain, an island nation, lived on sea transport. If German submarines figured out your convoy routes, what you'd lose wouldn't just be a few ships. It would be the nation's lifeline.

Or take North Africa. Why did Rommel get harder and harder to beat? A very simple reason: fuel shortages. But here's the question: why did his fuel tankers keep running into the British "by chance"? Was it really just luck for the British navy? Of course not.

And then there's the Normandy landing. Why did the Allies dare to bet so big? How could they be sure the Germans had been led astray by false intelligence? It wasn't because they were gambling. It was because they had really started to see the Germans' hand.

So breaking Enigma looks on the surface like cryptography, but in reality it was a fight over the most expensive thing in war:

Information advantage.

Whoever can read the enemy's communications is the one who makes fewer of the costly mistakes. And whoever makes fewer mistakes is the one who can put money, ships, oil, time and troops where they are needed most.

So the frightening thing about Enigma wasn't that "it was a cipher machine." It was that it was effectively a lock on the outside of the Third Reich's nervous system.

───

II. Why Enigma was so terrifying

Enigma is a name many people know today. But if you think of it as just "an old cipher machine," you're still underestimating it.

What made it truly frightening wasn't only its complexity. It was how cleverly it was designed.

On the surface, it was very simple. You press a letter, say A, and the machine lights up another letter, say X. You write down X and send it. On the other side, a machine set up the same way takes X and turns it back into A.

Sounds almost like a children's cipher game, doesn't it? The problem is, what's happening inside is nothing like that.

Inside it there are rotors, a reflector and a plugboard. Every time you press a key, the internal structure moves. That means the same input doesn't always produce the same output.

Press A today and you get X. Press A again a second later, and it might no longer be X.

What does that mean?

It means Enigma wasn't a lock. It was a lock that changed its own combination every time you pressed a key.

And even worse, the Germans had a particularly cruel rule:

Every night at midnight, the whole military changed its settings.

This created a particularly hopeless situation.

You spent a great deal of effort, and finally caught hold of some pattern. Then at twelve o'clock, everything was thrown out. Yesterday's clues couldn't simply carry over, and yesterday's work couldn't simply be reused. The next morning, you woke up to a whole new maze.

So the most terrifying thing about Enigma wasn't just "there are too many combinations." It was that it made traditional methods of code-breaking almost completely useless.

Guessing words wasn't enough. Intuition about language wasn't enough. Yesterday's experience wasn't enough either.

It didn't just give people a headache. It drove them to despair.

───

III. The British and French got the clues first, but still couldn't break it

Here's a dramatic detail.

The first to get their hands on material about the German cipher system wasn't Poland. French intelligence had, quite early on, used spies to obtain important information related to Enigma.

The British and French didn't hold back on effort either. They brought in many of the smartest people available: cryptographers, linguists, classical scholars, and even people who were very good at spotting patterns.

On paper, that lineup was already impressive. Yet after studying the problem for a long time, they reached a deeply discouraging conclusion:

It's too hard. Almost impossible.

What was the problem?

It wasn't that they weren't clever. It was that they were looking at the wrong problem.

They treated Enigma as a language problem. They tried to find words in letters, sentences in words, and patterns in sentences.

But the person who first saw through the problem clearly realized something critical:

This isn't a language problem. This is a mathematical problem.

And that person was the Polish mathematician Marian Rejewski.

───

IV. Why Poland was the first to understand it

There was a very cruel reality behind this.

The British and French could afford to study failure. Poland could not.

Britain was across the Channel. France, however weak, still had some buffer. They were certainly afraid of Germany, but they hadn't reached the point of "if we can't break this today, we'll be a conquered nation tomorrow."

Poland was different. Poland sat right next to Germany. For Poland, not being able to understand what the Germans were saying wasn't an academic regret. It was a matter of life and death.

Poland had no room to trial-and-error slowly. It couldn't afford to spend too long on the wrong method, the way Britain and France could.

So the Polish Cipher Bureau made a crucial decision:

If linguists can't do it, then we'll try mathematicians.

They recruited three young men:

Marian Rejewski • Jerzy Różycki • Henryk Zygalski

These three would later become the names most worth remembering from the prehistory of the cipher war.

───

V. Rejewski's real greatness was admitting everyone had been looking at the problem wrong

What made Rejewski remarkable wasn't that he was "a little smarter." It was that he first made one crucial flip.

Others saw gibberish. He saw structure.

Others were still guessing words. He had already started looking at permutations, cycles and mappings between letters.

In his eyes, Enigma wasn't German, wasn't messages, wasn't sentences. It was a system that could be described mathematically.

That step was critical.

Because many problems that look unsolvable aren't without answers. You've just been asking them the wrong way all along.

The experts from Britain and France were still "reading letters." Rejewski had begun "reading the machine."

What he understood first was this:

Enigma can't be solved by slowly guessing its way through. It has to be modeled, reverse-engineered and handled by machines.

With that, the whole thing changed completely.

───

VI. Before Turing's "bomb," Poland already had its own "bomb"

Later, when people talk about The Imitation Game, they remember Turing and Britain's "Bombe."

But even earlier, Polish people had already built their own Bomba.

It wasn't a general-purpose computer in today's sense. But it was already very close to what we'd understand as a special-purpose computing machine.

Its only purpose was:

To hand over the combinations a human brain could never test by hand to a machine to run.

This matters a great deal.

Because it shows the Poles didn't just "understand it in theory." They had already begun turning the problem into engineering.

In other words, the Poles had not only understood the question first. They also realized early on:

A problem of this scale can't be solved by intelligence alone. You need machines too.

Put simply, they had already caught a whiff of the "computing war."

And that's exactly what makes this part of the Polish story so tragic. They understood it too early, but their country was not strong enough to carry this path all the way through.

───

VII. The truly cruel part: they were close to success, but the country was about to fall

If the story stopped here, it would already be legendary enough. But the most painful part of Poland's history is the second half.

In 1938 and 1939, Germany upgraded Enigma. Its complexity went up.

Poland wanted to keep up, of course. The problem wasn't that they didn't know how. The problem was that they couldn't sustain it.

It's like a small team that gets the algorithm right and bets on the right technical path, only to run out of cash just as the project enters the stage of building machines, scaling up and sustaining heavy investment.

They had already found the answer. But the country was about to fall.

That's the most painful part of this Polish story. It wasn't a lack of skill. It was a lack of national strength.

───

VIII. So last time, we stopped right here

How Did Claude's Revenue Beat Its Old Boss in Two Years? The Story of Anthropic and Dario Amodei

2026-03-15T00:00:00Z

Video Information

Original link: https://youtu.be/wzxCvd3rWZk
Note: This article is based on a local transcript. Ads, subscription prompts and unrelated remarks have been filtered out. Only segmentation, headings and sentence cleanup were done. The original video's information and order are kept as much as possible, with no added explanatory paraphrasing.

Opening

OpenAI and Anthropic are the two most influential AI companies in the world.

The CEOs of these two companies were once put on stage together, ready for a handshake photo, and they absolutely refused to shake hands.

These two former colleagues who fell out are now outright rivals.

And on Dario's side, they even ran a brilliantly ironic ad during the Super Bowl.

That move was ruthless. You could say it really rattled OpenAI's Altman.

How did Anthropic manage to knock OpenAI flat in the enterprise AI market in just two years?

The Numbers

Let's look at the numbers first.

In 2023, the enterprise AI market was still OpenAI's turf, with half the market share. Anthropic had just 12% back then, and everyone assumed the outcome was already clear.

Then, by the end of 2025, the story had completely flipped.

Anthropic rose to around 40%, while OpenAI fell to just over 20%.

The revenue numbers are even more dramatic.

Anthropic had only about $100 million in 2023. It grew to $1 billion in 2024, and in 2025 it approached $10 billion. That's nearly a hundredfold increase in three years.

Epoch AI's latest analysis from this February said that at this pace, Anthropic's total revenue would overtake OpenAI's by the middle of this year, making it the most profitable AI company in the world.

How did this company overtake its rival so quickly in two years?

Dario Amodei's Background

We have to start with why Dario Amodei, their boss, left to found Anthropic.

Dario was born in San Francisco in 1983.

His family was nothing like the Silicon Valley elite. His father was an Italian-American leather craftsman, and his mother was a Jewish-American library project manager.

He grew up in the Mission District of San Francisco. Anyone who knows the city knows it was never a high-end neighborhood.

In high school, the dot-com bubble was in full swing. Everyone around him was thinking about how to write code and get rich, but he had no interest in that at all.

In 2000, 17-year-old Dario was selected for the US Physics Olympiad national team. He studied at Caltech first, then transferred to Stanford to study physics, and then went on to Princeton for graduate school, where he earned a doctorate in biophysics.

So he's a physicist, and he also holds a PhD in neuroscience. This double academic background gives him a way of looking at things that is completely different from the AI founders in Silicon Valley.

How His Father's Death Affected Him

In 2010, his father died of chronic myeloid leukemia.

But only a few years after his father passed, second-generation targeted drugs were developed. For that kind of cancer, survival rates jumped from 50% to 95%.

If scientific progress had been just a little faster, his father might still be alive today.

That completely changed Dario.

He began to think: if some technology could dramatically speed up scientific research, a tragedy like his father's would never have to happen again.

In the end, he saw that opportunity in AI.

From Baidu to OpenAI

In 2014, at 31, he joined Baidu's AI lab in Silicon Valley, and later went to Google Brain.

In the summer of 2015, Musk and Altman held their famous dinner at the Rosewood hotel and invited top AI researchers, including Dario and Ilya, to attend. That's how Dario became one of OpenAI's earliest core members.

During his years at OpenAI, he personally built GPT-2 and GPT-3. His team also developed RLHF, reinforcement learning from human feedback. This technique is what made AI obedient, easy to use, and safer.

Why He Left OpenAI

But during his four years at OpenAI, he and Altman had many disagreements about how AI should be developed.

It's like inventing a new drug: the animal tests look promising, but the human trials aren't finished yet.

Altman's approach was to launch first, watch the side effects as the product sells, and use scale to gather data.

Dario felt that you should complete the human clinical trials first, make sure it was one hundred percent safe, and only then release it, even if a rival grabbed the market first.

By the end of 2020, Dario couldn't stay any longer. He resigned together with his sister Daniela and several friends from the Google Brain days, seven people in all. It was the biggest exodus of talent from OpenAI's early days.

How Anthropic Was Founded

In 2021, these seven people founded Anthropic, splitting the equity almost evenly among themselves.

They also deliberately registered the company under a rather special corporate structure.

It means that if the CEO one day refuses to release a new model for safety reasons, shareholders cannot legally oust him.

They really did write "building safe AI" into the company's charter.

Holding Back Claude, Even Though It Could Have Launched Earlier

In fact, back in the summer of 2022, before ChatGPT came out, they had already trained the first version of Claude.

They could have launched first, but they chose to run more safety tests.

A few months later, OpenAI released ChatGPT the moment it was finished, and it exploded worldwide instantly.

Anthropic did miss the chance to be first, but it also proved that they really put safety and reliability first.

Why the Core Team Is So Stable

Now fast-forward to today.

Look at Musk's xAI: half of its twelve co-founders have left. It's not just the co-founders. Even its CFO stayed only a short time before walking out.

Turn back to OpenAI, where only three of its eleven founding members remain. Those who left include Musk, who fell out with Altman; people who departed after the failed boardroom coup; and textbook-level heavyweights like Andrej Karpathy and John Schulman. All of them are gone.

And now look at Anthropic.

Seven co-founders, working under the equal-split equity structure that startups fear most, five years in, and not one of them has left.

The reason it has stayed so steady is actually another key figure that most people have overlooked: Dario's sister, Daniela Amodei.

Daniela Amodei's Role

Most AI companies are run by technical people. The founders are brilliant scientists, but they don't know how to manage people, money, or an organization.

Anthropic's leadership is a very rare combination: siblings running the company together.

Daniela studied English literature in college. In 2013, she became Stripe's 45th employee.

During her five years at Stripe, she watched with her own eyes how a startup grew from dozens of people to thousands without falling into chaos.

She knows very well that when a talented engineer is hard to work with, it is devastating to team collaboration.

So she built a process specifically to screen out brilliant jerks.

That means at Anthropic, no matter how skilled you are, if your values don't match, you won't be hired.

They would rather go without top talent than let people with mismatched values in.

Why They Turned the Enterprise Market Around

Now that we understand Anthropic's stable core team, let's look at how they turned the enterprise market around.

First, from day one, Anthropic's strategy was set in stone: no mass market, no flashy products, only the needs of enterprise customers.

Many people think AI safety is just a hollow marketing slogan. But for enterprise customers, safety is a firewall, and a firewall is exactly what big companies care about most.

Think about it. Would a bank dare let an AI leak customer data? Would a drugmaker dare let an AI give wrong medical advice? Insurers require every AI interaction to be traceable and auditable, and every regulated industry needs its AI to comply with European and national regulations.

If you can't clear these basic hurdles, you don't even get a seat at the briefing.

Anthropic has built these requirements into its underlying architecture from the very first day.

The result is that transnational giants with extremely strict security requirements, such as IBM, Pfizer, Novo Nordisk, and Norway's sovereign wealth fund, have chosen Anthropic one after another.

Why Claude Code Was the Turning Point

If safety was the foundation that got enterprises to nod, then Claude Code, launched in May 2025, was the key that let Anthropic completely turn the tables.

Claude Code is no longer just a tool. It's a senior engineer that can help you finish a project directly.

First, it can close the loop on its own.

If you ask it to refactor an API, it will run the tests in the terminal by itself. When it sees errors, it fixes them, and it keeps going until every test turns green.

Second, it has a strong grasp of very large codebases.

Most AI handles a single file very well, but it gets lost in an enterprise project with tens of thousands of lines. Claude Code can move between different files and precisely track variable definitions and all sorts of dependencies.

Third, its hallucination rate is far lower than its rivals'.

When it's unsure how to use a certain library function, it checks the documentation itself instead of inventing a syntax that doesn't exist.

A principal engineer on the Google Gemini API team publicly said that Claude Code took only an hour to write a pricing design that Google's internal team had spent a whole year on.

Even Andrej Karpathy, a founding member of OpenAI, often says that when it comes to coding, Claude is definitely his first choice.

Even the developers of Claude Code admit that much of Claude's own code was written by Claude itself.

Why Enterprise Customers Are Willing to Pay

As a result, Anthropic's enterprise customers grew from fewer than a thousand to over 300,000 in two years.

Large customers spending more than $100,000 a year grew more than sevenfold within a single year.

Now let's look at the essence of the battle between Anthropic and OpenAI.

A large part of OpenAI's money comes from consumer subscriptions, where the average person pays maybe twenty-something dollars.

A large part of Anthropic's money comes from enterprise contracts and API calls. The amount each paying user contributes on average differs enormously.

People treat ChatGPT as a super search tool: looking up information, translating, asking everyday questions. In people's minds, it's priced at Google's level. Free is best, and paying $20 already feels expensive.

Anthropic is completely different in the enterprise. It solves real business problems.

Traditional AI at best gives you suggestions, but Claude Code takes over the entire development environment, runs the tests, and catches bugs on its own. When enterprises do this math, the benchmark is a senior engineer on a seven-figure salary.

Compared with that, an API bill of a few hundred dollars is ridiculously cheap.

OpenAI and Anthropic Are Fighting Different Battles

As a result, Anthropic captured the most valuable part of enterprise AI spending with only a fraction of ChatGPT's user base.

And the enterprise market has a trait that the consumer market doesn't.

Today you use ChatGPT, tomorrow you try Gemini, the day after you switch to Claude. Switching costs are almost zero.

But if a bank has spent six months integrating Claude into its lending, review, and internal systems, telling it to switch to GPT tomorrow is simply impossible.

That's why Anthropic's revenue structure is so healthy.

Enterprise contracts often run for several years, so the numbers are predictable and gross margins are high.

While OpenAI was charging into the consumer market, trying to win over more free users, Anthropic was quietly signing up big customers and focusing all its energy on the enterprise market.

On the surface, the two companies run on the same track, but they are fighting entirely different battles.

By the time OpenAI turned around and wanted to seriously compete for the enterprise market, Anthropic had already locked in the most valuable of those customers.

Ending

So now you know: Anthropic's comeback was practically a foregone conclusion.

Compared with Altman, Dario has always been very pragmatic. He puts everything into technology and R&D, and puts building relationships and hype far behind.

As Eric Schmidt, former CEO of Google, said when he invested in Anthropic: at the very early stage, you have no data, no revenue, no market, and no product. What you're betting on is Dario, the person.

In this most important AI showdown in human history, the market bet on speed, the investors bet on the person, and Dario bet on a safe, responsible AI that, had it arrived a little sooner, might have saved his father.

Futures in One Go: Why the World's Smartest Risk Tool Keeps Driving People to the Edge

2026-03-12T00:00:00Z

Have you ever asked yourself this?

If futures are meant to reduce risk, why do they always show up in the news in such a bloody, dramatic, almost casino-like way?

An 8-minute trading session in a national bond futures contract brought down a brokerage. A nickel price that doubled twice in two days scared the world's biggest nickel trader and JPMorgan half to death. Crude oil futures fell below zero, which meant "I'll give you the oil for free and pay you to take it."

You'd find that strange.

If a financial instrument was really created to help the real economy manage risk, why does everyone remember it only for:

Short squeezes
Forced liquidations
Negative equity
Manipulation
Margin calls
Trades being voided by the exchange
Institutions going bankrupt
Retail investors buying the dip all the way to the basement

Doesn't something seem off here?

The problem is, it isn't wrong, either.

Futures weren't invented by a casino. They aren't a demon toy Wall Street dreamed up with nothing better to do. At heart, they're one of the cleverest, most advanced, and most necessary inventions in human commercial history.

It's just that this thing is too powerful.

How powerful?

Powerful enough that once it gets tangled up with leverage, delivery, liquidity, funding pressure, and human greed and fear, it can turn from a rational risk-management tool into a super-amplifier that flips over the entire market mood, an entire financial institution, and even the whole regulatory system.

If you forced me to sum up futures in one sentence, I'd say:

Futures aren't for trading commodities. What futures really trade is "uncertainty about the future."

And the most expensive thing in human history has always been uncertainty.

I. What is a futures contract, really? It looks like it trades goods, but it's really trading "tomorrow"

Let's make this as simple as possible.

Say you run a milk tea shop. What do you fear most?

You fear rising ingredient costs. Milk, tea leaves, fruit, sugar. Today the costs look perfectly normal. Then three months from now, prices jump, and the profit on every cup you sell disappears.

So what do you do?

A very down-to-earth solution is this: you agree with your supplier in advance. "In three months, I'll buy a batch from you at today's price."

That is essentially the original logic behind futures.

Now flip it around.

Say you're a soybean farmer. What you fear isn't rising prices. What you fear is a bumper harvest that sends prices crashing. You've worked hard all year, and when the harvest comes in, supply floods the market and the price collapses. So what do you do?

You'd think: can I lock in the future selling price ahead of time?

See? One person fears the future will be too expensive. The other fears it will be too cheap.

One wants to buy in advance. The other wants to sell in advance.

As soon as they agree on that price ahead of time, a futures contract is born.

So futures started out very simply. They weren't for speculation. They were for:

Giving the future a price first.

How important is that?

Extremely important.

Because what the real economy fears most isn't earning a little less money. It's having no way to plan for the future at all.

If an airline doesn't know whether oil prices will spike, how does it set ticket prices? If a bakery doesn't know whether wheat will surge, how can it sign next year's supply contracts? If a metals plant doesn't know how copper, nickel, or aluminum will move next year, how can it accept long-term orders?

So you'll find that the greatest thing about futures markets has never been "making people rich." It's this:

They let society bundle up part of the uncertainty about the future, price it in advance, and transfer it ahead of time.

That is their real civilizational value.

II. The trouble is, once humans can trade the future, they'll inevitably start amplifying it

Up to this point, futures sound pretty righteous, right?

Very reasonable. Very down-to-earth. Very productive.

But here's the problem: anything that can be traded will inevitably attract people who aren't satisfied with using it just to hedge. They start thinking:

Can I use this to make even more money?

That's close to an iron law of financial history.

At first, people used it to lock in prices. Then they realized:

Hey, I don't actually need to own the goods. I don't need to make delivery. As long as I can judge where future prices are heading, I can place my bets in advance.

See, that's when things start to change.

Futures slowly transform from a "risk transfer tool between producers and buyers" into:

A hedging tool
An arbitrage tool
A speculation tool
A leverage tool
A gambling tool

It's no longer just "an agreement between a farmer and a factory." It becomes a vast arena for betting on future prices.

And once you reach that point, the most seductive and the most dangerous qualities of futures show up at the same time:

It's very capital-efficient

You don't need to pay the full value of the goods. You only need to post a portion as margin.

It's very efficient

A small move in price gets magnified into your profit or loss very quickly.

It's very transparent

The market quotes you a price every single day.

It's brutally unforgiving

Because if the price moves against you even a little, you may be asked to top up your margin right away. If you can't, sorry, your position gets closed out.

So what are futures most like?

They're like a highly advanced expressway system. They were built to make freight more efficient and keep society running smoothly. But once cars go faster, traffic gets heavier, and drivers get overexcited with no strict rules or brakes, the road turns into a pile-up.

Futures are the same.

They aren't evil by nature. They're just too fast. Too sensitive. Too good at amplifying.

And anything that magnifies "uncertainty about the future" many times over ultimately forces you to face one fact:

What's truly terrifying about financial markets isn't that prices are wrong. It's that you run out of money before prices correct.

III. Why do futures drive people crazy? Because what they trade isn't "right or wrong" but "can you survive until the end?"

This point is crucial.

Many people understand markets as:

I'm bullish, because I think it should go up
You're bearish, because you think it should go down
Whoever's right in the end makes money

That sounds fine. But in futures, it's only half the story.

The brutal part of the futures market is this:

Being right doesn't mean you'll survive long enough to get paid.

This logic is extremely important.

Say you're short. You think this asset is overvalued and will eventually fall. Over the long run, you may be completely correct.

But if it rallies sharply before it actually falls, and you're using high leverage, you may get forcibly liquidated by the exchange "before the truth arrives."

By the time it really falls, you're already gone from the table.

That's why the scariest thing in futures isn't "being wrong." It's this:

Even if you're right, you can still die along the way.

So in many cases, the futures market isn't really a contest of logic. It's a contest of:

Capital
Leverage
Liquidity
Margin
The structure of counterparties
Delivery constraints
And whether you can hang on until that final moment

Put simply:

Futures are not about who understands the future better. Very often, they're about who doesn't blow up before the future actually arrives.

That's why so many classic futures battles look so much like wars.

Because the cruelest thing in war isn't "who has a point." It's "who can survive to the last round."

IV. The 327 Treasury Bond Futures: 8 minutes, how did they knock a giant to its knees?

There's a famous story in Chinese financial history called the 327 Incident.

Why is it so famous?

Because it showcased nearly every dangerous element of futures all at once:

High leverage
Giants betting against each other
Disagreement over fundamentals
Short squeezes
Market manipulation
The exchange forcibly voiding trades
A financial institution nearly getting wiped out
The regulatory system being forced to reshape itself

Let's start with the background.

In 1995, there was a national bond futures contract code-named 327. On the surface, it was just an ordinary government bond futures contract. But the problem was that the Ministry of Finance had a policy of inflation-linked compensation for government bonds, and nobody in the market fully knew how big that compensation would be.

That meant a big gap in expectations sat underneath the price.

Some people thought the compensation would be generous, so bonds would be worth more and prices should rise. Others thought the compensation had already peaked, the outlook wasn't so rosy, and prices should fall.

So bulls and bears started betting against each other.

On the long side was mainly China Economic Development Trust (中经开). On the short side was Wanguo Securities together with Liaoning Guofa.

At first, it still looked like a normal battle between big institutions. Wanguo believed it was right, so it kept adding to its short position. From 400,000 contracts, to 800,000, to 1.4 million.

You can see where the danger comes in.

If you only express a view with a small position, you can accept it if you're wrong. But once your position is big enough that it starts tying the company's fate to the outcome, you're no longer betting on a view. You're pledging the entire balance sheet of your institution to back your opinion.

Then what happened next is the classic death spiral of futures:

Fundamentals turn against you
Prices start moving the opposite way
Your paper losses grow
You refuse to admit defeat and keep adding to your position
Your position gets bigger, and your margin for error gets smaller
One more small price move is enough to cause serious damage

Here's a deeply counterintuitive point:

Bond futures prices don't usually move that much. You won't see them swing tens of percentage points in a day the way Bitcoin can. But at the time, people were using 40x leverage.

So the price move wasn't simply "a dollar up." For the short side, a one-dollar rise could be enough to make the company throw up blood.

Finally came February 23, 1995, the decisive day.

Wanguo found itself cornered. Closing its positions normally would mean admitting defeat. And if it admitted defeat, prices might be pushed even higher. So it took one of the most reckless steps in financial history:

In the 8 minutes before the close, it dumped a massive amount of short positions.

It wasn't trying to trade normally anymore. It was betting that:

If it could push the price down first
Break the margin chain of its opponents
Squeeze the longs
Force them to be liquidated
Then it could slip out, or even turn the tables

This was no longer "price discovery." It was naked capital combat.

In those 8 minutes, Wanguo dumped an astronomical number of short contracts. The price was driven down from 151.3 all the way to 147.4. If the settlement had been based on that price, Wanguo would have almost pulled off a comeback.

But doing that was too obviously against the rules. That same evening, the exchange announced:

All the abnormal trades in those last 8 minutes are void.

The closing price was restored. In one moment, Wanguo went from nearly turning the tables to facing huge losses.

That wasn't just painful. It was a knockout.

Wanguo later came close to bankruptcy, the person in charge was sentenced to prison, and China's national bond futures pilot was suspended for nearly 20 years.

What's the most frightening part of this story?

It isn't that one side was evil. It's that the natural amplifying mechanism of futures, once it meets:

Giants adding leverage
Disagreement over fundamentals
Expiry approaching
A fragile counterparty
Games played at the edge of the rules

...quickly upgrades "normal trading" into "you die or I die."

V. The nickel short squeeze: when the market has only 100 milk teas, but someone has sold 1,500 cups

If the 327 incident is the classic of Chinese-style capital bloodletting, then the 2022 nickel short squeeze was a very modern, very dramatic, and very absurd showpiece in the global futures market.

Let's start with a simple version of the logic.

Imagine the market really only has 100 cups of milk tea. But someone has sold futures contracts for 1,500 cups. Do you think that could cause a problem?

In theory, it's usually fine. Most people don't actually need delivery, and many close their positions before expiry. But if something suddenly shocks the market and everyone realizes:

Oh no, there really aren't enough physical goods available for delivery.

Then trouble starts.

In 2022, the Russia-Ukraine war broke out. Russia, a major nickel producer, was hit with sanctions. The market suddenly realized:

Deliverable nickel had become scarcer
Spot inventories were already low
But short positions in futures were enormous

So how would the long-side capital that spotted this opportunity think?

It would say:

You're short a lot, right? Great, then I'll keep buying and push the price up. Either you post more margin, or you close out and admit defeat.

Qingshan Holdings happened to be in exactly that position, and became the most typical and most conspicuous big short.

Qingshan is a giant in the global nickel industry. Its short logic might not have been purely speculative; it may have included hedging. The question isn't what its motives were. The problem is that it was placed in an extremely dangerous spot:

Its short position was huge
Deliverable spot in the market was tiny
Geopolitics suddenly changed supply
Longs smelled opportunity and began concentrating to squeeze

The result: nickel prices went wild.

First they doubled. Then doubled again. Finally, they almost shot up like a rocket.

Think about how frightening that is.

For the short side, rising prices weren't just "being wrong about the view." Margin calls kept coming like a bottomless pit. Billions of dollars in funding pressure didn't mean you were fine just because you had assets on paper. You had to produce the cash right now, immediately.

At that point, the thing a short fears most isn't how much they'll lose in the future. It's this:

I'll run out of cash before tonight.

Eventually, the London Metal Exchange (LME) stepped in, halted trading, and voided many of that day's trades.

That move blew the market apart. Long-side funds were furious, because they believed they had won by the rules, and then the exchange flipped the table.

Meanwhile, on the short side, Qingshan and the financial chain behind it were effectively propped up by the exchange and the system together.

That's why this episode is so classic. It reveals one of the core truths of futures markets:

On the surface, futures trading is a price game. At its core, it's a three-way contest between delivery capability, funding capability, and control over the rules.

Looking only at right and wrong isn't enough. You also need to ask:

Who can withstand margin calls?
Who actually has the goods?
Who gets rescued first by the system when the rules break down?

At that point, this is no longer just a market. It's a power game at the level of financial infrastructure.

VI. Negative oil prices: how did "we'll give you the oil for free and pay you to take it" happen?

Many people, hearing about negative oil prices for the first time, think it's a joke.

How could oil be negative? Give oil away and pay people to take it? Isn't that crazy?

The truth is, it isn't crazy. It's simply the cruel delivery logic of futures markets pushed to the extreme.

Let's start with the most important point:

Crude oil futures aren't abstract numbers. At expiry, they require physical delivery.

In other words, if you hold a long position and don't close it, in theory you really will have to take the oil.

Now the problem: in 2020, when the pandemic broke out, global crude oil demand fell off a cliff. Planes stopped flying. Cars stopped driving. Large parts of industrial activity shut down.

Demand collapsed. Supply couldn't be cut immediately. So the scariest problem in the entire market wasn't that "oil is too cheap." It was:

There was nowhere to put the oil.

Storage tanks are finite. Transportation is finite. Delivery locations are finite. You can't just say, "Send me a truckload of oil, and I'll dump it in a river." That's illegal.

Meanwhile, the market was full of longs entering through various "paper oil," "wealth management products," and "agent channels." Many of these people weren't there to take delivery. They just wanted to buy the dip in oil prices.

The problem is, they could buy long, but they had absolutely no ability to take delivery.

So, as expiry approached, the market saw the most terrifying scene:

Longs had to sell
Because if they didn't, they'd have to take the oil
But no one in the market wanted to take it
Because even if they did, they had nowhere to put it

You can see how the price logic changes at that point.

It's no longer "what is oil worth?" It becomes:

Who is willing to take this headache off my hands?

So negative oil prices don't mean that oil itself has no value. They mean:

At that moment, in that place, under that delivery structure, "getting rid of this long position" was worth more than the oil itself.

What's that like?

It's like suddenly finding dozens of tons of ice cream in your home. You thought you'd hit the jackpot. Then you realize you don't have a freezer, no logistics, no buyers, and if you don't get rid of it, it all melts. At that point, you're not thinking "how much is this ice cream worth?" You're thinking "who will hurry up and haul it away for me? I'll even pay them."

That's essentially the logic of negative oil prices, magnified to textbook level in the world's most important crude oil futures market.

VII. So are futures a casino, or a risk management tool?

By this point, you might easily conclude:

Futures are so sinister. So ruthless. So much like a casino.

But if you stop there, you've misunderstood them.

Because the most classic, most thrilling, most life-or-death battles are precisely the most extreme, most out-of-control, and most easily spread side of futures.

They're gripping because they look like accidents.

And accidents are the most attention-grabbing. But you can't say the essence of the road system is crashing just because you watch car accident news every day.

The same goes for futures markets.

The part that's truly huge, truly everyday, and truly supports the real economy isn't these epic crashes. It's countless risk transfers that don't seem that exciting, but are extremely important.

For example:

Airlines locking in fuel prices
Farmers locking in grain prices
Metals plants locking in raw material costs
Insurers and asset managers using government bond futures to adjust duration risk
Arbitrage funds trading the basis between spot and futures
Speculative capital pushing mispricing back to reasonable levels

If you look at a single trade, futures are obviously zero-sum. You win, I lose. I win, you lose.

But if you look at society as a whole, futures aren't just zero-sum.

Because they transfer risk away from "people who can't afford to bear it" and onto "people who are more willing, more professional, and better able to bear it."

A bakery doesn't want to bear the risk of violent swings in wheat prices. So it can sell that risk through futures. Who takes it? Maybe a fund, maybe an arbitrageur, maybe someone willing to bet on prices.

Are these people just speculating?

To some extent, yes. But from a systemic perspective, they're also doing something very important:

They're bearing and pricing risk on behalf of the entire market.

That's the subtle thing about futures.

On one hand, they're very much like a casino, because they really do amplify human nature, leverage, fear, and greed to the point of distortion. But on the other hand, they're a huge risk market that the modern economy can't do without.

You can't live without them. Yet once you have them, you must be extremely afraid of them.

Conclusion: futures are most like high-voltage electricity, not a casino

So if you ask me today what futures really are,

I won't say they're a casino. Nor will I say they're "just an ordinary financial tool."

I'd rather compare them to:

High-voltage electricity.

Why?

Because high-voltage electricity isn't evil in itself. It's extremely efficient, extremely necessary, and extremely powerful. Without it, much of modern industrial society simply couldn't run.

But the danger is real too.

If you stay too far away from it, society goes dark. If you get too close, you'll be electrocuted on the spot.

So a truly mature market is never one that says "no high-voltage electricity." It's one that:

Admits it's powerful
Admits it's dangerous
Equips it with strict rules, clearing systems, risk controls, delivery mechanisms, and regulatory systems

Because the scariest thing about futures isn't that someone made a lot of money, or that someone lost a lot of money. The scariest thing is when a tool designed to manage risk ends up becoming the risk itself.

And the most classic, bloodiest, most dramatic moments in financial history all remind us of the same thing:

Humans invented futures to distribute the uncertainty of the future in advance. But once you forget that what they face is fundamentally "uncertainty," they will turn around and teach you a lesson in the most extreme way possible.

So when you look back at the famous squeezes, forced liquidations, negative oil prices, the nickel war, and the 327 incident, you'll see they're about far more than finance.

They're about a bigger question:

When a system is powerful enough to manage risk, it is also powerful enough to create disaster.

That is the real epic of futures.

WWII Stories: The Computing War Eighty Years Ago (Part 1)

2026-03-11T00:00:00Z

If you've seen The Imitation Game, you probably remember this picture.

England. Bletchley Park. A group of eccentrics. A young mathematician named Turing. A huge machine. And a line that has since been turned into almost a myth:

Breaking Enigma changed World War II.

That's true, of course. The problem is that it only tells the second half of the story.

Because before Turing walked into Bletchley Park, before the more famous British "bomb" started humming, before Britain turned codebreaking into a full national project, someone had already pushed that door open a crack.

And it wasn't the British.

It was the Poles.

The same Poland that in the autumn of 1939 was caught between Germany and the Soviet Union, torn apart within a few weeks, and often written about since as a tragic supporting character.

But if we're talking not about the front lines but about codebreaking, intelligence and information warfare, Poland was no supporting character at all. It was the lead of the prequel.

───

I. What the Germans really had to fear was more than tanks

Most people, when they talk about WWII, think of blitzkrieg, tanks, and how fast the German army advanced.

Those things are all true. But if you look at it from the Allied point of view at the time, you realize the most frightening thing about the Germans may not have been steel or aircraft. It was their communication system.

Simply put, in the most dangerous years of the war, the German advantage was that they believed every word they sent could not be understood by anyone else.

That was a disaster waiting to happen.

Because in war, the most expensive thing has never been a tank or an aircraft. The most expensive thing is a wrong decision.

Take the Battle of the Atlantic. Britain, an island nation, survived on sea transport. If German submarines worked out the routes of your convoys, you didn't just lose a few ships. You lost the nation's lifeline.

Take the North African front. Why did Rommel struggle more and more as the fighting went on? For a simple reason: fuel shortages. But then, why did his fuel ships keep running into the British "by coincidence"? Was it really just luck for the British navy? Of course not.

Then there's the Normandy landing. Why did the Allies dare to gamble so much? How could they be sure the Germans had been misled by false intelligence? It wasn't because they were gambling. It was because they had really started to see the German cards.

So breaking Enigma looked on the surface like cryptography, but in reality it was a fight over the most expensive thing in war:

Information advantage.

Whoever can read the enemy's communications avoids the most costly mistakes. And whoever avoids mistakes can put money, ships, fuel, time and troops where they matter most.

So what's truly terrifying about Enigma isn't that "it was a cipher machine." It's that it was the lock on the outside of the Third Reich's nervous system.

───

II. Why Enigma was so terrifying

Many people today know the name Enigma. But if you think of it only as "an old cipher machine," you're underestimating it.

What made it truly frightening wasn't just its complexity. It was how cleverly it was designed.

On the surface, it was very simple. You pressed a letter, say A, and the machine lit up another letter, say X. You wrote down X and sent it. On the other end, a machine set up the same way would take X and turn it back into A.

Sounds like a children's cipher game, right? The problem is that it doesn't work that way inside.

Inside, there were rotors, a reflector, and a plugboard. Every time you pressed a key, the internal structure moved. In other words, the same input did not always produce the same output.

Press A today and you get X. Press A a second later and it may no longer be X.

What does that mean?

It means Enigma wasn't a lock. It was a lock that changed its own combination every time you pressed a key.

Even more vicious, the Germans had a very harsh rule:

Every day at midnight, the whole army changed its settings.

That created a truly hopeless situation.

You spent a lot of effort finally finding some pattern. At twelve o'clock, it's all void. Yesterday's clues couldn't be carried over, and yesterday's work couldn't be reused. The next morning, you woke up to face a fresh maze.

So the most terrifying thing about Enigma wasn't just "too many combinations." It was that it made traditional codebreaking methods almost completely useless.

Guessing words wasn't enough. Language intuition wasn't enough. Yesterday's experience wasn't enough either.

It didn't just give you a headache. It drove you to despair.

───

III. The English and French did get clues first, but still couldn't break it

Here's a particularly dramatic detail.

The first to get hold of material related to the German cipher system was not Poland. French intelligence had obtained important information about Enigma through spies quite early on.

The British and French also didn't hold back. They brought in many of the smartest people they could find: cryptographers, linguists, classicists, and even people who were very good at spotting patterns.

On paper, that lineup was already strong. But after studying it for a long time, they reached a very discouraging conclusion:

It's too hard. It's almost impossible.

What was the problem?

It wasn't that they weren't clever. It was that they were looking at the wrong problem.

They treated Enigma as a language problem. They tried to find words in the letters, sentences in the words, and patterns in the sentences.

But the person who first saw through the problem realized something crucial:

This isn't a language problem. It's a math problem.

That person was the Polish mathematician Marian Rejewski.

───

IV. Why Poland was the one to see it first

There's a cruel reality behind this too.

The British and French could afford to study failure. Poland couldn't.

Britain was across the Channel. France, however weak, still had a buffer. They were certainly afraid of Germany, but they weren't yet at the point where "if we can't crack this today, we'll be conquered tomorrow."

Poland was different. Poland sat right on Germany's border. For Poland, not understanding what the Germans were saying was not an academic regret. It was a matter of life and death.

Poland had no right to trial and error. It couldn't afford to spend too long on the wrong method, the way Britain and France could.

So the Polish cipher bureau made a crucial decision:

If the linguists can't do it, switch to mathematicians.

So they recruited three young men:

Marian Rejewski • Jerzy Różycki • Henryk Zygalski

These three would later become the names that most deserve to be remembered from the prehistory of the codebreaking war.

───

V. Rejewski's real greatness was admitting everyone had been asking the wrong question

What made Rejewski remarkable was not that he was "a little smarter." It was that he made a crucial reversal first.

Others saw gibberish. He saw structure.

Others were still guessing words. He had already started looking at the permutations, cycles and mappings between letters.

To him, Enigma was not German, not a message, not a sentence. It was a system that could be described mathematically.

That step was enormously important.

Because many problems that look unsolvable aren't without answers. They just have answers that you can't reach because you're asking the wrong questions.

The experts from Britain and France were still "reading letters." Rejewski had started "reading the machine."

He was the first to realize:

Enigma couldn't be solved by people slowly guessing. It had to be modeled, reverse-engineered, and handled by machines.

From that moment on, everything changed.

───

VI. Before Turing's "Bombe," Poland had already built its own "bomb"

Later, when people talk about The Imitation Game, they remember Turing and Britain's "Bombe."

But earlier, the Poles had already built their own Bomba.

It wasn't a general-purpose computer by today's standards. But it was already very close to what we'd understand as a special-purpose computing machine.

It existed for one purpose:

To hand off the combinations that a human brain could never test by hand to a machine.

This matters a great deal.

Because it shows the Poles didn't just understand the problem "in theory." They had already started turning it into an engineering problem.

In other words, the Poles not only understood the question first. They also realized early on:

A problem of this scale can't be solved by intelligence alone. You need machines too.

Put simply, they had already caught the scent of the computing war.

And this is exactly what makes the Polish story so tragic. They understood it too early, but their country was not strong enough to carry the path all the way through.

───

VII. The real cruelty: they were close to success, while the country was close to collapse

If the story stopped here, it would already be legendary enough. But the most painful part of Poland's history is what came afterward.

In 1938 and 1939, Germany upgraded Enigma. The complexity went up.

Poland naturally tried to keep up. The problem wasn't that they couldn't. The problem was that they couldn't sustain it.

It's like a small team that got the algorithm right and bet on the right technical path, but when it came to the stage of building machines, scaling up and investing continuously, the cash flow ran out.

They had already found the answer. But the country was almost gone.

That is the most painful part of this story. It wasn't a lack of skill. It was a lack of national strength.

02-07 Gathering of Heroes: Career Paths and Industry Insights from Tech Professionals

2026-02-07T00:00:00Z

The Randomness of Finance Careers

Careers in finance are full of chance. One friend shared a dramatic case: a woman who used to be a car show model ended up as a fixed-income manager at a bank. Her path went like this: from car show model to bond sales, and because she was attractive and had a mentor who noticed her, she got into the bond trading department of a bank and eventually became a fixed-income manager. A career path like this shows just how much opportunity matters in finance.

Thinking from Traditional Trading to Quantitative Investing

When the conversation turned to investing, someone offered a sharp insight: money will become less and less valuable over time. That judgment reflects a deep understanding of the modern financial system. As for investment strategy, one person said they're not really a trader. They make decisions mostly based on understanding a project's performance.

Notably, even private equity funds managing tens of billions of yuan have been losing ground to quantitative trading in recent years. In 2024, for example, some fund manager friends, such as the boss of Black Wing Asset, who manages 20 billion yuan, started using large language models for quantitative trading. Their interns even published a paper in a top journal, applying AI models to quantitative strategies, and the returns far exceeded traditional discretionary trading.

The Truly Scarce Resources: Compute and Energy

From this angle, human traders are no longer the core competitive advantage. What's truly valuable? Compute, energy, and electricity. In the future, money may not even be the scarcest resource.

Based on this thinking, as early as around 2020, some people began investing in compute-related assets such as GPUs and HBM. This investment logic reflects an important methodology: keep reasoning forward, and work out what is truly scarce and what only looks scarce.

Investment Opportunities from a US-China Competition Perspective

Looking back, the investment logic at the time was somewhat naive, but it did catch some key trends. After Trump was elected in 2018, the US-China trade war escalated, and China's semiconductor industry faced enormous pressure. The thinking at the time was from China's perspective: the core of the US-China rivalry has shifted to AI competition, and the US is far ahead in crypto financial systems and AI.

For China to win, it might need to take a "brute-force" route: building data infrastructure. Another idea was that, facing the contradiction of a shrinking population but rising productivity, one could build a "double-deck elevated highway" of a digital world alongside the physical world, using virtual worlds to solve the problem of excess productivity and insufficient labor demand.

Musk's Grand Loop

By comparison, Musk came up with a much grander solution: Mars. Instead of creating a virtual demand, he opened up an entirely new scenario of real demand. Musk's Optimus V3 robot is said to be astonishing, and people in Silicon Valley who've seen it say Tesla stock is worth buying. More importantly, V3 uses only silicon carbide devices.

Musk's whole loop of thinking goes like this: send robots to Mars to transform the planet. For compute, deploy it in space, where there's neither a heat dissipation problem nor light pollution. For energy, solve it with space solar power. Finally, use robots to terraform Mars. This loop is far more macro than the purely digital-world vision. At the time it was hard to see that far, and only now are we slowly beginning to understand it.

The Underlying Logic of Infrastructure Investment

The investment logic at the time was: to counter America's AI advantage, China could only take the infrastructure route. Infrastructure means generating electricity. With electricity you get compute, with compute you can counter the models, and then you rely on China's huge data advantage.

Based on this logic, they took a server apart completely and found that GPUs and storage were the two largest components, followed by communication connection chips and various interface chips. So they bought all of these semiconductor targets. Then they added the energy side: photovoltaics, lithium batteries, energy storage including liquid energy storage, all configured. The judgment at the time may not have been fully mature, but these investments have performed quite well so far.

Investment Discussion

Over dinner, everyone enjoyed their food while discussing recent investments. Someone mentioned that the market had dropped sharply a couple of days earlier, causing considerable losses, and that they were now trying to buy the dip. The conversation turned to a specific investment: TowerJazz.

The Investment Value of TowerJazz

Why pay attention to TowerJazz? Because it has scale advantages. Currently, the photomask process for all CPUs needs silicon photonics. Previously it used EML, but now it has to use silicon photonics. And all silicon photonics chips, no matter which design vendor, have to be fabricated at TowerJazz, because it's the only one with that capability. This is a classic case of a monopoly advantage in a "chokepoint" technology.

When someone asked about the code, the speaker said it wasn't convenient to share publicly, because it was already known to too many people. But the logic itself is worth watching, so everyone can do some homework first. The whole dinner had a relaxed atmosphere while people exchanged these deep investment insights.

Crazy Saturday Offline: Product and Investment Philosophy in the AI Era

2026-02-07T00:00:00Z

Opening: What Should We Care About in the AI Era?

It's a Saturday afternoon. In a café in Shanghai, a group of young people sits together, ready to start a conversation about AI, investing, and independent development. Some are developers who just moved from the securities industry to a big tech company. Some are college seniors trying to build their first iOS app with AI tools. And some are product managers and founders who are curious about AI agent tools like OpenCloud.

Over three-plus hours, the talk starts with the philosophical questions of AI safety, moves through the underlying logic of US stock investing, and finally lands on a real case of indie iOS development. It's not just a tech talk. It feels more like a collective exploration of how we should think and act in an era that changes so fast.

Part 1: Philosophical Reflections on OpenCloud and AI Safety

Speaker: Steven

Time: 00:34:05 - 00:42:48

Starting from a Real Case

Steven turned on the projector and first shared a real case he had just seen on social media:

"I want to start with privacy and security. Earlier, Eric mentioned that if you deploy an on-device model yourself, that would solve OpenCloud's security problems. But I don't think that's really true."

He described a situation that shocked everyone: "I just saw someone on social media who was using OpenCloud. He uploaded his entire AI configuration, including the API token and all sorts of encryption settings, straight to GitHub, publicly. His API token leaked, just like that."

Someone in the room gasped. Steven went on: "So you see, even if the model runs on-device, you still can't fully control what it does. You can repeatedly write 'don't upload sensitive information' in every skill doc and every system prompt, but the generative nature of AI models means their memory can get tangled, and they can do all kinds of uncontrollable things."

The Uncontrollability of Technology and the Barrier to Entry

Steven then explained the contradiction in tools like OpenCloud:

"OpenCloud is actually expensive and hard to use. Even if you can deploy it on Docker or a VPS, if you do, you lose its most powerful ability, which is operating your browser, handling your files, and sending WeChat messages for you. But if you deploy it on your own computer, it could delete your files or share your private information at any moment."

"This is because, at the code level and given how uncontrollable AI generative models are, there's no way to completely rule this out at this early stage. That's why everyone is buying Mac Minis." (Laughter in the room.)

Do We Really Care About Security?

But the discussion quickly turned to a deeper question. Steven paused and said:

"But you know what? I think Chinese people (老中) don't really care that much about data security. That's the first point. The second point: if today were the last day you were alive in this world, and you were asking whether this product is good, you wouldn't think about security at all. You'd only think about what it can actually do."

This hypothetical made the room go quiet for a few seconds. Steven continued:

"Based on these two points, I think the way we're discussing this, as a group like us, carries a huge elitist bias. Because you could do something, the kind of thing Indian founders might do: use a crawler to scrape all of OpenCloud's GitHub issues, find the common problems clustered in those issues, and look for market opportunities or build a better project."

"But doing that... how should I put it? I wouldn't call it stupid, but it isn't that smart. The real problem is this: the people who know about OpenCloud but aren't using it, their problems are the real problems. The people filing issues on GitHub are only a very small part of the picture."

The Market Education Value of OpenCloud

Steven raised an interesting point:

"OpenCloud now has 170K stars. Out of those 170K stars, something might emerge in April or August of 2026 that's better than it, or that fixes some of its flaws. But the bigger question is why half of the people are still unaware of it. (He meant the potential user market.)"

"So I think it contributes a lot. Just like how it helped everyone order bubble tea earlier, it's doing market education, showing people what the future looks like. It may only be a transitional state, but more products will grow out of it at some stage in the future and solve the concerns people have today."

OpenCloud Still Fundamentally Depends on the Model

Steven offered a sharp judgment:

"How amazing is it, really? If you swap it onto a really terrible model, say Kimi 1.5, or the models from early 2024, you'll find it isn't magical at all. Its ability to complete tasks might not even match the basic research-level thinking mode OpenAI had back then."

He used a metaphor: "So OpenCloud is most likely just a shell around the model, like a car's body. Whether it's impressive still depends on its battery and its engine, meaning the underlying model."

Optimism from a Long-Term View

Near the end of the discussion, Steven reached a relatively optimistic conclusion:

"Most of the problems we worry about now probably won't exist in the future. Data issues will persist, that's certain. But I don't think people will develop that deep a sense of security awareness. The people in this room might, but the people outside it won't necessarily. Our elders and our children probably won't."

"And besides, fully controlling AI behavior is also very hard. We want the model, our 'kids,' to follow exactly the instructions I give them, but they can make mistakes, and they'll emerge. They'll do things we didn't expect."

He paused, then stated a more radical view:

"Many parents don't want their kids to play games. They may have a strong desire for control. But that social attitude will shift to some degree over time. I think we have to allow for this deviation. Even from an accelerationist standpoint, we may have to accept that we ourselves will no longer be the center of this society."

"The relationship between humans and AI may slowly move from today's 'ten to zero' to 'eight to two,' or even 'nine to one.' That slider will keep moving. I think this is a reality we have to accept."

Part 2: OpenCloud's Productization and Its Capability Boundaries

Discussion time: 00:40:06 - 00:47:25

Participants: Steven, community members

The Core Problem of Productization

A classmate working in product and business raised a very practical question:

"Many of us who work in product and business care a lot about where OpenCloud's capability boundaries lie. If it's truly productized, how will ordinary people use it?"

He described a concrete pain point: "When we chat with ChatGPT or Claude, we have to explicitly say 'use such-and-such skill for me.' But ordinary users express themselves as 'I want to get something done,' and they expect the AI to call the right skills on its own. So I'm wondering whether it has really done a good job on intent recognition. That's the first problem."

"The second problem is that even for very simple questions, it still tries to call skills. There's a lot of thinking involved, and the cost of those calls adds up, eating a lot of tokens. For that problem, it's not the optimal path. It might even be worse than just using a large model directly."

He finally asked: "Is there a way to optimize its way of thinking, or its calling path, so it makes real progress on token consumption and intent recognition? That way, we could use it in general, everyday scenarios too."

Stretch the Timeline and None of This Matters

After hearing this, Steven gave an answer that surprised many people:

"I think that's just how it looks from where you're standing right now. If you stretch the timeline infinitely, you'll find that none of this matters."

He gave an example: "Think back to 2023, the GPT-3 era, when the context window was only a few dozen tokens. I forget the exact number, but it was very small. Looking at it now, it doesn't seem like such a big deal. Even whether RAG (retrieval-augmented generation) needs to exist has become unimportant."

"What I mean is that as capabilities keep being unlocked, you can just analyze a big chunk all at once without worrying about token consumption, which is a very minor detail. So I still think it depends on the car engine and the car battery, using the metaphor I mentioned, and on their iteration. As for that framework, which is like the car's shell, it's... not that important."

"The framework-level problems we have right now matter less than model iteration."

A Three-Factor Framework in Practice

Another participant added that they had recently been testing OpenCloud's limits and had summed up three factors:

First, the choice of model.

"A friend of mine told me, 'I installed OpenCloud too, but it feels dumb and hard to use.' So I asked which model he was using, and he said Pinyin, a weak model. Meanwhile, I've been using Claude 4.5 the whole time, now 4.6, and the experience is completely different. The model is the most important thing."

Second, how clean the context is.

"OpenCloud's design seems to make you forget that 'context' is even a concept. Unlike ChatGPT or other tools, there's no explicit 'new chat' button. But given what current models can do, starting a new conversation is still very important."

"In OpenCloud, I personally use two commands very often: neo (reset the context) and C (compress the context). neo flips the page and resets the context. C compresses the context and rewrites it. This helps it understand your next task better. If you're using very little of the context window, your next command will run a little better."

Third, prompt design.

"Prompt design has three layers:

System prompt: defines what kind of assistant the AI is and what role it plays. It's loaded in every conversation.
User prompt: the user's command, which decisively defines the task and the strategy and approach to take.
Memory: not necessarily loaded every time. Memory is a database the model chooses whether to search."

"Together, these three pieces are what bring OpenCloud to the state you want and make it solve problems in a suitable way. So these three are quite important."

Part 3: Will AI Tools Make Hardware More Expensive?

Discussion time: 00:45:18 - 00:52:17

An Interesting Economics Question

Someone raised a concern about the hardware market:

"I'd like to ask a question that's more about consumers. Do you think tools like OpenCloud could cause the kind of price swings we're seeing with memory today, for Mac Minis or other machines that host this service? If the hardware price swings caused by AI really spread into the personal computer market, what would that look like?"

His logic was: "Say my SaaS product suddenly sells really well. Won't the hardware it runs on go up in price too?"

Steven offered a figure that surprised people:

"I think this is a very interesting question. But if you analyze it through economics, it doesn't mean that just because something sells well, its price goes up."

"After that three-hour session (meaning a certain AI product) went viral, the resources on Google Cloud and Amazon Cloud all tilted toward it. After that tilt, there's something in economics called the 'concentration effect,' and it won't become more expensive."

He explained further: "Why? Because what gets the most expensive is one person's demand. The demand from 100 people, 10,000 people, or 100,000 people actually goes down. Of course, if the demand you're meeting has to go through chips, computing power, or rare elements constrained by the laws of physics, then it could be expensive. But if it's something current capacity can already supply, it's not necessarily expensive, and it may keep getting cheaper."

Then he laid out a key premise:

"Personal market demand, compared with data center and office business demand, may not even reach 10%. That's the simple premise. Given that premise, personal demand only affects the retail price ceiling. In other words, what price you can buy at depends on which channels and sales outlets you can reach."

"For example, you might buy on JD.com, but JD's supply is limited, or Apple's share of sales is limited, so within the existing pool of resources, the price could go up. But don't worry, supply will naturally catch up. If demand isn't especially urgent, then with our current supply capacity, you can get as much as you need."

The Truth About Rising Memory Prices

Someone pressed further: "Then why are memory prices going up?"

Steven gave a very clear answer:

"Memory prices are going up because Micron switched its capacity, the capacity that used to serve personal PCs, entirely over to data centers and to big customers like NVIDIA."

"So the reason manufacturers don't want to expand is that they have to judge whether this demand is short-term or long-term. That's why there will be shortages in the short run. But don't worry. Given Apple's expansion capability, the supply capacity of all Apple devices is tied to China's industrial capacity, so it can supply whatever it wants. At most it affects some channel prices. As for retail prices, you might wait two weeks at most."

OpenCloud's Barrier to Entry Is Still High

Steven summed up at the end:

"And honestly, OpenCloud's barrier to entry is still very high. You can't get everything done with a single sentence. This market is nowhere near the level of 'everyone uses it.' So I think the impact on hardware demand is negligible."

"The bigger forces are still changes in enterprise and data center demand. The personal market? If more people knew about this thing... but honestly, our circle is very 'involuted' (卷). In China, the undergraduate education rate is only 4 to 5 percent, and the quality of that is even lower. I don't think many people have this need."

He offered an analogy: "Today everyone is grabbing red envelopes, fighting over 1,000 red envelopes worth 2.5 yuan each, and more people will go for those. But most people really don't... If you affect the existing needs of most people, those needs aren't as rigid as the need to grab train tickets during Spring Festival travel."

The room erupted in laughter.

Part 4: Surfing the Money, the Investment Philosophy of a Former Coder-Turned-Trader

Speaker: Evan (Ailun)

Time: 00:53:49 - 01:47:15

Opening and Self-Introduction

Steven handed the mic to Evan: "I think about 90% of the topics we discussed earlier are hard to turn into real execution, hard to turn into action in the real world. But the stock market is a simple thing where you can make money through cognition. If you know the answer to a question before 51% of people do, you can make money. I think Evan can answer this question. Please welcome Evan, who will share on US stocks."

Evan walked up on stage and adjusted the projector.

"Uh, do you all like this standing style, or do you prefer sitting?" (Laughter in the room.) "Never mind, let's sit. Let me get my setup adjusted..."

He opened his laptop and started screen-sharing. Someone joked, "First-person perspective, immersive experience." Evan laughed: "Right, right, a body camera."

After the laughter, he formally began:

"Alright, let me start this way. Last time, I gave a brief talk about options. This time it's also going to revolve around the skills we already have and the use of some tools."

"Researching tools is something you can never finish learning. Every new tool comes out and gets updated very fast. You can't rely on tools to make money. This is the conclusion I reached after going through some things. The logic of making money and the thinking behind resource allocation are actually far more important."

"We need to know what the market is. When you're at the table, you need to be clear about whether you're a player or the one being played. So understanding the situation and finding ways to profit is quite important."

Personal Background

"Let me introduce myself first. I graduated with a bachelor's degree in 2019. When I graduated, I was quite interested in Futu (a brokerage) and started teaching myself coding in my junior year. Then I went through some ups and downs. Well, not really ups and downs. I wanted to join Futu, which is the US brokerage I'm using now, but it didn't work out. Then I went to Huatai Securities as an intern, and worked there for my first year."

"After that, I realized the brokerage business was not what I had imagined, and I thought I might as well be writing code. Luckily I had a coding background, so I switched careers seamlessly and joined ByteDance. Then I went to Horizon Robotics, and after leaving Horizon, I'm now at a certain factory. I won't say which factory." (Laughter in the room.)

"That's my work history. But my main 'core business' is actually writing articles. I just switch topics. At the brokerage I wrote investment articles, and when I was on US stocks, coding, and at Horizon, I wrote technical articles. So I've written quite a lot."

"My main job is really content output, and I don't know how good it is. So I won't start by going over the most basic data. That's all very natural. And since AI came along, tools have become less of the main thing. I still want to emphasize the logic and motivation behind what we do, and how we actually play this game. That matters a lot."

Interaction: Everyone's Investing Experience

Evan started an interactive segment:

"First, I'd like to ask: how many of you have invested in stocks before? Alipay funds count too."

About half the room raised their hands.

"OK, that's the first layer. Now, not counting actual trading for the moment, how many of you know about US stocks, know the paths into US stocks, and understand what it takes to get there?"

A few more hands went up.

"Wow... those are all non-mainstream people, haha. Then, how many of you have actually traded, placed your first real order, whether or not you made money?"

"One, two, three... one, two, three..." he counted. "Good, I'm satisfied. OK, next: how many of you have experience with financial derivatives? Things like futures, options, leverage, or contracts?"

A few people raised their hands.

Evan laughed: "Oh my, very good, very good... you're all impressive! OK, fine. Then today I'll still sort things out from some more basic concepts. I also hope... it's not really a lecture, just a sharing session."

"I believe that no matter what industry you're in, AI or business or anything else, you're interested in money. So the first thing we're going to talk about today is getting money clear. How can we find growth opportunities, and how can we use some AI to strengthen this understanding."

He paused: "Before that, would anyone be willing to share a tip for making money? It doesn't have to be investing. Anything goes, science is fine, the reverse approach is fine too."

"Making money in reverse?" (Laughter.) "Yes, it's really just a matter of forward and reverse. Either works."

"Who wants to go first? Feel free to come up."

A Story About a Liquidation

After a few seconds, one audience member volunteered:

"I'll go. This is an idea I had, not a current one; I started thinking about it in high school. Around 2019, which was a huge bull market. I opened a 3x leveraged position, starting from roughly... when BTC was around 30,000 to 40,000 dollars. Then it kept going up, and by the time it reached its next peak, my assets had multiplied several times."

"But then I didn't buy the dip. At the time, I thought the 'BTC will hit 100,000' that many people were hyping would happen. And it did eventually hit 100,000, but not at that moment. I just didn't hold on, and then it kept falling day after day, and in the end I got liquidated. What a painful lesson."

"After that, I stopped touching forex-related investments and started studying US stocks."

Evan nodded: "Yes, that's how it is. Everyone, please manage your risk, haha. Anyone else? OK, I think maybe I'll share a successful case instead."

He summed up with a smile: "Actually, to succeed you first have to go a little crazy. It's the same logic. You definitely have to go through some setbacks and some beatings before you know what growth is and what patterns are. You can't teach this. You can't teach someone to do it. It's better to just actually trade and lose a hundred thousand or so, right?" (Laughter in the room.)

The Nature of Money: A Resource Dispatch System

"Really, what we call growth or making money, I'd like to return to a most fundamental question: what is money, at its core?"

Evan said: "This isn't some high school politics class. Anyway, money is this thing, and I have a word for it... When I was playing Battlefield (the game), in the scene where you rob a bank, people shout 'Take your vote!' In English, 'vote' doesn't just mean a ballot. It stands for a kind of choice, a kind of right to allocate resources."

"Money isn't just paper notes. At its core, it's a voucher for allocating resources. Once you build that way of thinking, a lot of things become clear."

He went on: "Because once you turn money into a resource dispatch order, I feel it's no longer so mysterious to me. Then I keep asking myself: are the losses and the so-called money we lose actually burned? In the market, we often see news like, 'the other day, some market lost trillions of dollars, and its market cap evaporated.' Did your money really evaporate? Do you think that wealth really vanished into thin air?"

"In fact, it didn't. Because there's a basic judgment: a crash just means someone sold, and someone turned the 'goods,' the stock, into cash. The money all left, so the price of the stock fell. It's really just a transfer. The money wasn't burned. It just moved from one place to another."

"It may have pulled out of tech and software, and gone into real estate, bonds, and many other places. Only: do you know where it went? That's what matters."

"If you're just an ordinary small retail investor, a leek, you see the drop and can only curse a few times: 'It's down again, when will I get out of this?' And then you just wait. But I think we can think a step further. It's really only when you lose a lot that you start to reflect, to rethink."

Investing Is Surfing

Evan introduced a core metaphor:

"If money isn't created out of nothing, and if money has the property of flowing, then what we see as ups and downs is really a model of resource allocation. Money flows. Why do we say the US stock market puts so much emphasis on 'liquidity'? High liquidity means everyone is pouring into that place, into that industry."

"Why do we say finance, the internet, and high-paying industries make money? Because resources are concentrated there. The money isn't sitting in someone's pocket or in a current account. And precisely because of liquidity, precisely because we have reform and opening up and global flows of resources, we get continuous growth and development."

He paused, then said the core metaphor of the day:

"So if you want to make money through investing, in my view right now, it's not all that different from surfing the internet. It's very much like it."

"It's like a topic you see blowing up on Twitter, where everyone's attention is on it. The logic in the stock market is the same. Gold surged before, and silver was doing well before that. That shows everyone is putting their liquidity votes into that place. It represents something."

"And it's just like surfing. When the wave is high, how do you judge whether this wave is truly low or whether it's going to do something else? Your logical judgment of the whole trend, I think, is a process that can positively build a foundation."

"So I now treat investing as surfing, a basic kind of surfing, and we think through it from there."

The Mathematical Model of Growth

Evan offered a technical yardstick:

"If we want to use tools and have AI understand what I intend, say, 'pick ten stocks that will grow for me,' it will certainly give you something. But it doesn't mean anything to you, because you need to know what the mathematical relationship behind that growth is, so that you can build the logic yourself."

"A basic concept I really like using now is: how do you measure whether something is growing all the time? It's actually just a very simple derivative from high school math. As long as dt is greater than zero, meaning in the positive interval, you won't have much trouble."

"That means someone is continuously investing in it, continuously throwing money into it. Naturally, we would think AI, or some hot industries, or gold, are essentially growth assets."

"So whether it's a zero-sum game or some other kind of game, we really just need to put ourselves in the situation where dt is greater than zero."

The Essence of Information Asymmetry

"This also relates to a saying we hear a lot: information asymmetry. When many people first start trading stocks, they talk about 'whose buy I should follow' or 'who's really good, I'll copy his trades.' I think that kind of thing is, at its core, still..."

Evan paused: "Backwards logic. Why? The most basic reason is simple: what do you have that would make someone willing to make money for you? What's in it for them?"

"I don't think that logic holds up. So if you haven't built the right attitude toward money, or the right logic about resources and business, then it's actually quite dangerous. Even if you make money from some piece of news, the news won't keep coming forever, unless you actually know insiders, or you're a born heir yourself. But that... isn't that just another game? What I'm talking about is the game of ordinary people. That's how it's played. Most of us did come up from ordinary people, step by step."

A Line from Elon Musk

Evan shared an idea he found interesting:

"Elon Musk once said something I think is very true. He said: 'Compared to Musk, the distance between all of you and a hundred million dollars is shorter than the distance between Musk and a hundred million dollars.'"

Some people in the room laughed.

"Why? Because for us to make a hundred million dollars, we just need to earn a hundred million. But he has hundreds of billions of dollars now, which means he would have to spend hundreds of billions before he got down to a hundred million. So he's farther from a hundred million." (Huge laughter.)

"So when I say this, I completely understand it. Because in this world, it's not the case that the more money you have, the more you can throw into it, say tens or twenty trillion, and you'll definitely get to Mars. Without adding any news, I think that if he wants to go to Mars, money alone is nowhere near enough. Because there are plenty of rich people in this world, but they can't build something like SpaceX either. You can't just throw money at it and get it."

"If you can find a suitable, reasonable path, allocate resources sensibly, find a scientific method, and put money into the right research and AI, so that it helps you find materials that can best withstand external pressure, then you can actually pull this off."

"So this is also why I think making money and the capital markets, along with their logic, are such an attractive area. Right, now let's get back to making money."

Why the US Stock Market?

"To make money, you first have to know how to buy and sell. Buying and selling means exchanging. Money's essence is price. Buying and selling, the stock price is the most basic balanced value of a trade, a signal point."

"Now, for us ordinary people, most people's approach to earning money is going to work, which basically means selling your time, your knowledge, your existing resources, your content, and for some, your labor. So is it possible that we sell a bit of our own small amount of money to earn more money? It's just like playing a game. Could ten thousand dollars turn into a return of a hundred thousand, or even two or three hundred thousand?"

"That's the reason investing is so interesting to me."

Evan then started on the most important choice:

"OK, so once we have this basic framework, if we're going to start investing, the first step is to choose a suitable market. In that market, setting aside all the data, and not worrying about foreign exchange controls or local policies, which market has the biggest waves, or the most resources?"

He paused and said: "Without any doubt, it's the US market."

"Why does the US market go up year after year? What's the underlying logic? I'll get you involved. You tell me, why does the US stock market keep growing at a high rate every year?" He pointed into the audience.

Someone replied, "I have no idea." (Laughter.)

Another person said, "Everyone's money keeps flowing in?"

Someone else added, "Because, say, the US has been using dollars to settle oil and so on, and then the dollars printed flow back into the US market, since you can only trade US stocks in dollars."

Evan nodded: "Yes, I think that's the most direct one. Capital continuously flowing into this market is a very big contribution."

The Asset Allocation Choice of the World's Rich

"So basically, the US market... let me put it plainly: as long as you were born on this planet, as long as you aren't cut off, as long as you're connected to the international internet and international communications, and once you've achieved a little success, your investment advisors and every government fund guidance will tell you the same thing: you need to allocate your money to the United States."

"The inflow into the US market every year is an astronomical number. It's flowing in every moment. Whether rates are rising or falling, and the rate change does have a bigger impact, the basic logic is: the rich people of the world all put their money in the US."

"So when I analyze this, I think that if I want to make money, I'll definitely go to the pool with the biggest flow."

The Invention of Stocks: The Dutch East India Company Story

Evan told a historical story:

"Here's a bit of knowledge: how were stocks invented?"

"The Dutch East India Company invented them to take on the Age of Discovery, to spread out all the risks of sending ships, hiring crews, and building vessels. The Netherlands wasn't a big country, and it didn't have many natural resources either, but through this financial mechanism, stocks, it could put the resources of the entire society into the profitable age of ocean voyages."

"And the Dutch East India Company, for its own citizens, if you had bought its shares back then, you'd have been very happy in the 17th and 18th centuries, because its dividends kept paying out for 200 years, roughly speaking."

"So if it keeps making you money for 200 years, that's very satisfying. Even though, yes, we all talk about the colonial era, the slave trade, and the opium trade, that doesn't change the fact that this mechanism was invented. It's a very good way of spreading out the risks of the unknown."

Comparing US Stocks and Crypto in Scale

Evan showed a comparison chart:

"This data is for reference. I checked it with AI and some search tools. Right now, crypto is roughly somewhere between 380 billion and 2.5 trillion dollars. The market has flows both in and out."

"After all that, we can finally come back to the main topic. I also went through many of these logical deductions before deciding: since my understanding has reached this point, will I use this understanding to put some of my allocated money into the US market?"

"The answer: yes, I will."

Part 4 (continued): Trading Strategies and Tool Practice

Time: 01:17:29 - 01:47:15

Four Basic Trading Strategies

"I talked a bit long on this before, so I'll try to go through it quickly." Evan switched to the next slide.

"Since we've chosen to trade in a market with a big flow pool, when we open this game, what we see can roughly be summarized into these four:

1. Long-term investing (Buy and Hold)

"The simplest one: I buy it, I hold it, and I ignore it. That's the most basic. You can buy a house or gold bars this way too. It's the simplest strategy."

2. Day trading

"The second is day trading. You buy today and sell tomorrow, or sell within a week. You trade frequently and make money through many, many small fluctuations."

3. Swing trading

"The third is swing trading. You catch a medium-term trend, maybe hold for one, two, or three months, and then when you feel the trend is over, you exit."

4. Algorithmic trading / quantitative trading

"The fourth is algorithmic trading. You write code and let the machine trade for you automatically. This requires programming ability and mathematical modeling skill."

Evan said: "The strategy I use now is mainly a combination of the first and the third. That means I keep part of my capital invested for the long term to capture the market's average return. The other part, I adjust based on the market's swings."

The QQQ Strategy: A Concrete Backtest

"I did a backtest earlier that was quite interesting. It's: what would happen if I had started dollar-cost averaging into QQQ (the Nasdaq 100 ETF) every month from 2010 and held until now?"

He opened an Excel sheet and started showing the data:

"I used data from TradingView, and then wrote a simple Python script to backtest it. Assume you invest 1,000 dollars every month, starting from January 2010 and continuing through December 2024."

"The result? The total invested is about 180,000 dollars, and the final market value is about 540,000 dollars, a return of close to 200%."

Someone in the audience gasped.

"But!" Evan emphasized, "this is only the simplest strategy. If you're a bit smarter and reduce your investment when the market is obviously overheated, and increase it when the market is in panic, your returns will be even higher."

"For example, in March 2020, when the pandemic broke out, QQQ fell 30%. If you had dared to double down at that moment, your returns could have doubled."

Toolchain: TradingView + Longbridge + MCP

"So how do you actually do this? The toolchain I use right now looks like this:"

Step 1: TradingView for charts and backtesting

"TradingView is a very powerful charting tool. Not only can it show real-time quotes, but you can also write strategies with Pine Script and run backtests."

"For example, say I want to test a simple moving average strategy: buy when the 50-day moving average crosses above the 200-day moving average, and sell in the opposite case. I can write the code directly in TradingView and see how the strategy performed over the past ten years."

"The advantage of TradingView is that its data is very complete, and you can operate it right in the browser without downloading any software."

Step 2: Placing orders with Longbridge Securities

"After looking at the charts, I place orders with Longbridge Securities. Longbridge is a US stock brokerage that supports Hong Kong and US stock trading. Its fees are relatively low, and it supports a Chinese interface, which is friendly to domestic users."

"Another advantage of Longbridge is that it has an API. In other words, you can write code to place orders automatically. This is very useful for people who want to do algorithmic trading."

Step 3: Using AI for assisted analysis

"Finally, I use some AI tools to help with my analysis. For example, I feed financial reports and news data to AI and have it summarize the key information for me."

"Or I ask AI to write simple backtesting scripts for me. For example, 'Write me a Python script to backtest a monthly dollar-cost averaging strategy on QQQ,' and AI can give me runnable code directly."

"This greatly improves my efficiency. Before, I might have spent an hour writing code, debugging, and plotting. Now it may only take ten minutes."

Applying MCP (Model Context Protocol)

"I'm also trying something very new lately, called MCP." Evan said, "MCP is a protocol launched by Anthropic (the company behind Claude), which lets AI directly call various tools and APIs."

"For example, I can write an MCP server that lets Claude call Longbridge's API directly to check my holdings, place orders, and view market quotes. That way, I can say in the chat box, 'Buy 100 shares of Apple for me,' and Claude will automatically call the API to complete the operation."

"Of course, this is still in the testing stage. I don't recommend that people use AI to place orders directly, since it involves real money. But I think this is a very interesting direction."

Risk Control: Never Go All In

At this point in the discussion, Evan emphasized a very important principle:

"No matter what strategy you use, the most important point is: never go All In."

"I've seen too many people who put all their money in when the market is good, and even add leverage. Then the market pulls back, they get liquidated, and they lose everything."

"My own principle is: I only put 20 to 30 percent of my total assets into the stock market, and within that 20 to 30 percent, I further diversify across different targets."

"For example, I put 50% into a broad-based ETF like QQQ, 30% into some individual stocks I'm bullish on, and keep 20% as cash for management, so I can buy the dip at any time."

"That way, even if the market drops 50%, I'd lose at most 10 to 15 percent of my total assets. It won't cripple me."

Interaction: Everyone's Holdings

Evan asked the audience: "Who here is holding positions right now? What are your main holdings? Would you share?"

Someone said, "I mainly hold QQQ and SPY (the S&P 500 ETF)."

Another person said, "I bought some tech stocks, like NVIDIA, Microsoft, and Apple."

Someone else said, "I bought some Chinese concept stocks, like Alibaba and Pinduoduo."

Evan nodded: "All of those are fine. QQQ and SPY are the most stable choices. Tech stocks can deliver higher returns if you pick well, but they're more volatile. As for Chinese concept stocks... I personally don't recommend them much, because the policy risk is too high."

"But the most important thing is, you need to know why you're buying this asset. Is it because you like its fundamentals? Or because you think it's going up? Or because someone else recommended it?"

"If you can't even explain why you're buying, then you'd better not buy."

Choosing Information Sources

Someone asked: "Evan, what news do you usually read? How do you get your information?"

Evan answered: "I mainly look at a few:

Bloomberg: the most authoritative source of financial news, but it requires a subscription.
The Wall Street Journal: also very authoritative, with very high-quality reporting.
Twitter/X: this is what I use most. A lot of important news often spreads on Twitter first. I follow some well-known investors, analysts, and journalists.
Reddit: especially r/wallstreetbets and r/investing, where you can see the sentiment of retail investors.
Seeking Alpha: an investment analysis community with many high-quality analysis articles."

"But, the most important thing is not to get drowned in information. There's too much information now. If you look at everything, you'll get lost. My advice is: find three to five sources you trust, check them regularly, and ignore the rest."

Final Advice

Evan wrapped up:

"What I shared today is really all very basic material. But I think the basics are often the most important."

"A lot of people want to jump straight to advanced techniques, to learn options, leverage, and algorithmic trading. But if you're not clear on the most basic questions, like 'why does the market go up' and 'how does money flow,' then learning more techniques won't help."

"My advice is: start with the simplest dollar-cost averaging, gradually accumulate experience, and gradually build your own investing system."

"Investing is a marathon, not a sprint. You don't need to earn 100% a year. You only need to steadily earn 10 to 20% per year, and with long-term compounding, your returns will be very impressive."

"Last, never invest money you can't afford to lose. If losing this money would affect your life, then you shouldn't be investing."

The room broke into applause.

Part 5: Building an iOS App from Zero to One: An Indie Developer's AI Practice

Speaker: Leo (a college senior and indie iOS developer)

Time: 01:47:15 - 02:34:52

Opening and Self-Introduction

Steven handed the mic to Leo: "Next, please welcome Leo to share his iOS development experience. Leo is a senior in college, but he has already used AI tools to develop a very interesting iOS app. I think his experience is very instructive for classmates who want to do indie development."

Leo walked up on stage, looking a bit nervous. He smiled and said: "Hi everyone, I'm Leo. Today I want to share how I, as a student with no computer science background, used AI tools to build an iOS app from zero."

"Let me start with my background. My undergraduate major was International Economics and Trade, which has nothing to do with computers. But starting in my junior year, I became interested in programming and started teaching myself."

"At first I learned Python, because people online say Python is simple. But after a while, I realized I didn't know what to use Python for. Web scraping? Data analysis? Neither was really what I wanted."

"Then it suddenly occurred to me: I use my phone every single day. If I could make an app that I myself use, wouldn't that be cool? So I decided to learn iOS development."

Why iOS Development?

"A lot of people might ask: why iOS, instead of Android or the web?"

Leo explained: "There are a few main reasons:

I use an iPhone myself, so I'm more familiar with the iOS ecosystem and it's easier for me to think of needs.
The iOS development experience is better. Xcode is a bit clunky, but overall it's still nicer to use than Android Studio.
iOS users are more willing to pay. If I want to commercialize the app later, iOS is a better choice.
SwiftUI has lowered the barrier. Before, to learn iOS development, you had to learn Objective-C or UIKit, which was very complicated. But with SwiftUI, the learning curve is much gentler."

First Attempt: A Painful Failure

"My initial idea was very simple: make a to-do list app. I felt that the to-do apps on the market didn't really fit my needs, and I wanted to make one that was simpler and more suited to my habits."

"So I started learning Swift and SwiftUI. I watched a lot of tutorials, bought some courses, and spent about two months finishing the basics."

"But when I really started writing code, I found I had no idea where to begin."

"I knew how to create a button, and I knew how to show a list, but I didn't know how to combine these things into a complete app."

"I was stuck at this stage for a long time, about a month, with no progress at all. At one point I even thought about giving up."

The Turning Point: AI Tools Step In

"The turning point came last October. I came across an article about how to use ChatGPT to assist with programming."

"At first I approached it with a try-it-and-see mindset, and I asked ChatGPT a question: 'I want to make a to-do list app. Where should I start?'"

"ChatGPT gave me a very detailed answer, including:

The main screens needed (a list screen, a detail screen, a settings screen)
The data models needed (a Todo model and a Category model)
How to store the data (Core Data, UserDefaults, CloudKit)
The features to implement (add, delete, edit, search, sort)"

"This answer made me suddenly realize: the reason I didn't know how to start before was that I didn't have a clear architecture."

"Once I had this architecture, I started implementing features one by one. Whenever I ran into something I didn't know, I asked ChatGPT, which gave me code examples, and I then modified them to fit my needs."

"That's how, in about two weeks, I finished the first version of the to-do app."

Problems with the First Version

"But when I actually started using this app, I found a lot of problems:

Poor performance. When there were more than 100 to-do items, scrolling the list would stutter.
Ugly UI. Although everything worked, it looked very amateurish.
Missing lots of details. For example, there were no gesture operations, no animations, and no sound cues.
Data sync issues. I used iCloud sync, but data often got lost or duplicated."

"This made me realize: making an app that works and making an app that's pleasant to use are completely different things."

Second Iteration: Learning from Great Apps

"I started studying the great to-do apps on the market, like Things, Todoist, and OmniFocus. I analyzed their interaction design, visual design, and feature design, and looked at how they solve problems."

"Then I found that these great apps share some common traits:

The interaction is very smooth. Every animation and every gesture has been carefully designed.
The visuals are very clean. There are no superfluous elements, and every pixel has a purpose.
The features are just right. They don't pile on functions; they push the core features to the extreme.
The details are thoughtful. For example, long-pressing gives haptic feedback, and deleting asks for confirmation, and so on."

"I started redesigning my app based on these standards."

AI's Role in the Second Iteration

"In the second iteration, AI played a very important role. I treated AI as a full-stack assistant and had it help me with all sorts of tasks:

1. Code refactoring

"I paste the code I wrote into AI and say: 'Help me optimize this code so it's more efficient and readable.' AI gives me some suggestions, such as:

Extracting repeated code into functions
Using more efficient algorithms
Following Swift best practices"

2. UI design

"I describe the interface I want, and then have AI generate the SwiftUI code. For example: 'I want a card-style list, where each card has rounded corners and a shadow, with a checkbox on the left and the title and time on the right.'"

"AI gives me a piece of SwiftUI code, and I adjust it based on how it looks."

3. Animation implementation

"Animation is one of the harder parts of iOS development. I tell AI, 'I want an animation that slides in from the right,' and AI gives me the code and explains what each line does."

4. Troubleshooting

"When I run into a bug, I paste the error message and the related code into AI and ask it to help analyze the cause. Many times, AI can point directly to the problem."

5. Documentation lookup

"The iOS API documentation is enormous, and I can't memorize all of the APIs. When I want to implement something but don't know which API to use, I ask AI, and it tells me which class and which method to use."

From ChatGPT to Claude to Cursor

"I started out using ChatGPT, but later I found that for programming tasks, Claude is often better."

"Claude gives more complete code and more detailed explanations, and it hallucinates less."

"Later, I discovered Cursor, an AI editor based on VS Code. It lets you call AI directly inside the editor, which is very convenient."

"The advantage of Cursor is that it can see your entire project structure, so the suggestions it gives fit your project's style better."

"For example, when I'm writing a new feature, Cursor automatically suggests which files I should create, where the code should go, and how it should integrate with the existing code."

"This greatly improved my development efficiency. Before, a feature might have taken me a full day; now it takes about half a day."

The Final Product: An App with Warmth

"After roughly three months of iteration, I finally made a version I was happy with."

Leo took out his phone, mirrored it to the big screen, and showed his app.

"This is my app, called 'Focus Flow.' Its core idea is: help you focus on what matters most right now."

"Its main features are:

Smart sorting: automatically sorts to-do items by deadline, priority, and estimated time.
Pomodoro timer integration: every to-do item can start a Pomodoro timer.
Statistics: records how many to-dos you finished each day and how much time you spent.
Widget support: shows the three most important things for today on your home screen.
iCloud sync: automatic sync across multiple devices."

"But most importantly, every single detail of this app is something I polished with care."

"For example, when you complete a to-do, there's a pleasant animation, paired with haptic feedback, which gives you a sense of accomplishment."

"Another example: when you haven't completed any to-dos for a long time, the app gently reminds you, 'Want to take a break?' instead of coldly pushing you."

"I want this app to be not just a tool, but a friend that keeps you company."

The Experience of Getting onto the App Store

"After finishing the app, I decided to publish it on the App Store."

"This process wasn't easy either. I needed to:

Register an Apple Developer account: 99 US dollars a year.
Prepare all sorts of materials: app icon, screenshots, promotional video, app description.
Handle the privacy policy: the App Store requires you to have a privacy policy page.
Submit for review: waiting for Apple's review usually takes one to three days."

"When I submitted for the first time, I got rejected. The reason: my privacy policy wasn't detailed enough."

"I spent another day using AI to generate a detailed privacy policy, resubmitted, and this time it passed."

"When I saw my app appear on the App Store for real, the sense of accomplishment was truly hard to describe. This was the first time I made a real product, not just a practice project."

User Feedback and Iteration

"After the app launched, I started promoting it in some communities, like Sspai and V2EX."

"The response was pretty good. In the first week, there were about 200 downloads. It's not many, but for me it was already very satisfying."

"More importantly, I received a lot of user feedback. Some people said they liked the design, some asked for certain features, and some found bugs."

"I take every piece of feedback seriously. If it's a good suggestion, I consider adding it in the next version. If it's a bug, I fix it as quickly as I can."

"This feeling of communicating directly with users makes me feel that I'm not making a cold product, but creating a kind of connection."

Thinking About Commercialization

"Someone asked me: how do you plan to make money?"

"Honestly, at first I didn't think much about making money. I made this app mainly to learn, and to prove to myself that I could make a complete product."

"But later, as more users came, I started thinking about the possibility of commercialization."

"I studied the revenue models of some indie developers, and there are roughly these:

Paid download: users have to pay before they can download.
In-app purchase: the app is free, but advanced features require payment.
Subscription: charged monthly or yearly.
Ads: showing ads inside the app."

"In the end, I chose subscription, because:

It lets users try it for free first, lowering the cost of trying.
Continuous revenue can support my ongoing development and maintenance.
It doesn't hurt the user experience, unlike ads, which can be disruptive."

"I designed a Pro version at 3 dollars a month or 25 dollars a year. The Pro features include:

Unlimited to-do items (the free version is capped at 20)
More themes and icons
Advanced statistics
Priority customer support"

"Currently, about 5% of users have bought the Pro version. The revenue isn't much, but it's enough to cover my server costs and the Apple Developer account fee, with a little left over."

"For me, this is already very satisfying. I never planned to get rich off this app. As long as it can sustain itself and keep me motivated to keep going, that's enough."

The Limits of AI-Assisted Development

"I've talked a lot about the benefits of AI, but I also want to talk about its limits."

"AI is not all-powerful. It can help you write code, but it can't make decisions for you."

"For example:

Product direction: what kind of product you want to make and what problem it solves. AI can't answer that.
Design taste: what makes a design good, what makes an interaction feel comfortable. That takes your own judgment.
Code architecture: how to organize the code and how to design modules. That requires some programming experience.
Bug investigation: some complicated bugs AI may not be able to pin down accurately, and you have to debug them yourself."

"So, AI is a powerful assistant, but not a replacement. You still need to learn, think, and practice."

Advice for People Who Want to Learn iOS Development

Leo summed up:

"If you also want to learn iOS development, my advice is:

1. Don't chase perfection

"Your first app will almost certainly be rough, and that's normal. Don't try to make a perfect product right away. Make it first, then improve it gradually."

2. Start with small projects

"Don't start with a very complicated app. Begin with a simple feature, like a counter, a calculator, or a notes app. Build a solid foundation before taking on more complex projects."

3. Read other people's code

"There are many excellent open-source iOS projects on GitHub. Look at how others write, and learn their coding style and architecture design."

4. Make good use of AI tools

"AI can greatly lower the barrier to learning, but don't rely on it too much. Try on your own first, and only ask AI when you really can't solve it."

5. Be patient

"Learning to program doesn't happen overnight. It takes time and practice. It's normal to run into difficulties, so don't give up easily."

6. Build the product you want to use yourself

"Most importantly, make the product you truly want to use. Only then will you have the ongoing motivation to refine it, instead of giving up halfway through."

The room broke into applause again.

Epilogue: On Creating and Acting

Time: 02:34:52 - 02:41:18

Steven's Summary

Steven walked up on stage and gave the final summary:

"Thanks to Evan and Leo for their wonderful talks. I think the three topics today, AI tools, investing, and indie development, look unrelated, but they actually share one theme: how we should think and act in this era of rapid change."

"First, stay curious. Whether it's a new tool like OpenCloud, the US stock market, or iOS development, if you don't try it, you'll never know what it can bring you."

"Second, learn by doing. Evan is right. Losing a hundred thousand or so teaches you more than listening to a hundred lectures. Leo also truly learned iOS development by actually building a project. Theory can never replace practice."

"Third, embrace uncertainty. The pace of AI development far exceeds what we imagine, and market swings often surprise us too. We can't predict the future, but what we can do is: keep learning, keep adapting, keep acting."

"Finally, find what you truly care about. Whether you're building products, investing, or creating content, if you're doing it only to make money or to finish a task, it's very hard to persist. Only when you truly care will you be willing to put in time and energy, and have the patience to polish every detail."

"That's all for today's sharing. I hope everyone can find their own 'wave,' and bravely ride it."

Final Interaction

After the formal session ended, people didn't leave right away. They gathered together and kept talking.

Some were asking Evan about options, some were asking Leo for details on SwiftUI, and some were discussing the future of AI with Steven.

The lights in the café gradually dimmed, but the enthusiasm for discussion stayed high.

This is the charm of Crazy Saturday: a group of young people full of passion for technology and the future, exchanging ideas here, sparking each other's thinking, and then heading off with new inspiration and drive.

[End of text]

Appendix: Key Takeaways

OpenCloud

Core value: market education, helping more people understand the possibilities of AI agents
Main problems: uncontrollable security, high barrier to use, fundamentally dependent on the underlying model
Usage tips:
- Choose the strongest model (Claude 4.6)
- Use neo (reset) and C (compress) frequently
- Carefully design the system prompt, user prompt, and memory

Investing

Core philosophy: surf the flow of money and find growth curves where dt > 0
Why US stocks: continuous global capital inflows, the strongest liquidity
Basic strategies:
- Long-term holding of broad-based ETFs (QQQ, SPY)
- Invest only 20 to 30% of your assets in the stock market
- Never go All In
Toolchain: TradingView (charts and backtesting) + Longbridge Securities (trading) + AI (assisted analysis)

iOS Development

Learning path:
- Learn the basics of Swift and SwiftUI
- Use AI to get started quickly
- Practice with small projects
- Study the design of great apps
- Keep iterating and polishing the details
AI tools: ChatGPT → Claude → Cursor
Commercialization: subscription, free plus a Pro version
Most important: make the product you truly want to use

Recorded and compiled on: February 8, 2025

Editor's note: This document is based on a transcript of the live recording. It tries to faithfully reproduce the original remarks of the speakers, the conversations, and the technical discussion. Some colloquial expressions have been retained to preserve authenticity.

The discussion began with a rather sharp topic. Steven had just come across a case on social media: a user, excited to try OpenCloud, uploaded his entire AI configuration file, including his API token and various encryption keys, straight to GitHub, publicly. This real case set off a debate among everyone present: is on-device AI tooling really safe?

The Uncontrollability of Technology

"Even if your model is deployed locally, you still can't fully control its behavior." Steven's remark made the mood a little heavy. He explained that even if you repeatedly stress in various system prompts and skill docs "do not upload sensitive information," the generative nature of AI models means it can get "tangled in memory" and carry out operations you never expected.

The core of the problem is this: the very power of tools like OpenCloud is also the source of their danger. They can operate your browser, read and write files, and send WeChat messages. That "can do anything" capability brings convenience, but it also means the tool could delete your important files or share your private information at any moment.

If you deploy it on Docker or a VPS, these risks drop, but you also give up its most valuable local capabilities. It's a classic paradox: the freedom you want and the safety you want often contradict each other.

Do We Really Care About Security?

But the discussion quickly turned to a deeper question. "To be honest, I think most Chinese people really don't care that much about data security." Steven's observation struck a chord. He went on to explain: if today were the last day you were alive in this world, and you were asking whether this product is good, you wouldn't consider security at all. You'd only consider what it can do.

This hypothetical actually reveals a fundamental problem in product design. When we tech practitioners sit here discussing privacy, security, and data leaks, have we fallen into some kind of "elitist bias"? Do our parents, our children, and the vast majority of ordinary users who aren't in this room really care about these things the way we do?

"We may need to allow for this deviation to exist." Steven said, "From an accelerationist perspective, we may even have to allow ourselves to no longer be the center of society. The relationship between humans and AI may slowly shift from today's 'ten to zero' to 'eight to two,' or even 'nine to one.' That slider will keep moving. This is a reality we have to accept."

Optimism from a Long-Term View

By the end of the discussion, everyone reached a consensus: most of the problems we worry about now may not be problems in the future. Just as GPT-3's context window of a few dozen tokens, which we once worried about, has now grown to the millions. Technology will naturally solve many problems that seem unsolvable today.

But data security will persist. It's not something technology alone can fully solve. It's a complex issue that needs social attitudes, laws and regulations, and technical means to evolve together.

Part Two: The Real Value of OpenCloud, from Tool to Way of Thinking

The discussion naturally turned to OpenCloud's product capabilities. A classmate working in product and business raised a very practical question: "We all care a lot about where OpenCloud's capability boundaries lie. If it really gets productized, how will ordinary users use it?"

The Dilemma of Intent Recognition

The core problem is this: right now, when we use ChatGPT or Claude, we have to explicitly say "use such-and-such skill." But ordinary users express themselves as "I want to get something done," and they expect the AI to call the right tools on its own. This seemingly simple need actually involves complex intent-recognition technology.

What's even more frustrating is that even for simple problems, OpenCloud still calls skills, goes through a long thinking process, and burns a lot of tokens. That path is obviously not optimal, and it may even be slower than using a basic large model directly.

Someone asked whether there's a way to optimize its calling logic, so that it doesn't "use a cannon to kill a chicken" with simple questions, and only activates the full skill-calling mechanism for complex ones.

The Shell and the Engine of a Car

Steven offered a vivid metaphor on this: "OpenCloud is like a car's shell. What really matters is the engine and the battery, which means the underlying model."

He explained that if you switch OpenCloud to a very weak model, such as Kimi 1.5 or the models from early 2024, you'll find it isn't magical at all. Its ability to complete tasks may even fall short of the basic thinking mode OpenAI had back then. What does that show? It shows that OpenCloud's value fundamentally still depends on the capabilities of the underlying model.

"Many of the problems we see now, like token consumption and path optimization, are in my view all 'car shell' level problems. When models improve by another order of magnitude, when the context window goes from millions to billions, these problems may no longer be problems at all. You don't need to care about those minor optimizations."

This view is actually very controversial in tech circles. But it does offer a perspective worth considering: in a fast-iterating technology field, where should we put our energy? Into optimizing the performance of today's tools, or into thinking about more fundamental questions?

A Three-Factor Framework in Practice

Of course, for people who want to use OpenCloud right now, there are still some practical methods. One participant shared his experience: "When I use it myself, I've found three factors to be very important."

First, the choice of model. A friend of his also installed OpenCloud, but used Pinyin or another weak model, and the experience was very poor. He, on the other hand, used Claude 4.5 throughout, and even 4.6, and the experience was completely different. A model's capability is the most fundamental thing.

Second, how clean the context is. OpenCloud's design makes you forget the concept of "context": you don't have an explicit "new chat" button. But in practice, given the limits of current models, clearing the context is still very important. He often uses two commands, neo (reset context) and C (compress context), to help the AI better understand the next task.

Third, prompt design. This has three layers: the system prompt (defining the AI's role and capability boundaries), the user prompt (clarifying the specific task and strategy), and memory (a knowledge base the AI can search on demand). These three work together to get the AI to solve problems in an appropriate way.

The Market Education Value of OpenCloud

In the end, everyone agreed that OpenCloud may only be a transitional product, but its biggest contribution is market education. Just as WeChat Pay once trained Chinese people in mobile payment habits, OpenCloud is letting more people see the possibilities of AI agents. There will certainly be more and better products in the future that solve the problems it has today.

"It has to do this market education, so people know what the future looks like. That matters. It may be only a transitional state, but at some stage in the future, truly mature products will grow out of it."

Part Three: Will AI Tools Trigger a Hardware Price Increase? An Economics Question

Someone in the discussion raised an interesting question: "If tools like OpenCloud really became widespread, would they cause Mac Minis or other personal computer hardware to see the kind of price spikes we saw when Bitcoin mining drove up graphics card prices?"

Steven offered a figure that surprised people: "The demand in the personal market, compared with data centers and the enterprise market, may not even reach 10%."

This is an important realization. We tend to overestimate the influence of the individual consumer market. In reality, what actually drives hardware prices are the large-scale purchases of enterprise customers. A single data center's purchase volume can be equal to hundreds of thousands of ordinary consumers.

From the perspective of economies of scale, an increase in demand doesn't necessarily lead to higher prices. When something goes from meeting the needs of 100 people to meeting the needs of 10,000 or 100,000 people, prices may actually fall because of scale effects and supply-chain concentration.

"The most expensive thing is one person's demand. When 100, 10,000, or 100,000 people have the same need, the price actually goes down." Steven explained, "Of course, if the demand has to go through chip computing power or is constrained by the laws of physics, it could be expensive. But if it's only at the software level, or can be met by existing capacity, it's not necessarily expensive, and it may even keep dropping."

The Truth Behind Memory Price Increases

So why are we seeing memory prices rise? Steven gave a very clear answer: "Micron switched its capacity, originally meant for personal PCs, entirely over to data centers, to big customers like NVIDIA."

That's the real reason for the memory price increase. It's not that personal demand suddenly exploded, but that the allocation of supply-chain resources changed. When manufacturers find that selling to data centers earns more money, they naturally adjust their capacity. It's a supply-side story, not a demand-side one.

Apple's Supply Chain Capability

As for whether the Mac Mini will run short, Steven was optimistic: "Apple's expansion capacity is tied to China's industrial capability. There may be short-term inventory tightness, but it can recover in two weeks at most."

He also made an interesting point: OpenCloud's barrier to entry is still very high. You can't expect ordinary people to get everything done with a single sentence. This market is far from reaching "everyone uses it" levels. Most people may not have even heard of this tool, let alone used it.

"So I think the impact on hardware demand is minimal. The bigger force still comes from changes in enterprise and data center demand."

Part Four: Surfing the Money, an Investment Philosophy from a Coder-Turned-Trader

In the afternoon session, things reached the most exciting part. Evan (Ailun) took the stage to share his experience with US stock investing and quantitative trading. He began with a self-introduction: he graduated with a bachelor's degree in 2019 and originally wanted to be a product manager at Futu, but ended up at Huatai Securities. A year later he found the brokerage job wasn't what he had imagined, so he switched to writing code, working at ByteDance and then Horizon Robotics, and is now at a certain factory. But his main line of work has always been writing: first investment articles, and later technical articles.

"I hope today isn't about teaching everyone how to trade stocks, but about sharing more fundamental logic." Evan said, "Tools change, AI changes, but the way of thinking and the understanding of resource allocation are the things that matter more."

The Essence of Money: A Resource Dispatch System

Evan shared a concept that changed how he thinks about investing. When he was playing the game Battlefield, in the scene where you rob a bank, people shout "Take your vote!" In English, "vote" doesn't just mean a ballot. It also stands for a kind of choice, a kind of "resource dispatch order."

"Money isn't just paper bills. At its core, it's a voucher for resource allocation." Evan said, "Once you build this way of thinking, a lot of things become clear."

He gave an example: we often see news saying "some market evaporated trillions of dollars." Did that money really evaporate? Did that wealth really vanish into thin air?

"It didn't. A crash just means people sold stock and turned the 'goods' into cash. The money wasn't burned. It just moved from one place to another. It may have flowed out of tech and into real estate or bonds. The key is: do you know where it went?"

If you're just an ordinary small retail investor, seeing a drop, you can only curse a few times and wait to break even. But if you understand the flow of liquidity, you'll start asking: where is the money going? What's going to rise next?

Investing is like surfing. You need to know the direction of the current across the whole pool, and know where the biggest waves are. What you need isn't to wait in place, but to actively look for the places where capital is pouring in.

Why Choose US Stocks?

Evan spent a long time explaining why he chose to invest in US stocks rather than A-shares or crypto.

"What's the underlying logic behind why the US stock market keeps growing year after year?" He asked the audience. Someone answered that it's because of dollar hegemony and oil settlement: the dollars printed eventually flow back into the US market.

Evan nodded: "That's an important reason. But more fundamentally, the US is the first choice for the world's wealthy to allocate assets. As long as you have some money on this planet, as long as you're not completely isolated, your investment advisors and financial planners will definitely tell you to put part of your money into the US market."

What does that mean? It means that every moment, an astronomical amount of capital is flowing into US stocks from all over the world. This market receives an enormous inflow of funds. Whether there's a rate hike or a rate cut, whatever short-term fluctuations there are, the long-term trend is upward.

"If you want to make money, you have to go to the pool with the biggest flow. Without a doubt, that's the US market."

The Invention of Stocks: The Wisdom of Spreading Risk

To illustrate the value of the stock market, Evan told a historical story.

Stocks were first invented by the Dutch East India Company. In the Age of Discovery, building ships, hiring crews, and buying goods all required huge amounts of capital and carried enormous risk: ships could sink, crews could mutiny, cargo could be seized by pirates. No single merchant could bear that kind of risk alone.

So they invented the stock mechanism: spread the risk across the whole of society. You buy a share, I buy a share, and we all share the risk and the returns together.

"The Netherlands had no particular resources and wasn't a big country, but through this financial mechanism, it put the resources of the entire society into the Age of Discovery." Evan said, "And if you had bought the Dutch East India Company's shares back then, you'd have been very happy, because it paid dividends for 200 years."

The story shows that a stock isn't just a piece of paper. It represents a mechanism of risk distribution and resource allocation. After several hundred years of evolution, this mechanism has become very mature.

A Mathematical Model of Growth

Evan offered a simple test for judging growth: "How do you measure whether something is growing continuously? High school math taught us: take the derivative and check whether dt is greater than zero. As long as you're in the positive range, you won't run into major trouble."

What does that mean? It means you should look for tracks where people keep investing and capital keeps flowing in. The AI industry is like that, some energy stocks are like that, and gold is too. They all represent some kind of "scarce resource."

"We're not looking for a zero-sum game, we're looking for a positive-sum game. We want to put ourselves in an environment where dt > 0."

Strategy in Practice: A QQQ Backtest

Evan showed some backtest data for specific trading strategies. Using the Nasdaq 100 index fund QQQ as an example:

If you had held QQQ from 1993 until now, a 26-year stretch, your return would be about 13 times your money. Sounds good, right? But you may have forgotten that your maximum drawdown could reach 82%. That means you might go through a period where your account shrinks by more than 80%. Most people simply can't handle that psychological pressure.

So is there a way to keep a respectable return while lowering the risk? Evan shared a simple strategy: add a moving average filter. When QQQ falls below its 200-day moving average, sell immediately, and buy back once it regains the average.

The return of this strategy drops to about 8 times, but the maximum drawdown is greatly reduced. For most people, this may be a more acceptable risk-return ratio.

"If you want to be more aggressive, you can use 2x or 3x leveraged ETFs." Evan said, "The US stock market has many products like this, where fund companies build in leverage. Of course, returns are higher, but so is the risk."

Tool Choices: Longbridge and TradingView

Evan specifically recommended two tools.

First is Longbridge Securities. "I looked at a lot of brokerages, and Longbridge is the most friendly to individual developers. Its API is very open, so you can easily get historical data and run strategy backtests. In comparison, Futu, despite its big name, isn't as good in this respect."

Second is TradingView. "This is a backtesting platform for individuals. You don't need any institutional account. As long as you can write a little Python, you can run your strategy and see how it performed historically."

He also specifically pointed out an MCP (Market Context Protocol) tool that can be called directly within Claude to fetch stock data in real time and generate professional analysis reports.

"But I don't recommend letting AI trade for you directly." Evan said with a smile, "Because every US stock trade has commission fees, around 0.02 percent. If AI traded at high frequency, your fees would be terrifying. AI is good as an adviser, but the actual trading still has to be done by you."

Cognition and Risk

At the end of the session, Evan shared his core view: "Stock investing is fundamentally about making money through cognition. If you can know the answer before 51% of people do, you can make money. But if you're just following trades and listening to tips, that's dangerous. Why would anyone help you make money? What do they get out of it?"

He also reminded beginners in particular: "If you want to get in, I suggest starting with ETFs, and not going straight for high-risk products like options and futures. Losses are the best teacher, but you have to control the size of your losses and not get liquidated in one go."

Someone in the audience asked a sharp question: "Have you made money from stock trading?"

Evan laughed honestly: "I did make some money last year and the year before. But the big drop at the beginning of this year cost me a lot too. That's the market. Nobody can make money forever. What matters is building the right thinking framework and understanding the logic of capital flows. Tools change, markets change, but these fundamentals don't change."

Part Five: Building a Really Good Product with AI, a Senior Student's Path as an Indie Developer

The final sharing segment was a senior college student's experience developing an iOS app. He seemed a little nervous, but when he talked about his product, there was light in his eyes.

"Hi everyone, I'm... my handle on Twitter is..." He introduced himself first. "In my spare time I independently developed an iOS app called NeoDB. It's also the first app I've made, and it's now on the App Store, with more than 11,000 users, and over 3,000 stars on GitHub."

The room applauded. For a senior student to get results like this on a first app is no small feat.

Why Build NeoDB?

"NeoDB is an open-source platform for tracking books, films, and shows, a bit like Douban." He showed the app's interface. "You can mark movies you've watched, books you've read, music you've listened to, and games you've played. You can rate them, write reviews, and see other people's recommendations."

Why did he build it? He gave a very simple answer: "Because I have this need myself."

This echoed a line from the afternoon: "Beautiful yourself is the fastest way to beautiful everyone." Making a product for yourself is the fastest path to making a product for everyone.

"NeoDB itself is a website, but it doesn't have an official mobile app. I'm a user of the website myself, and I wanted to use it on my phone, so I decided to make one. That's the best starting point for a product: starting from your own needs."

The Complexity of a Product: Beneath the Iceberg

He showed several screenshots of the app: the timeline, the discovery page, the marking page, the settings page, search, user profiles, item details... The dense pages made the audience realize that building an app is far more complex than they imagined.

"These pages are really only 20% of the app." He said something that surprised many people.

"Why? Because for every page you build and every new feature you develop, you have to design several states. The first is the success state, where the page displays normally. The second is the loading state, where data is still being requested. The third is the error state: network errors, server errors, insufficient user permissions... and this may be subdivided into several types."

He went on: "That means every feature needs at least three sets of UI. And that's before counting edge cases: what if the user has no network? What if the user switches accounts? What if data sync fails? All of these have to be considered."

"So I say this is only 20%. The other 80% is the polishing and detail work you don't see."

Four Stages from Zero to Product

He shared his development process, summarized in four stages:

Stage one: have an idea. "The idea doesn't need to be very professional. Many good ideas come from outsiders." He gave an example: he knew an artist friend who, after learning about AI, built an app related to music and art. "If you were just an internet practitioner, you might never have thought of this need."

Stage two: get the core features running. "Don't try to make a perfect product right away. My first version was very crude and entirely generated by AI. It had only two functions: logging in, and marking items. But once that core feature worked, the MVP was established."

Stage three: polish the details. "This is the most time-consuming stage, maybe 70% of the total development time." He said, "Because AI is very good at building a single page, but making the whole app have a unified design language, smooth animations, and consistent interaction logic requires you to adjust it bit by bit."

He particularly emphasized: change only one color scheme or add only one animation at a time, not a big overhaul all at once. "Because AI tends to miss details. If you give it too many instructions at once, it may overwrite features you had before. So you must use Git for version control, so you can roll back at any time."

Stage four: launch and iterate. "Once you've completed your first release, the road ahead becomes much clearer. Users will give you feedback, and you'll know what to do next."

AI Is a Tool, People Are the Core

He spent a long time on one point: in the AI era, the model doesn't matter as much as the person.

"Models are updated every day. Yesterday Anthropic released Claude 4.6, OpenAI released 5.3, and DeepSeek released 2.5... Models change, but the people who use them don't. Same model, different people, completely different results. Same person, different models, also different results. So the key is the person."

He gave a vivid example: "AI can help you generate pages, write code, and do design, but what's your product called? What problem does it solve? What style are the colors? How fast should the animation be? These decisions all come from people. AI is your assistant, but it can't replace you in making decisions."

He also mentioned an anxiety that resonates with many people: "I often see people on Xiaohongshu saying 'I made an app in three days,' 'listed on the App Store in two days.' I get anxious and wonder whether spending a month polishing details is too slow."

"But later I realized that products churned out in bulk, like articles mass-generated by AI, don't really have any competitive edge. Truly good products are always people-led and carefully polished, because products are made for people, and only people know what people need."

Aesthetics Is a Long-Term Practice

Someone asked him: how do you develop product taste?

He admitted honestly: "There's no quick way to build taste. It's a long process of accumulation."

His advice: follow excellent designers, experience carefully designed apps, and observe the small details that seem minor but actually matter, like whether the fonts are aligned, whether you can swipe left to go back, and whether the animation speed is just right.

"I'd recommend following some designers on Twitter and watching interviews with developers of great apps. You don't have to fully understand their technology, but by using those apps, you'll gradually build your own taste."

He also recommended a prototyping tool, Excalidraw: "Its lines are imperfect, and that actually helps you focus on the overall structure instead of getting stuck on pixel-level details. Sometimes I sketch a prototype first, and sometimes I have AI generate one directly and then adjust it."

Thinking About Monetization

On how to make money, he shared his strategy: "Free download plus in-app purchases. Don't charge for the download, because that would drastically lower conversion."

His approach: make the core features free so users can try them. Then set some advanced features, for example, five free uses per day, with payment required starting from the sixth. "Let users form a habit first, then guide them to pay. The conversion rate is higher that way."

Someone asked how to handle taxes on revenue. He said casually: "Apple automatically handles taxes in each country. You only need to collect the money. For a small individual developer like me, with income not very high yet, you don't need to worry too much about it."

Done Is Better Than Perfect

Near the end of his talk, he said something very powerful: "You have to finish the product and then launch it. Even if it's not perfect, that's fine."

"I've made some products before, but because of perfectionism, I always felt they weren't good enough, so none of them were ever released. This time I forced myself: finish first, launch first, then iterate slowly. Once you've taken the first step, the road ahead becomes incredibly clear."

"Like today, each of us left home and came to this venue. Once you've taken the first step, you've arrived. If you keep sitting at home thinking 'should I go, what if there's traffic, what if the talk isn't any good,' then you'll never get there."

The room applauded warmly.

Epilogue: People Are the Most Important Variable

Three hours of sharing came to an end, and outside the window it had already grown dark. Nobody rushed to leave. They gathered together to keep talking, exchanging WeChat contacts and sharing their projects.

The topics of this session were broad: from AI safety to product design, from investment philosophy to indie development, from technical tools to business models. But if you had to find one theme running through the whole session, it would be the line Steven said in his final summary:

"Beautiful everyone is the fastest way to beautiful no one. Beautiful yourself is the fastest way to beautiful everyone."

In an age where AI evolves every day and tools update every day, what truly stays the same? It's human needs, human taste, human judgment, and human creativity.

OpenCloud may be replaced by better tools, QQQ strategies may stop working, and NeoDB may be surpassed by other apps. But if you understand the underlying logic, understand how money flows, understand how products are polished, and understand what users really need, then no matter how the outside world changes, you can find your own place.

AI is a tool, and models change, but people don't. In this fast-changing era, what's scarcest is not technology but judgment; not tools but ways of thinking; not speed but direction.

The lights in the café were warm and bright. These young people left with their heads full of ideas and inspiration and walked into the Shanghai night. Some of them may really build a product that changes an industry, some may find their own investing approach in the US market, and some may keep exploring the limits of AI.

But what matters more is that they understood one truth: in the AI era, people are the most important variable.

Recorder's note: This text was compiled from the recording of the Shanghai "Crazy Saturday" offline event on February 7, 2025. For smooth reading, the spoken expressions have been given some literary treatment, but the core views and facts all come from the original recording. Some technical details and figures may deviate due to transcription errors and are for reference only.

Was Fanta Made From Wastewater in WWII? The Dark History of Fanta

2026-01-28T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. In this episode, I want to talk about the story behind Fanta. Yes, that's the fruit-flavored drink you see everywhere in convenience stores, in orange, purple and green, each one a different flavor.

The Fanta brand was actually born in Nazi Germany during World War II.

Opening: December 11, 1941

At 2:30 p.m. Berlin time on December 11, 1941, Max Keith, head of Coca-Cola's German subsidiary, stood by the window of his office at the Essen headquarters, clutching an urgent telegram he had just received. Outside, air-raid sirens were tearing through the skies over the Third Reich.

A few minutes earlier, Adolf Hitler had delivered a fiery speech at the Reichstag, formally declaring war on the United States of America.

For Max Keith, this was effectively a death sentence. He was not just a German. He was the highest representative in Germany of Coca-Cola, a classic American corporate giant.

The Supply Crisis

By now, the transatlantic shipping routes had been completely severed by submarine warfare and blockades, and a huge, precise commercial machine was letting out the wail of something about to grind to a halt.

Over the previous decade, Keith had built a business miracle. But now that the United States and Germany were officially at war, the US government immediately invoked the Trading with the Enemy Act. That meant the Coca-Cola headquarters in Atlanta was strictly forbidden from exporting any materials to Nazi Germany.

Even worse, the mysterious concentrate formula known as "7X," the very soul of Coca-Cola, was cut off completely.

Inside the Essen plant, the huge canning lines now looked painfully bright. This had once been one of the most efficient liquid-canning centers in Europe, and now it had to face the cruel reality of its conveyor belts slowing down. The concentrate stock in the warehouse was running out at a visible rate. Every bottle sent to the front meant an irreversible depletion of supply.

Trouble Inside and Out

Keith knew very well who his enemy was. It wasn't the Allied bombers. It was the SS and the bureaucrats of the Reich Ministry of Economics.

Under Nazi Germany's regulations on enemy property, any asset belonging to an enemy country had to be placed under trusteeship. In practice, that was outright confiscation: a loyal Nazi Party member would be installed to take over the factory, and the machines would be dismantled to make shell casings, or the trucks would be requisitioned for the Eastern Front to haul ammunition.

Keith had 43 bottling plants and more than 2,000 German employees. If the factories stopped, those workers would not only lose their jobs. They would be immediately drafted and sent to the meat grinder of Stalingrad. As for Keith himself, a senior executive who had long worked for an American company and whom the Gestapo suspected of pro-American sympathies, his fate could very well be a concentration camp, charged with treason.

Looking for a Way Out

To save these factories, Keith had to prove to the authorities in Berlin that they did not belong only to Americans. They were an indispensable part of Germany's wartime economy. He needed to keep the production lines running, and keep this enormous commercial machine operating.

But without the 7X concentrate from America, what could he possibly still produce?

Germany's material shortages had reached an outrageous level. Sugar was the most strictly controlled strategic resource. Anything that produced calories went first to the Wehrmacht, and civilians' ration cards allowed a pitiful amount. As for the chemicals needed to make a soft drink, such as citric acid, food coloring and flavorings, they were either diverted to the war industry or depended entirely on imports that had dried up completely.

The technical director even suggested bottling plain water to keep the machines running. Keith rejected this foolish proposal on the spot. In wartime, no one would pay for a bottle of tap water, and the Nazi economics ministry would never allow such a waste of energy and glass bottles.

He needed a new drink. One that didn't require an American formula, didn't consume precious military resources, and could be mass-produced entirely from raw materials already available in Germany.

Inspiration From Garbage

It sounded like a fantasy. In the winter of 1941, Germany didn't even have enough fat to make soap. Where would there be spare raw materials to make a capitalist luxury?

Keith gathered his most trusted technical team. In that conference room, full of despair and tobacco smoke, a crazy idea began to sprout: if all the good raw materials were going to the army, then all that was left was garbage.

The "garbage" here is not a figure of speech. It was literally industrial waste.

At the time, Germany's dairy industry was running at full capacity to supply the army with butter and cheese. Cheese production creates a yellow-green, cloudy liquid by-product called whey. It was usually poured straight into drainage ditches, or used as the lowest-grade pig feed. It had a sour, fermented smell, and although it contained a little lactose and protein, it tasted terrible.

That was the first key ingredient Keith found.

Then they turned their eyes to another forgotten corner: the jam and cider factories. After squeezing every drop of juice out of apples and other fruit, what was left were heaps of brown, sticky fibrous residue. This apple pomace was also usually treated as waste.

Mixing pig-feed-grade whey with pomace that looked like dirt sounds more like a poison from a medieval witch's cauldron than a bestselling drink. But in 1941 Germany, it was the only card Keith had left to play.

Alchemy in the Lab

He gave the technical department strict orders: no matter the method, this mixture had to become drinkable. It was a race against time. Every day, Gestapo officials lingered at the factory gate, and Keith had to fight not only the shortages, but also the greedy eyes inside the Nazi Party that were eyeing this fat piece of meat.

In that frigid winter, in the lab at the Essen factory, chemists began fiddling with these foul-smelling wastes like alchemists. They tried centrifuges to separate the stink from the whey, and tried saccharin to hide the bitterness of the apple pomace. It was really a survival game. If they failed, the factory would be taken over, the machines melted down, and the people sent to the front.

Just as American soldiers in training camps were gulping down the real thing, Coca-Cola, Max Keith, a lone commercial wolf inside Nazi Germany, was preparing to put an unprecedented oddball product on the market in this land scorched by war.

1942: The Birth of a Formula

The winter of 1942 was bitterly cold. The lab at the Essen factory reeked of a nauseating smell, and it didn't come from chemical reagents. It came from rotting fruit and fermented dairy.

Standing in front of chief chemist Dr. Schettlich were two piles that industry considered worthless garbage. On the left were barrels of yellow-green whey, the wastewater from the cheese plants, giving off a strong sour stench. On the right were heaps of brown apple pomace, the fibrous lumps left after the jam factories had squeezed out the juice, which looked more like mud than food.

The first problem was the whey. Though rich in protein, that stubborn cheesy off-smell was fatal to a carbonated drink. The technicians had to design a complicated filtration system, repeatedly boiling the liquid and skimming off the foam, trying to strip away the smell and keep only a faintly sweet base.

But that was just the first step. To add texture and flavor, they had to deal with the apple pomace. The dried, clumped fibers were crushed again and mixed with the treated whey. In the process, they were surprised to discover a remarkable chemical reaction between the two. The whey's protein gave the drink a richness it had never had before, while the pectin in the pomace gave the liquid a cloudy look, similar to murky fruit juice.

Sweetness Crisis

As mentioned last time, after World War II broke out, Coca-Cola's subsidiary in Nazi Germany was soon cut off from its concentrate, which meant it could no longer produce Coca-Cola in Germany. The German subsidiary's manager, Max Keith, had to come up with his own substitute for Coca-Cola so he could keep selling in Nazi Germany, saving his factories from being torn down into scrap and his workers from being sent to the front as cannon fodder. In 1942, they found a new formula that mixed apple pomace and whey to imitate the taste of the drink.

Coca-Cola conquered the world thanks to the perfect balance of sugar and caffeine. But in 1942 Nazi Germany, sugar was a strategic reserve more precious than gold, and the source of caffeine had long been cut off. Without sugar, the liquid of whey and apple pomace tasted like sour soup gone bad.

Keith used every connection he had and finally found the only alternative: saccharin. This was a chemical product refined from coal tar. It was 300 times sweeter than cane sugar, but it had a distinct metallic aftertaste. Although the Nazi authorities strictly regulated food additives, under the pressure of extreme scarcity, it was the only choice.

And so the first samples were mixed. They had a cloudy, dark brown color, with tiny fibrous particles floating on the surface. Compared with the clear, sparkling Coca-Cola in the hands of American GIs, it was practically a counterfeit from hell. But this was not about pursuing quality. It was about surviving in a collapsing empire.

The Chameleon Drink

This drink had one more strange trait: it was a chameleon.

Because wartime supplies were extremely unstable, the formula had to be adjusted constantly. If apple pomace ran out one month, the factory would use pear pulp, or even grape skins. If saccharin ran out, they would try beet extract instead. This meant that the color and taste of every batch leaving the factory was essentially random. Sometimes it was yellow, sometimes brown, and sometimes it even had a purplish tint.

Consumers never knew what they would taste when they opened the next bottle. It was like a mystery blind box.

Naming "Fanta"

Once the samples were finally drinkable, Keith faced one last challenge: giving this oddball a name.

On the day of the decision, the senior executives of the sales team were summoned to a conference room. The atmosphere was heavy and oppressive. Everyone knew that if this new product failed, their next assignment would be a draft notice to the Eastern Front.

Standing at the conference table, Keith loudly urged his dispirited subordinates on. He demanded that everyone use their utmost creativity and come up with a name that sounded loud and clear, and that would be easy to remember by word of mouth. He shouted in German, "Use your imagination!" (In German, the word for "imagination" is pronounced "Fantasie.")

At the very moment this order rang out, Joe Knipp, a veteran salesman sitting in the corner, seemed to be struck by lightning. Whether it was a flash of inspiration in desperation or an instinctive reaction from the subconscious, he blurted out: "Fanta!"

The whole room fell silent at once.

"Fanta" was short, punchy and easy to pronounce, and it had a direct kinship with "Fantasy." It sounded like a magic spell, and also like a cheerful exclamation of delight. Most importantly, it completely stripped away the associations with "trash" and "waste" in the original ingredients, giving the drink a fresh, dreamlike identity.

Keith made the call on the spot: "That's the one!"

Production and Marketing

With the name settled, the machines at the Essen factory roared to life again. This time, though, the conveyor belts were no longer carrying the familiar contour bottles with red logos. Workers pasted new labels onto recycled bottles. They bore a simple two-color design and the brand-new name.

To make the new product stand firm in the market, Keith devised a deceptive marketing strategy. He did not advertise the drink's ingredients. Instead, he emphasized that it was a supplement that provided energy and lifted spirits in wartime. Amplified by the Nazi propaganda machine, this liquid made of whey and pomace transformed into a "symbol of German technological innovation."

In the spring of 1942, when the first truck loaded with Fanta rolled out of the factory gate, Max Keith let out a long sigh of relief. He had succeeded. Through alchemy-like means, he had turned industrial waste into a money-printing machine.

But he could never have imagined that this "illegitimate child" born to answer a wartime crisis, this "oddball" barely surviving under the shadow of Nazi insignia, would one day become the second best-selling beverage brand in the world.

Meanwhile, at the Atlanta headquarters across the ocean, no one knew what was happening here. They only knew that the German subsidiary's books were still miraculously balanced. But they could never have imagined what kind of bizarre liquid that balance was built on.

1943: The Sales Myth

As the production lines ran at full speed, Fanta began to spread like a virus through every corner of the Third Reich. It was not just a drink for quenching thirst. In the years that followed, it would take on even more unexpected roles, becoming an indispensable lifeline on German family dinner tables.

It was 1943. Nazi Germany's war machine was suffering heavy blows in the icy snows of Stalingrad, while on the home front, Max Keith was creating an incredible sales myth.

According to preserved company archives, Fanta's sales in 1943 alone exceeded 3 million cases. That is an astronomical figure. While most German companies were collapsing because their raw materials had run dry, the performance of Coca-Cola's German subsidiary not only failed to shrink, but showed a strange, explosive growth.

Fanta's distinctive ribbed glass bottle almost became the most common consumer product among the ruins of the Third Reich. Its popularity even surpassed the propaganda pamphlets handed out by the Nazi Party.

"Liquid Bread"

But behind this crazy sales volume lay an absurd truth: most German people who bought Fanta at the time did not buy it to drink. They bought it to eat.

As Allied bombing intensified and the sea blockade tightened, Germany's food rationing system was on the verge of collapse. For ordinary housewives, the scarcest commodity was still sugar. Without sugar, you couldn't make jam, you couldn't bake cakes, and you couldn't even get the children to swallow the coarse rye bread.

At this moment, Fanta unexpectedly took on the role of a lifeline. Because the drink contained a high concentration of beet sugar and saccharin, it effectively became a liquid sugar jar.

In kitchens in Berlin, Hamburg and Munich, thrifty German housewives invented a series of astonishing "Fanta recipes." They poured Fanta into stockpots to simmer root vegetables that tasted like wood chips, adding calories and flavor. They even used Fanta to knead dough and make sweet pastries.

For this reason, Fanta earned an ironic nickname at the time: "the liquid bread of the Third Reich."

The Charcoal Trucks

To keep this astonishing supply running, Keith had to face even harsher logistical challenges. By now Germany's gasoline reserves had run completely dry. All fuel had to go first to the armored divisions and fighter planes at the front, and civilian trucks couldn't get a single drop.

To keep the trucks carrying Fanta on the road, Keith showed his cold, technocratic wisdom. He ordered that several hundred of the company's Opel Blitz trucks be converted, fitted with bulky wood-gas generators. This was a primitive and inefficient technology: wood was burned in a boiler at the back of the truck to produce combustible gas that drove the engine.

Although these converted trucks had very weak power, and drivers sometimes had to get out and push them up hills, they miraculously kept Fanta's logistics network running across all of Germany. When the German army's supply lines were stuck in the mud of the Eastern Front, the charcoal-powered trucks, emblazoned with the Coca-Cola logo, still ran on schedule along German roads pockmarked by bombs.

The Dark Human Cost

Yet this commercial miracle was supported not only by charcoal and waste, but also by a much darker human cost.

As the war worsened, nearly all eligible German men were conscripted. The skilled German factory workers at Essen were either captured in the North African deserts or had died on the Russian tundra. The production lines faced an unprecedented labor shortage.

To fill these gaps, Max Keith made a decision that was highly controversial after the war: he began applying to the Nazi authorities for the allocation of forced laborers.

From the second half of 1943, the faces in the Fanta factory changed dramatically. The German workers who had once worn neat uniforms disappeared, replaced by a group of foreign laborers with gaunt faces and terrified eyes. Most of them were French prisoners of war, Polish civilians and Soviet citizens known as "Eastern workers" from occupied territories. Under the strict surveillance of the Gestapo, these people, stripped of their freedom, were forced to work on the production lines day and night.

Every bottle of Fanta sent to German trenches, and every crate of "liquid bread" that German families treasured, was soaked in the sweat and blood of these forced laborers.

Stop DCA-ing into QQQ: This Blended Recipe Is the Ultimate Answer for Ordinary Investors

2026-01-19T00:00:00Z

Opening

This article takes a deep look at holding TQQQ long term and using a 200-day moving average stop-loss to beat the market steadily. It also answers a core question: can the same moving average strategy be applied directly to QQQ, or to the 2x leveraged QLD?

I will answer everything in one place and show the backtest results. At the end, I'll lay out the pros and cons of each, along with an ultimate portfolio strategy that grew 27x in 26 years. This simple strategy may be the ultimate plan for ordinary investors who invest for the long term.

Through real backtest data, we will show that even without picking stocks, you can beat the market with the right investment system.

Disclaimer

First of all, this video is only a personal sharing of trading and strategy. It does not constitute any investment advice.

Backtest Time Span

The backtest runs from March 1999 through the end of 2025. These 26 years are extremely valuable, because they cover several famous crashes in US stock market history:

The 2000 dot-com bubble
The 2008 financial crisis
The 2020 COVID circuit-breaker crash
And the violent rate-hike cycle of 2022

A strategy that can't survive these crises is not one we can stick with over the long run.

Core Logic: SMA200 Trend Filter

The core logic we test is very simple: use the SMA200 with a buffer band to filter out bear market cycles. During bull markets, we use reasonable leverage to amplify returns.

First, a quick explanation of the SMA200. It is the 200-day moving average, which institutions widely recognize as the dividing line between bull and bear markets. But we made one key improvement: instead of using the SMA200 directly as a buy or sell point, we add a buffer band.

Specific rules are as follows:

Buy rule: we only enter when the price is more than 4% above the SMA200
Sell rule: we only exit when the price is more than 3% below the SMA200

Why go to all this trouble?

Let's use a stock called XYZ as an example. Suppose XYZ's SMA200 is $100. Without a buffer band, if the price drops to 99 you sell everything, and if it rises to 101 you buy back in. If the market keeps whipsawing around 100, you get slapped in the face again and again, and your capital is quickly ground away by commissions and slippage.

With a buffer band, however, the buy price becomes 104 and the sell price becomes 97. We only buy when the trend is strong enough to break above 104, and we only sell when the trend has truly broken down below 97. This 7% range is our airbag, designed to filter out meaningless noise.

The logic behind this is simple: catch the big moves, let go of the small ones. We would rather miss a big bounce off the bottom than trust a fake trend. We would rather give back some profit than run too early and only leave when a real bear market arrives.

One more note: in all the tests below, whether QQQ, QLD or TQQQ, we use the 200-day moving average of QQQ as the reference.

QQQ Backtest Results

Let's start with the most basic Nasdaq-100 ETF, the familiar QQQ. We compare two approaches:

First: buy and hold
Second: use the SMA200 filter

Full 26-year backtest

Strategy	Total return	Max drawdown
Buy and hold	1327% (13x)	82.96%
Moving average filter	811% (8x)	48.22%

Yes, the strategy helped you avoid the worst of the 2000 dot-com crash, cutting the drawdown from a near-total 83% down to 48%. But what did you pay for it? Total return shrank by 40%. You gave up a huge slice of the gains, ending up with far less than buy and hold.

Last 15 years

Strategy	Total return	Max drawdown
Buy and hold	1161%	35.12%
Moving average filter	561%	27.64%

The most painful part of this data is that to bring the drawdown down from 35% to 27%, you cut risk by less than 8 points, yet your return was cut in half, falling from 11x to 5x. That insurance premium was far too expensive.

Last 5 years

Strategy	Total return	Max drawdown
Buy and hold	103%	35%
Moving average filter	108%	19%

The backtest shows that although the moving average strategy clearly reduces volatility, for a 1x leveraged QQQ it is a trade with negative expectancy.

Why does this happen?

Because US stocks, the Nasdaq in particular, have a powerful inner ability to recover, and they keep trending up over the long run. When QQQ falls, it usually bounces back in a V-shape over time. With a moving average filter, however, you have to wait for the SMA200 × 1.04 confirmation signal, so you often miss the most violent part of the early rebound. You took fewer hits, but you didn't get to eat any meat. The price of avoiding risk is missing out, giving up huge potential gains.

Conclusion: If you only buy QQQ, forget the moving average filter. Buy and hold is king. Any extra maneuvering is, in the long run, just working for your broker.

QLD Backtest Results (2x leverage)

Now the fun begins. We switch the underlying to the 2x leveraged QLD, and the data changes in a fundamental way.

Full 26-year backtest

Comparison	Value
QLD with moving average filter, total return	3856% (38x)
QQQ held, total return	1327% (13x)
Excess return	Nearly 3x

Look at the risk. People usually assume leverage multiplies risk, but with the help of the 200-day moving average filter, QLD's historical maximum drawdown is 76.45%. That is still high, but note that unleveraged QQQ held outright had a maximum drawdown of 82.96%.

You read that right! By using the 200-day moving average filter to avoid the main down legs of 2000 and 2008, the 2x leveraged product achieved a smaller historical maximum drawdown than the 1x product, and it also earned 25 times more on your principal. That is the power of moving average filtering on leveraged ETFs.

Last 15 years

Metric	QLD with MA filter	QQQ held
Total return	2939%	1161%
Annualized return	25.56%	-
Max drawdown	49.43%	35.12%

Although the strategy's drawdown is higher than holding QQQ directly, earning an extra 1800% in return makes it worth taking on the extra 14% of volatility. This trade is extremely cost-effective.

Last 5 years

Metric	QLD with MA filter	QQQ held
Total return	286%	103%
Max drawdown	35.82%	35.12%

The two are almost identical! That means in this recent high-volatility market, you took on nearly the same risk as QQQ, yet enjoyed close to 3x the return from QLD. This is what we often call the sweet spot.

What does a leveraged ETF fear most? Volatility decay and long bear markets. If QQQ falls 50%, the 2x leveraged QLD would theoretically fall 100%, which means going to zero. But under the protection of the SMA200 filter, this fatal weakness is repaired. During a major bear market, the strategy forces you into cash, cutting the chain of compounding losses that drags on leveraged ETFs.

Conclusion: The SMA200 strategy is a burden on 1x leverage, but on the 2x leveraged QLD it is a necessity. For QLD, this strategy is simply perfect.

TQQQ Backtest Results (3x leverage)

Finally, let's look at the most extreme case: the 3x leveraged TQQQ.

About the data

Before showing the TQQQ data, we need to explain an accuracy issue in the backtest.

After repeated verification, we found that TQQQ's performance in extreme market conditions needs to be treated with special caution. Although the return figures look very tempting, the risk hidden behind them is proportionally large.

According to option pricing models, high-leverage products like TQQQ face significant volatility decay when held over the long term. So holding TQQQ alone has an extremely poor risk-reward ratio, which is why we need to pair it with the moving average filter.

Real data

Full 26-year backtest

TQQQ with the moving average filter, total return: 7952% (roughly 80x)
That is much stronger than the 13x from holding QQQ directly

Last 15 years

Total return: 9521% (95x)
Annualized return: 35.58%
Max drawdown: 65.99%

Looking at returns alone, this is basically a money printer. $10,000 invested 15 years ago would have grown to $960,000, enough to outperform 99.9% of fund managers in the world.

But wait. Before you celebrate the returns, you must look at the risk:

Full-history max drawdown: 90.65%
Even in the strong bull market of the last 15 years, the max drawdown still reached 65.99%

What does a 90% drawdown mean? It means your $1 million in assets shrinks to just $100,000, and then that remaining $100,000 has to grow 9 times just to get back to where you started. In the real world, nobody can endure that kind of despair. Most investors will give up and leave when their assets are cut in half, then cut in half again, and collapse just before dawn.

Conclusion: For ordinary people, TQQQ is a lottery ticket, not an asset. It can make you rich overnight, or leave you with nothing. A full position in TQQQ with a moving average filter may have astonishing theoretical returns, but in practice it is an extreme test of human nature.

The Final Verdict on the Four Strategies

First: QQQ buy and hold (the benchmark)

Annualized return of 10.42%, a middling result
Downside: slow accumulation of wealth. A 13x gain over 26 years looks fine, but in between you have to endure an 83% drawdown
If you entered at the 2000 peak, you would need a full 15 years just to break even. Many ordinary families cannot afford that time cost

Second: QQQ with the moving average filter

The verdict in two words: not worth it
To reduce risk, you sacrificed far too much potential return. The 26-year total shrank from 13x to 8x
For a 1x leveraged asset, using SMA200 timing is an unnecessary complication

Third: QLD with the moving average filter (the best value for money)

A 38x return over 26 years, three times that of holding QQQ
Annualized return of 25.56% over the last 15 years, with risk controlled to a level comparable to QQQ
This is an excellent strategy, but for friends who want to make a leap in social class, it may lack a bit of explosive power

Fourth: TQQQ with the moving average filter (the devil's temptation)

A 95x return over the last 15 years is truly crazy
But a 90.65% max drawdown over the full history is unacceptable
This isn't investing, it's gambling. Putting your entire livelihood on it is like dancing on the edge of a cliff

The Ultimate Solution: Core-Satellite Asset Allocation

So are we at a dead end? QQQ is too slow, the QQQ moving average filter is too timid, the TQQQ moving average filter is too dangerous, and the QLD moving average filter is good but not extreme enough.

The question is: is there a way to combine the stability of QQQ, the high returns of QLD and the explosive power of TQQQ, while keeping risk genuinely within an acceptable range?

The answer is yes. This is the ultimate solution I'm giving you today: core-satellite asset allocation.

The specific recipe

Position	Asset	Strategy	Role
60% (core)	QQQ or VOO	Buy and hold forever	Ballast. It makes sure you ride the tech dividend and never miss a rally, earning the market's average return (β)
30% (satellite)	QLD (2x leverage)	SMA200 moving average filter	Growth engine. It uses QLD's sweet-spot effect to amplify returns while risk stays under control, earning the excess return (Alpha)
10% (gamble)	TQQQ (3x leverage)	SMA200 moving average filter	The lottery ticket. Treat it like buying a call option: if it goes to zero, you only lose 10% of total assets, but if it multiplies 90 times, you can leap into a new class. It earns convexity return (Convexity)

Execution logic

Buy signal: when QQQ's price is above SMA200 × 1.04, the buying window for the satellite and gamble positions officially opens
Sell signal: when QQQ's price falls below SMA200 × 0.97, this 40% of the money must follow discipline strictly and be fully sold into cash
Core position: the 60% core position never moves, no matter what happens

Practical note: To keep the data rigorous, the backtest goes all in the moment a signal appears. In real life you don't need to be so rigid. Once the signal is confirmed, you can choose to dollar-cost in over several periods, or wait for a small pullback and add on dips. Building the position in batches smooths your cost basis and makes the psychological pressure smaller. The key is to finish building the position within the trend-confirmation window, not to agonize over which day's price you got.

Combined Portfolio Backtest Results

Full 26-year backtest

Metric	Portfolio strategy	QQQ benchmark
Total return	2748% (27x)	1327% (13x)
Max drawdown	81.33%	82.96%

The returns have nearly doubled! An initial $10,000 would become $280,000.

On the extreme test, I have to be honest: during the 2000 dot-com crash, this portfolio's maximum drawdown was still as high as 81.33%. Why? Because the 60% QQQ core position fell 83% in that disaster. Systemic risk is something nobody can escape. Even so, it is still better than going all in on QQQ, which hit 82.96%. That proves the cash-out mechanism of the satellite positions did provide real protection in an extreme disaster.

Last 15 years

Metric	Portfolio strategy	QQQ benchmark
Annualized return	24.36%	18.41%
Final assets (starting from $10k)	$260,000	$126,000

Don't underestimate those extra 6 percentage points. After 15 years of compounding, over the same period your assets are more than double what holding QQQ alone would have produced.

Last 5 years (the most counterintuitive data)

Metric	Portfolio strategy	QQQ benchmark
Max drawdown	33.56%	35.12%
Sharpe ratio	0.96	0.74

You added 30% in 2x leverage and another 10% in 3x leverage, yet your max drawdown is smaller than going all in on unleveraged QQQ!

Why is adding leverage safer? Because when the market truly crashes, as in 2022, your 40% satellite positions (QLD plus TQQQ) have already hit their stop-loss and turned into cash. At that point, only the 60% QQQ is taking the beating, and cash does not lose value. It acts as the shock absorber for the entire portfolio. And in bull markets, those 40% leveraged positions provide powerful upside.

This is the real magic of SMA200 timing in asset allocation: bull markets bring explosive power, and bear markets bring a cushion. That is the perfect balance we want.

Why not go all in on QLD?

Someone is bound to ask: buying QLD with a moving average filter returned 38x, higher than this portfolio's 27x, with a similar drawdown. So why not just hold QLD outright?

The answer is simple: if you go all in on QLD, you won't be able to hold it.

Although the maximum drawdowns are similar, QLD's volatility is still far greater than QQQ's. Drawdown is a cold number, but real investing is a severe test of human nature. If you go all in on QLD, the next time a long choppy market arrives and the moving average keeps slapping you, your capital will be ground down again and again, and the panic will make you unable to hold on.

And even if I didn't say it, I don't think the friends watching this would go all in on QLD just because the backtest looks good. You would still keep some of the more stable assets. That is human nature.

In this portfolio, the 60% in QQQ is your anchor. As long as you still believe in the long bull trend of American tech, holding it will keep your heart at ease.

So for friends with smaller accounts, you can go all in to pursue faster growth. But for those with larger net worth, I still recommend this portfolio strategy.

Remember: investing pursues not the highest theoretical return, but the highest return you can actually hold on to.

Why not keep a cash position?

First: the strategy has a built-in dynamic cash pool

Many friends ask: shouldn't I keep some cash to buy the dip? What if it keeps falling?

My answer is this: the 40% satellite portion of this strategy is essentially your dynamic cash. Remember, the core of the SMA200 moving average filter is: fully invested in bull markets, all cash in bear markets.

When the market truly crashes into a bear market, your QLD and TQQQ will all be sold, and your account will automatically hold 40% in a big pile of cash. That 40% is your safe harbor in the bear market, and also the most powerful ammunition you have when the next bull market starts.

So you don't need to keep extra cash. The system automatically keeps you in cash when you should hold cash, and keeps you invested when you should be invested.

Second: avoid cash drag

If you still hold 20% cash in a bull market, waiting for an opportunity, quantitative investing calls this "cash drag." TQQQ's annualized return in bull markets can reach 60% or more. Every dollar you leave sitting idle wastes the dozens-fold compounding that dollar could have delivered.

Since we have already used the SMA200 to control downside risk, when the signal says it's safe, we must push forward at full speed and be fully invested. That is what makes the risk we are taking worthwhile.

Third: logical consistency

We do trend following, not left-side bottom fishing. Holding cash is usually meant for buying more as prices fall. But our strategy is trend following, which is right-side trading. Our logic is: when the price breaks the moving average, the trend has gone bad, so we run rather than buy.

If you still hold cash and use it to average down after the price has broken the moving average, you violate the original purpose of the stop-loss and turn into catching a falling knife. So in this system, cash is there to save your life, not to add to positions.

Fourth: separate investment money from emergency savings

I must give a risk warning here. When I say don't keep cash, I mean the money in this investment account. As for your emergency savings for life (six months of living expenses), please keep them honestly in a bank time deposit or a money market fund. That money must never go into the stock market. Never use your food money to buy TQQQ. That is gambling with your life, not investing.

Summary

Today we verified a counterintuitive truth: for ordinary retail investors, the danger isn't leverage. It's using leverage without a strategy.

This 60% core, 30% satellite and 10% lottery portfolio, backed by 26 years of data, proves its strength:

It earns more than QQQ
It is safer than TQQQ
It doesn't require you to understand stock picking
It doesn't require you to watch the market
It only requires you to follow discipline

This is a very mature and actionable asset allocation plan:

The 60% in QQQ makes sure you never miss out on the national tailwind
The 40% satellite positions provide strong excess return (Alpha), and they automatically switch to cash in a crash, acting as the shock absorber for the whole portfolio

This is the victory of a system. Investing isn't about who is smarter. It's about whose system is more robust.

I hope this strategy provides a useful reference for growing your assets. Remember: investing carries risk, so enter the market with caution.

Notes from the 2026 Year of the Horse Feng Shui and Fortune Lecture

2026-01-18T00:00:00Z

Preface

This lecture took a deep look at the fortune features of the 2026 Year of the Horse, the impact of the fire energy of the Ninth Period on the next 20 years, guidance on the fortunes of each zodiac sign, and practical methods for feng shui layouts. It covered many dimensions, including investment direction, industry trends, choosing a home and placing lucky charms, to give listeners comprehensive feng shui guidance.

1. The Era: The Fire Energy of the Ninth Period (2024–2043)

1.1 What is the Ninth Period?

From 2024 to 2043, we are in the "Ninth Period." The Ninth Period belongs to "fire," and the color of fire is red. This means that over the next 20 years, things related to "fire" and "red" will be especially vibrant.

"From 2024 to 2043 is the Ninth Period, and the Ninth Period is a fire period. Fire, right? What color is fire? Red. So for investments, you must go with HSBC, because the HSBC logo is red." Segment 1 00:02:39

1.2 Forecast for the Nation's Fortune

The Chinese national flag is red, so starting in 2024, China's technology will rise to a new level, and the nation's fortune over the next 20 years will be very good.

"What color is China's national flag? Red. So starting in 2024, China's technology will advance to a new level, and the next 20 years will all be very good." Segment 1 00:02:39

1.3 Favorable Industries

Under the fire energy of the Ninth Period, the following industries will enter a golden age of development:

Artificial intelligence (AI)
New energy
Medicine and medical equipment
Elderly care
Pharmaceuticals and cosmetics
Virtual industries (VR, blockchain, the metaverse and other things you "can see but can't touch")
Film and entertainment

"What industries are best for you all? AI, new energy, and medicine and medical care, and elderly care. Virtual things, blockchain, VR, things you can see but not touch, those are all good." Segment 1 00:05:36

2. Feng Shui Basics

2.1 What is feng shui?

The lecture broke feng shui down into three understandable layers:

(1) Environmental science

Do you like living in a place with plenty of sunlight, clean and bright? Of course you do. That's how the environment affects people: a place bathed in sunlight naturally has better feng shui.

"Feng shui is actually very simple. Would you like to live in a place that is very dirty and dark? Definitely not. So a place where sunlight floods in and the view is pleasant has better feng shui a bit. That is environmental science." Segment 1 00:07:26

(2) Energy-field science (magnetic field matching)

Just as some plants like water and others like dryness, a person's birth year, month, day and hour are like a "seed" that needs to find the right "soil" and "climate."

"Some plants suit dry conditions, and some need a lot of water. Soil cultivation and hydroponics are different. If you get it backwards, the plant will rot. The year, month, day and hour you were born are the seed; you need to find the right magnetic field for it." Segment 1 00:08:10

(3) Psychology

The height and width of a space affect people's moods. For example, a 2.1-meter ceiling height makes people feel oppressed, while a tall lobby makes them feel comfortable.

"Look at that three-way overpass outside the window: it looks uncomfortable and unpleasant, and it affects your mood. This lobby has very high ceilings, so don't you feel comfortable? If the ceiling were only 2.1 meters, would you be happy? No. So when you buy a home, buy one with a higher ceiling. That's psychology." Segment 1 00:09:18

2.2 Why do Chinese houses "face south" (sit north, face south)?

This is a big-data conclusion built up over a thousand years of experience:

Best natural light: In the Northern Hemisphere, a house that sits north and faces south gets the most sunshine throughout the year
Shelter from the wind and cold: In winter, the northwest wind blows, and a tall building behind the house can block the cold wind
Ancient needs: Without electric lights in ancient times, people relied on sunlight, so facing south gave the best light

"China is in the Northern Hemisphere, and a house that sits north and faces south sees the most sunlight on average. More than 1,000 years ago there were no electric lights, so people relied on sunlight coming in. Another reason is that in winter the northwest wind blows, and a high wall at the back blocks it, so when you buy a home, try to choose one facing south." Segment 1 00:12:28

Special case: In Chongqing, where it is cloudy year-round, local people don't care much about orientation when buying a home, because there is little sunlight to begin with.

3. Zodiac Fortunes for the 2026 Year of the Horse

3.1 Basics of the zodiac

How the zodiac is calculated

The zodiac sign is not calculated from the first day of the first lunar month. It is calculated from Lichun (the start of spring).

"What zodiac sign is someone born on February 6, 2026? A Snake! The first day of the Lunar New Year is February 17. The zodiac starts from Lichun: only babies born after 3:17 a.m. on February 4, 2026, are born in the Year of the Horse." Segment 1 00:02:39

Important reminder: If you were born in the 1970s and your birthday falls around Lichun, you need to double-check your zodiac sign.

3.2 Zodiac signs affected by Tai Sui (needing special attention)

🐴 Horse (on Tai Sui)

Fortune features:

✅ Heavier workload and more responsibility
✅ Bosses, supervisors and clients will give you more tasks
⚠️ Prone to injury

Things to note:

Avoid high-risk sports: skiing, diving, horseback riding, skydiving, small aircraft
Pay attention to travel safety, especially when taking photos at cliffs and precipice edges

"Horse people, your workload will increase and your responsibility will be greater. But take care of your safety, because you're prone to injury. Don't ski, dive, ride horses or skydive; try to avoid all high-risk sports." Segment 3 00:14:30

🐭 Rat (clashing Tai Sui)

Fortune features:

If 2025 was not good, 2026 will bounce back
Rat people born in autumn or winter will see their wealth luck improve
⚠️ But don't chase oversized investment projects; be steady

Ways to resolve it:

Move the furniture at home (for example, buy a new sofa or adjust where the furniture sits)
Don't criticize your partner too much, as it can trigger impulsive reactions
In 2026, your partner may lose their temper more easily than usual

🐮 Ox (harming Tai Sui)

Fortune features:

✅ Good luck, and success comes easily
⚠️ But you may attract others' envy, slander and backstabbing

Strategy: Make your fortune quietly, keep a low profile, and don't brag about your achievements everywhere.

🐯 Tiger (punishing Tai Sui)

Fortune features: Prone to injury. Watch out for traffic safety. If someone picks a fight with you, give way and don't confront them head-on.

Ways to resolve it: Work on projects and consult on decisions with a noble person born in the Year of the Dog or the Year of the Pig. You can borrow their good luck.

🐰 Rabbit (breaking Tai Sui)

Advice: If you have a big project or a big idea, make it real first before telling your friends, so that you avoid getting cold water poured on it or being disturbed. Do it first; once it's done, then post about it on social media.

3.3 Fortunes of other zodiac signs

🐉 Dragon

Fortune rises and falls a lot, with both good and bad. Business trips bring in money: "one horse brings wealth." But you are prone to illness, and too much wealth weakens the body. Travel more for business, and after you earn money, donate some to charity.

🐍 Snake

Look for noble people. Prone to illness, and too much wealth weakens the body. After you earn money, it's advisable to donate to charity.

🐑 Goat (strong peach blossom luck)

✅ Likely to have a baby, especially a boy. ✅ If you are not having children, work will be very busy. ✅ Single people will easily find a partner. If you have plans for a baby, it's best to "start work" before the 31st day of the third lunar month.

🐵 Monkey (excellent academic luck)

✅ Children do well in exams (postgraduate entrance and school admissions). ✅ People who write copy or do design will have lots of inspiration and clear thinking. You'll need to keep traveling for work, so don't be too idle.

🐔 Rooster (peach blossom year)

✅ Heavenly romance is stirring, and it's a marriage year. If you already have a boyfriend or girlfriend, this year is suitable for getting married. Singles should actively go on blind dates (ask friends to introduce you, and don't spend much money on dating platforms).

🐶 Dog (strong wealth luck)

✅ Make a fortune and get promoted. ✅ Learn new skills (AI, painting, etc.). ⚠️ But you'll have many minor illnesses, so avoid places with heavy yin energy (quiet museums, mountain caves and caverns).

🐷 Pig (strong noble-person luck)

✅ Work goes smoothly. Employees will be promoted by their bosses, and business owners will be introduced to new clients by existing ones. ⚠️ But there will be small financial losses. It's advisable to put the losses to use in investments (buy gold, invest in children's education).

4. Practical Feng Shui Layouts

4.1 Doorway direction and carpet color for 2026

Based on the direction the front door faces, placing a carpet of the matching color can improve your fortune:

Door facing east: Red carpet → good wealth luck, easier to conceive
Door facing south: Black, gray or blue → use this if you want a boy
Door facing west: Keep a low profile → prone to petty people and marital quarrels, so keep low-key
Door facing north: Coffee-colored → stable fortune
Door facing southeast: Red or green → strong peach blossom luck, single people find partners easily
Door facing southwest: Black, gray or blue → manage your wallet and control spending

"If you want to make money, go check which direction your front door faces. If it faces east, put down a red carpet. This year your wealth luck will be good, and it'll be easier to get pregnant and have a child." Segment 3 00:09:57

4.2 Zodiac lucky charms (six-harmony noble people)

Each zodiac sign has a corresponding "six-harmony noble person" sign. Carrying the matching lucky charm can boost your fortune:

🐭 Rat → Dragon
🐮 Ox → Rooster
🐯 Tiger → Dog
🐰 Rabbit → Dog
🐉 Dragon → Rooster
🐍 Snake → Rooster
🐴 Horse → Goat
🐑 Goat → Pig
🐵 Monkey → Dragon
🐔 Rooster → Dragon
🐶 Dog → Tiger
🐷 Pig → Goat

How to wear it: It can be a pendant, bracelet or necklace. Just carry it with you.

4.3 Choosing the material of the lucky charm (based on birth month)

Choose the right material based on the solar month of birth:

March 6 – May 6: Gold, white gold, green jade
May 6 – August 8: Gold, silver, white jade
August 8 – March 6: Wood, jadeite, ruby, amethyst, agate

4.4 Placing plants in the office or at home

❌ Taboo plants: cactus (spiky), aloe, azalea, bougainvillea

✅ Fine to place: lucky bamboo, fresh flowers, dragon fruit potted plants, chili potted plants, tomato potted plants (red things; the fire energy of the Ninth Period is strong)

"Don't keep cactus at home or in the office. Spiky things are bad and tend to cause gossip and quarrels. You can grow dragon fruit, chili and tomatoes, these red things. In the Ninth Period, it's very good to have everything red and bright." Segment 3 00:42:23

4.5 Placing feng shui ornaments

How to place a dragon turtle

Core principle: Head facing inward (toward the office desk or the inside of the room), not outward (not toward the door or window). This is how you draw wealth energy in.

How to place an elephant

Correct way: Buy an elephant with its trunk pointing down (to draw in wealth). If you have a pair, both trunks should point down.

❌ Wrong way: An elephant with its trunk pointing outward or open does nothing, and your wealth will leak out.

4.6 Animals not to keep as pets

❌ Not recommended: birds (parrots, etc.), fish, turtles

Reason: Keeping them confined deprives them of freedom and space, which is "a crime" and bad for feng shui.

✅ Fine to keep: cats and dogs (they are companion animals; you walk the dog and let it move freely)

5. Choosing a Home and Feng Shui

5.1 Common misconceptions about choosing a floor

Common online misconception: believing a certain number (such as the 8th or 18th floor) has especially good feng shui, or believing low floors are "grounded" while high floors lack "earth energy."

Correct understanding:

The floor number is psychology, and each region has different habits (in Hong Kong people like the 8th, 18th and 28th floors, while in Shanghai people avoid the 18th and 4th floors)
Heaven, earth and humanity: Ancient feng shui said "earth energy" because buildings only had three floors. Modern high-rises have 30 to 40 floors, so there is not just "earth energy" but also "sky energy."

5.2 The right way to choose a floor

Core principle: Look at the external environment, not the number

Step 1: Look at the external view

If the 6th floor faces a sharp corner outside or an overpass points at the window → not good, switch to the 8th floor
If the 8th floor faces a mountain grave outside → not good, choose another floor

Step 2: Consider natural light

Older communities (narrow spacing between buildings) → choose a middle to high floor
Newer communities with wide spacing → low floors are fine

Advantages of middle to high floors: more sunlight and more yang energy. Middle to low floors with close spacing between buildings get poor light and heavy yin energy.

5.3 Floor layouts to avoid

❌ Door facing door (drafty "through-hall sha"): the front door faces the balcony door or a window, creating a "through-hall sha" that lets wealth energy flow straight out
❌ Missing corners: an irregular floor plan with recesses affects the family's fortune
❌ Poor door orientation: see "Doorway direction and carpet color" above

5.4 Surroundings to avoid

❌ Surroundings you should not buy into:

Too close to a hospital (lots of bacteria and viruses)
Right next to an elevated bridge or expressway (heavy sha energy, noisy, and it affects mood and decisions)
Facing a chimney

6. Auspicious Start Dates and Times

6.1 The best day to start work in 2026

Best date: the 9th day of the first lunar month (Zhengyue chu jiu)

Best time: 11:00 – 13:00 (the noon hour)

If you're a salaried worker and have to be at the office by 9 a.m.:

Check in at the office at 9 a.m.
Between 11 and 13 o'clock, turn on your computer, call clients and send emails

6.2 Why starting work matters

Psychological principle: If your first day at work goes smoothly and you're in a good mood, you'll do things and deal with people with a smile, and the whole year will go well.

7. Mindset for Success and Relationships

7.1 Giving and reward

Successful people and companies all give first and receive rewards later.

Case study: WeChat's free strategy: WeChat doesn't charge. It gives everyone free use first. After people get used to it and can't do without it, it starts to monetize later.

7.2 Looks and fortune

Double eyelids vs. single eyelids

People with double eyelids: The eyes are the windows to the soul. Double eyelids look friendly, cute and approachable.

People with single eyelids: If you want to improve your fortune, it's recommended to get double-eyelid surgery (or use double-eyelid tape). Jackie Chan and Louis Koo have both had double-eyelid surgery.

The importance of hairstyle

Advice for men: Don't let your hair cover your forehead. Look spirited and show positive energy.

Advice for women: Grow your hair long (at least to the shoulders). Don't wear short hair or let your hair cover your forehead.

Nose

A big nose = good wealth luck. Don't touch your nose. A big enough nose is a good thing.

7.3 Energy fields and relationships

Energy is contagious: If the people around you have good luck, and you spend a lot of time with them, their luck will influence you.

Advice: Go to positive-energy gatherings, and stay away from people who complain about negative things every day.

7.4 Everyday ways to lift your fortune

When your fortune is poor, you're unlucky or you feel gloomy:

Go on a spring outing in the suburbs
Sit in the park and soak up the sun
Get in touch with plants and trees

8. The Special Background of the Red Horse, Red Sheep Year

8.1 What is the Red Horse, Red Sheep year?

Cycle: once every 60 years

Characteristics: Everywhere in the world is unsettled. People are more likely to argue and fight, and important figures will change.

8.2 Investment advice

Investment strategy for 2026–2027:

❌ Don't invest aggressively
✅ Preserve capital and avoid risk
✅ Leave your money to professional institutions to manage

"The overall environment in 2026 isn't that easy. Preserve capital first, avoid risk, and leave your money to HSBC to manage." Segment 3 00:57:03

9. Selected Q&A

Q1: My house faces north, the door is on the north side, and the balcony is on the south side. What should I do?

Video clip Segment 3 00:46:10

Answer: This is "through-hall sha": door facing door, and wealth energy will pass straight through. You should have a feng shui master come to the site to look at it, as the layout may need to be adjusted.

Q2: Should the lucky charm be a plush toy or made of gold or silver?

Video clip Segment 3 00:52:52

Answer: Choose the material based on the birth month (see "Choosing the material of the lucky charm" above). If pure gold is too expensive, gold plating works too, or you can buy jade.

Q3: Do feng shui ornaments need to be changed every year?

Video clip Segment 3 00:51:06

Answer: No need to change them. They can stay in place for 20 years or even 100 years. But:

The placement direction may change each year
The lucky charm (six-harmony noble person) does not change; it is fixed

Q4: My son is a Dragon (born 1988), and my girlfriend is a Horse (born 1990). When is a good time to get married?

Video clip Segment 3 00:52:52

Answer:

Dragon and Horse, a very good combination, "dragon-horse spirit"
You can get married in the Year of the Horse, and it "brings joy and wards off three disasters"
Best months: the 8th and 10th lunar months

Q5: My 7-year-old son has a high, full forehead, and he has bangs covering it. Is that necessary?

Answer: Segment 3 01:00:53

✅ Don't cover the forehead!
A high, full forehead means very smart and strong drive to get ahead
This is the same forehead Li Ka-shing had (Li Ka-shing also had this forehead when he was young)
He may be mischievous and strong-willed, so don't force him, and give him plenty of room
He will achieve a lot before age 30

10. Summary and Key Points

Core Takeaways

The Ninth Period of fire (2024–2043): Over the next 20 years, AI, new energy, medicine, elderly care and virtual industries will boom
2026, Year of the Horse: Zodiac signs that clash with Tai Sui (Horse, Rat, Ox, Tiger, Rabbit) need special attention, but there are ways to resolve it
Feng shui isn't mysterious: it is environmental science + energy-field science + psychology
Give first, receive later: successful people all give first
Energy is contagious: spend time with positive people who have good luck
Red Horse, Red Sheep year: the overall environment in 2026–2027 is unstable, so invest conservatively

Things to do right away

✅ Do these as soon as you get home today

Check which direction your front door faces, and lay down the matching color carpet
Buy the six-harmony lucky charm for your zodiac sign
Don't keep cactus in the office; replace it with a dragon fruit or chili potted plant
Check whether you have any elephant ornaments with trunks pointing outward, and replace them with ones with trunks pointing down
If you're in a Tai Sui year, move the furniture or buy a new sofa

✅ Near-term plans

If you're a Rooster, consider getting married this year
If you're a Goat and want to have a baby, "start work" before the 31st of March
Start work on the 9th day of the first lunar month (11:00–13:00)

✅ Long-term habits

Go to positive-energy gatherings
Stay away from people who complain and bring negative energy
Get out in the sun and spend time in nature more often

Note: The above content is for reference only and does not constitute investment advice. Invest with caution, and make rational decisions based on your own situation.

How the Nobel Economics Prize Cracked the Secret of the Technology Explosion

2025-12-28T00:00:00Z

Hi everyone, welcome to this episode of Cold-Knowledge Blind Box.

In the sci-fi novel The Three-Body Problem, one of the premises for the dark forest to hold is the "technology explosion." The idea is that within a very short time, a civilization can do what Earth did during the Industrial Revolution: in roughly 300 years, it creates the scientific and material wealth of the previous 100,000 years and moves toward a stage where everyone has enough to eat and is free of poverty and hunger.

Homo sapiens has been on Earth for about 300,000 years. For 290,000 of those years, the way humans lived was basically a flat, lifeless line. Whether you were a blacksmith in ancient Egypt, a stonemason in ancient Rome, or an ordinary person in China during the Tang, Song, Yuan, Ming or Qing dynasties, what you ate, what you wore and how long you lived didn't differ all that much.

In other words, "being poor" was a shared human experience that crossed civilizations and dynasties.

Now imagine you are a person from ancient Rome, a time machine picks you up, and you get dropped among the peasants of a 15th-century European town. You'd find that you've just traded one kind of poverty for another. It's nothing like those web novels where the time-traveler gets a happy, cheat-powered life in the future.

Then the truly wild part arrives. Around 250 years ago, global GDP data suddenly shoots up like a rocket. Economists call it the famous "hockey stick curve." In just over 200 years, the wealth and technological breakthroughs humanity created exceeded the total of all of history before that. The share of people living in extreme poverty dropped from about 80% to roughly 10% today.

So look at it this way: you and I, and the generations around us, are among the first in history to be born by default into an era of sustained growth. That sounds great.

But I think the question has only just begun: why us, these last few hundred years? Why did the rocket take off right around 250 years ago?

You might say: isn't that easy to explain? The Industrial Revolution, steam engines. We all learned that in middle school. What's so mysterious about it?

That's not wrong, but imagine it like a suspense movie. We know the man was shot. The real question is: who pulled the trigger? Why did they do it? And how did the gun end up in their hands?

You have to understand that technological innovation has always existed. Take the ancient Romans: they built aqueducts and made concrete, and that engineering still looks astonishing by today's standards. Then there are the pyramids of ancient Egypt, and even today nobody fully knows how they were built. And China's Four Great Inventions completely redirected the course of human civilization.

But why didn't these astonishingly brilliant inventions lead our ancestors to collectively live the lives of the modern middle class?

The answer is pretty brutal. Economic historians gave it a name: the "Malthusian trap."

In plain terms: before the Industrial Revolution, whenever our technology improved even a little, say we squeezed out a bit more grain, people didn't have time to get better off before they had more children. With more mouths, the resources per person got spread thin again, and after the loop closed, everyone was back at the edge of barely being able to eat.

It's like working hard until you finally get a 30% raise, and then your monthly expenses change: the mortgage goes up a bit, your kid signs up for two more interest classes... and in the end, you're left with only 2,000 bucks a month again.

So now you see what I really want to figure out: 250 years ago, what did humanity do right to collectively "break out" of the Malthusian trap that had held us for hundreds of thousands of years? How did we discover the secret of sustained prosperity?

This is no longer a "mystery of the ages." It's a "300,000-year mystery."

I'll tell you right now: you're lucky again, because this answer was solved in 2025, and the people who solved it won the 2025 Nobel Prize in Economics. So they are three economic "Sherlock Holmeses," and that's a pretty fitting way to describe them. They are Joel Mokyr, Philippe Aghion and Peter Howitt.

Over this 300,000-year history of growth, the "crime scene" was examined in different ways. Some people, like historical detectives, walked back through the scene, went through dozens or hundreds of years of records, and finally found that first spark. Some people, like engineers, took apart the mysterious engine that set off the growth, drew its internal structure, and wrote a user manual. And some people packaged that manual into a toolkit of modern policy, telling governments how to use the engine.

So let's follow the first detective, Joel Mokyr, back to the crime scene to find the spark that lit everything.

What Mokyr spent his life doing was, in short, studying economic history. He realized that if you want to understand the explosion of the Industrial Revolution, you first have to imagine the world before the explosion: what was that quiet state like? Why did it last for hundreds of thousands of years?

He described that world with a very vivid phrase: "a world of engineering without mechanics."

What does that mean? A blacksmith could forge iron, but he didn't understand metallurgy. A farmer could grow crops, but he didn't understand soil science. A doctor could treat patients, but he didn't understand microbiology. Everyone knew "this method works," but very few knew "why you do it this way."

Mokyr separated these two kinds of knowledge very clearly:

One is "prescriptive knowledge": the kind of thing where "just follow the steps and you'll be fine," like a recipe, or the craft secrets passed from master to apprentice, or the trade secrets of a workshop. This is the world of craftsmen, farmers and artisans.
The other is "propositional knowledge": explanations of why the world works the way it does, a summary of the underlying laws. This is the world of scientists and philosophers.

So for the long years before the Industrial Revolution, these two groups basically lived in parallel universes that didn't touch: philosophers thought getting their hands dirty was dirty, grunt work, and didn't bother to step into a workshop; and craftsmen couldn't make sense of papers and theories full of symbols.

As a result, what did innovation rely on? Only experience, luck and trial and error. Say a blacksmith didn't get the heat right one day, but by accident he discovered that "quenching makes this steel harder." Fine, he writes it down and does it the same way next time, and that counts as an innovation. But because he had no idea why it got harder, he had no way to systematically improve his craft or keep optimizing it. So innovation back then tended to "jump" once and then stay in place.

So where was the turning point? Mokyr believed the key period was the Enlightenment in 17th- and 18th-century Europe.

When people hear "the Enlightenment," they usually think of big words like "reason, freedom, individual rights." But in Mokyr's view, it had a very concrete byproduct: it finally got these two groups that had never talked to each other, the people who asked "why" and the people who handled "how to do it," to sit down at the same table.

If you want the most typical example of that "same table," it's probably a mysterious group in Birmingham, England, called the Lunar Society, or the "Moonlight Society."

Picture it: in late 18th-century England, on the night of every full moon, a group of the top tech nerds and academic eccentrics of the time would gather near Birmingham. They traveled by moonlight, which is why they were called the "Moonlight Society." They also joked that they were a bunch of "lunatics."

And who were these lunatics?

James Watt, the engineer who improved the steam engine;
Matthew Boulton, who commercialized Watt's technology and ran the factories;
Erasmus Darwin, grandfather of Charles Darwin, a naturalist and physician;
Josiah Wedgwood, the "father of English pottery";
And a whole group of chemists, doctors and natural scientists.

What a lineup. You could call it the "18th-century version of the Avengers of science."

At this table sat the people who knew the "why" of the most cutting-edge scientific theory, and also the practical doers who mastered the most advanced production techniques and knew how to make money, the "how to do it" people. They ate and argued fiercely: How do you use the latest chemistry to improve pottery glazes? How do you use new physics to raise the efficiency of steam engines? How do you design canals to cut transport costs? They even talked about education and political reform.

This is what Mokyr called "a real-world experimental ground where knowledge undergoes chemical reactions." When the "why" line and the "how to do it" line finally twisted into one rope, a positive feedback flywheel started turning: scientific theory began guiding technological innovation, technology ran into new problems in practice, and those problems in turn forced science to upgrade its theories.

Once this flywheel starts spinning, it's very hard to stop. It went from relying on one genius's flash of inspiration to a system that could accelerate itself and keep iterating.

But note that this system didn't appear out of thin air. It needed a special social soil: one that tolerated failed experiments, allowed people to question authority, and didn't rush to burn new ideas as heresy. That's why England and the Netherlands were able to take off first. To a large extent, it's because at that point in time they happened to have exactly this kind of soil.

That is the spark Mokyr helped us find.

Okay, so we've roughly found the spark that ignited growth. But that's only the first half of the story.

Plenty of "spark-flash" moments happened in history: ancient Greek philosophy and mathematics, or Song-dynasty China's technology and commerce. They were all dazzling for a while, but most of them were fleeting. They flew for a bit, then slowly slid back to where they started.

So the question is: why didn't this spark go out this time? Why did it ignite an engine that could roar along for more than 200 years?

That's where the two "engineer-type" detectives come in, Aghion and Howitt.

If Mokyr's work was "finding the first scene of the crime," then what Aghion and Howitt did was take that scene apart and use math and models to tell you exactly how this thing actually runs.

The core secret of this engine comes down to five words: "creative destruction."

Alright, that's where we'll stop for this episode of Cold-Knowledge Blind Box. Next time, we'll talk about where this "creative destruction" came from.

Hi everyone, welcome to this episode of Cold-Knowledge Blind Box. Last time we talked about how the 2025 Nobel economists found the secret behind the human economic explosion of the past three centuries. They are Mokyr, Aghion and Howitt.

Mokyr's contribution was "finding the first scene of the crime," which is the "real-world experimental ground where knowledge undergoes chemical reactions." When the "why" line and the "how to do it" line finally twisted into one rope, a positive feedback flywheel started turning: scientific theory began guiding technological innovation, technology ran into new problems in practice, and those problems in turn forced science to upgrade its theories.

Aghion and Howitt took that crime scene apart and used math and models to tell you exactly how this thing actually runs.

The core secret of this engine comes down to five words: "creative destruction."

In fact, they didn't invent the term. It was first proposed by the economist Joseph Schumpeter. But what Aghion and Howitt contributed was turning this poetic intuition into a rigorous framework that could be calculated and used for prediction.

So what does "creative destruction" mean? In plain language, every real technological breakthrough is a "tear it down and rebuild" rather than "add a brick here and there." It creates new products, new industries and new models, and at the same time it inevitably destroys everything old.

This sounds abstract, right? Let's start with a few examples you'll understand right away:

Do you remember the cabinet at home where you used to stack DVDs and even VHS tapes? In the 1990s and early 2000s, a company called Blockbuster was the undisputed "king of video entertainment." It had more than 9,000 stores around the world and a market value of several billion dollars. Renting discs on the weekend to watch movies was a ritual for many families.

Then a small company called Netflix appeared. At first it only did a niche business: mailing DVDs. Through trial and error, it gradually turned into today's online video streaming. You know what happened next: Blockbuster filed for bankruptcy in 2010, and the blue signs that once lined street corners now survive in just one "living fossil" store. Netflix's creation, meanwhile, uprooted the entire DVD rental industry.

Now let's look further back. When cars were first invented, who was the happiest? Certainly not the carriage drivers. Every Ford Model T on the road meant one fewer carriage driver with a fare. The efficiency and convenience of cars steadily destroyed thousands of years of the carriage industry: carriage makers, harness craftsmen, stable owners, and even the workers who cleared away horse dung were all wiped out.

You don't even have to look that far back. Over the past 20 years, we've watched feature phones get wiped out by smartphones. Nokia went from "forever king" to a "nostalgic display" in the corner of a store counter. Video arcades, internet cafes and traditional department stores were nibbled away piece by piece by e-commerce, short videos and mobile games.

From a macro perspective, this is called "improved productivity" and "more choice for consumers." But for an ordinary person in their forties who could only repair feature phones or stack DVD shelves, this process comes down to one word: "unemployment."

In Aghion and Howitt's model, every technological revolution like this doesn't just add a bit of GDP to the world. It also hands a certain group of people a cold "notice of unemployment."

It sounds cruel, right? But that's their conclusion: if you want long-term economic growth, this is the only path. If an economy doesn't allow destruction, it also can't achieve real creation. Such an economy is like a stagnant pond: on the surface, everyone seems well protected, and no old species is being eliminated from the water, but the price is that it can never evolve new species.

So since destruction can't be avoided, the next big question is: how should we regulate this engine? Should we let it grow wild, or should we hit the brakes hard?

Here, Aghion and Howitt proposed a second, very key insight: they found that there is an "inverted U-shaped" relationship between innovation and the market.

Imagine an upside-down U-shaped curve in your head:

At the far left of the curve is "monopoly," where competition is weakest. Picture a market with only one big boss, like a dominant state-owned enterprise or a nearly monopolistic tech giant. Does it have any motivation to innovate? Not really, because it can make money just by lying there. Any big innovation could overturn its comfortable life.
At the far right of the curve is "perfect competition," or you could think of it as a market where "everyone sells cabbage at rock-bottom prices." Say you open a small restaurant, and you finally come up with a "signature dish." The next day, the restaurants next door all copy it and make it even cheaper. Would you still be willing to put effort into researching new dishes? You'd probably have no mood for it: "Why would I research something just for others to copy?"
So when competition becomes so fierce that profits are as thin as paper and no innovation can keep its rewards, naturally nobody wants to spend money and time on innovation.

So where is the most vigorous innovation? At the top of this inverted U-shaped curve. It's not zero competition, and it's not a life-or-death brawl. The best state is "moderate competition." You can imagine it as: "You have a little lead in your hands, but someone is watching behind you, so you don't dare to lie back and relax. Yet you also have enough profit to invest in R&D."

After Aghion and Howitt drew this graph, they gave governments a very clear role: the government's job is to keep the market honestly sitting in the middle of this inverted U-shaped curve.

And how do you do that? By relying on two things: an "accelerator" and a "brake."

What's the accelerator? It's patents and intellectual property protection. It tells innovators: go ahead and take the risk. If you really do come up with something, I will allow you a period of "legal monopoly," so you can recover your R&D costs and earn a large sum on top. This is the reward for "creation."

A very typical example of this era is Nvidia. It spent more than a decade and huge amounts of money and engineering talent building CUDA, an ecosystem that tightly integrates software and hardware. At the time, it was a huge gamble, betting on an architecture nobody could guarantee would "definitely win." But once it paid off, the ecosystem became its exclusive moat. In AI computing, it gained a near-monopoly position, and that was followed by a profit explosion that comes once in decades.

That's the logic of the accelerator: if you dare to gamble, and you win, you get to enjoy a period of excess returns.

But here's the problem: if you keep stepping on the accelerator, and a company gets so fat with profit that it starts deliberately blocking others' way, what then?

That's when you need the other hand: the "brake," meaning antitrust and competition policy. The brake isn't saying "you're too successful, you can't make money." Instead, it tells giants who have become "road hegemons": you can enjoy the fruits of your lead, but you can't use your advantage to block the road for those behind you.

So you may have seen many news stories recently: huge fines on big tech companies, orders to spin off certain businesses, or blocking certain mergers and acquisitions. Essentially, they're all doing the same thing: pulling that "inverted U-shaped curve" back toward the middle, so the market doesn't dive into the "oligopoly" end.

So within this framework, a dynamic economy is one that carefully "walks a tightrope" between "encouraging innovation" and "encouraging competition." Walk it well, and the engine can roar smoothly forward. Walk it badly, and it either stalls or dies.

So now we have two key keys in hand:

Mokyr's key tells us that the spark of continuous innovation comes from the deep fusion of "theoretical knowledge" and "practical knowledge";
Aghion and Howitt's key gives us the structural diagram of the growth engine: "creative destruction," along with the balancing act between "the brake" and "the accelerator."

With these, we've basically laid out the underlying logic of how humanity's wealth surged over the past 250 years.

But now the times have changed again: will this user manual, born in the industrial age and perfected in the information age, still work when faced with AI, this new species?

The answer actually goes against your intuition. Not only is it not outdated, it has become more important than ever.

Because AI pushes both of these forces, "knowledge fusion" and "creative destruction," to the extreme.

Let's look at Mokyr's line first: knowledge fusion.

If the Industrial Revolution was an era when scientists and craftsmen finally sat at the same table, then what is AI doing? It has built its own table, locked "why" and "how to do it" together in a small black room, and let them talk nonstop for 24 hours a day: translating theory into executable "prescriptions," even digging new patterns out of data that humans hadn't noticed, and turning those back into new "propositional knowledge."

At the frontier of science, the intensity is even more extreme. Take AlphaFold, which predicts protein structures. It takes in the sequences and structures of hundreds of thousands of known proteins, and outputs high-precision predictions for hundreds of millions of unknown proteins. You can picture AI as a "tireless knowledge-fusion machine." From this angle, the growth it may bring could very well be an amplified version of the Industrial Revolution, many times over.

But at this point the risk also arrives: remember our second theory? "Creative destruction."

On this line, the power of AI is just as unprecedented. This time, what gets drawn into "creative destruction" isn't only the distant carriage drivers or the owners of long-gone DVD stores, but the people around us, and even ourselves. White-collar workers and knowledge workers, who used to be considered "iron rice bowls"...

Some international organizations have already released data that is quite direct:

How the Blue LED Was Invented, and the God of Staying Up Late

2025-11-26T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. This episode is about how the blue LED was hammered into existence. Without blue light, there's no three primary colors, and without those, none of the screens that keep us up late would exist.

The story of Nakamura Shuji is very interesting.

How do you sum up this "almost magical" invention of his?

There's a famous picture online:

Red LEDs are easy. Green LEDs are easy. Blue LEDs are "almost impossible."

Have you ever wondered about this?

Why do we have so many lights and so many monitors today, and how did they become possible?

LEDs were invented in the 1950s. So why didn't we leap straight from then to today, with LED big screens and LED lightbulbs everywhere?

This story is really about the blue LED.

Because we all know that light has three primary colors:

If you want to make white light, and white light is the most natural kind, then you have to collect all three primary colors and mix them together. The white in the middle is the one that actually means something.

If we could only make a red LED and a green LED, and maybe with effort a yellow LED, but never a blue one, then we could never "collect all the dragon balls" and gather that white light.

Without white light, all those LED lights and LED displays we depend on would be just talk.

But the road to finding the blue LED was full of twists and turns.

Hey, when I was filming my main channel, there was a little light in the background. Not sure if you recognize it. That light is called a "glow lamp."

That is the strange path humanity took when it was at the point of despair with LEDs, when it seemed hopeless.

Who made glow lamps big?

The Soviets did.

If you look at old Soviet videos, like the ones from Chernobyl, they all had timers, and their computers all had glow displays flickering on them.

Right, so they thought, "LEDs can't do this, a blue LED is impossible to make, so let's try something else." They used the principle of "cathodoluminescence," the glow of those tubes, to fill in the gap.

So the Soviets used a lot of these.

But why did this technology eventually get phased out?

Because the "blue LED" suddenly got made!

Our "dragon balls" were finally collected all at once, and we entered this huge "age of staying up late" with LEDs.

But the mysterious Japanese man who made the blue LED, Nakamura Shuji, came from a small provincial company in Japan called Nichia Chemical.

After making such a world-changing invention, the company only gave him a bonus of 20,000 yen, roughly over a thousand yuan. That's the whole story.

Okay, let's go back to Nakamura Shuji and talk about how he actually made the blue LED.

Let's start with a very typical story from his childhood:

He was a kid who did terribly in school and terribly in sports, the kind of child who was always much slower than everyone else, who was "bad at everything."

But his older brother forced him to join the school volleyball team.

Once he joined the volleyball team, people finally saw what kind of person Nakamura Shuji was:

He was loyal to his friends, and he was extremely good at making connections with people.

How far did it go?

This guy, who was terrible at sports and terrible at studying, was always the "class representative" in primary school.

Why?

Because all the people on the volleyball team, and all his classmates who knew him, always voted for him.

He was simply someone who "got along with everyone."

This person should have become a politician or a leader. But what happened?

This guy's dream had nothing to do with that. Interpersonal relationships came to him as instinct, and he simply didn't care about them.

What did he love?

He loved Astro Boy.

In Astro Boy, there's that "Professor Ochanomizu," right?

His dream was to become "Professor Ochanomizu"!

Such a streetwise guy, and yet he wanted to become a "physicist." That was his dream.

What a strange person.

As this strange guy kept growing, by the time he reached high school, his grades got a little better, and he got into one of the school's "part-time classes."

There were forty people in this class, and he was always somewhere around "38th or 39th."

His teacher said to him:

"Nakamura, you still need to study hard. If you want to get into a good university, you should quit that volleyball club. Go all-in for one last push and you can get into university."

But Nakamura said with great conviction to his teacher:

—Here we can see his personality again—

"I can't quit the volleyball team!

If I quit the volleyball team, our school's volleyball will be even worse. It'll 'flash yellow' and 'flash marry' (slang), and I'd be letting my brothers down.

Even if we never place, even if our volleyball team is always bad, I will never leave it!"

What a person with real leadership quality.

And sure enough, after the teacher used this kind of motivational pressure, his grades got a little better.

A little better, and in the end he took the college entrance exam and got into a third-tier university, or something like our "third-tier colleges" here. It was the local "University of Tokushima."

But note this: the University of Tokushima did not have a physics department!

His dream was to "become a physicist," so many of his classmates pitied him and said:

"Oh, you're just... his life is over, you know!

You want to be a physicist, and you ended up at a university like this, there's no hope!"

Then this guy said something that really showed his character:

"If I had really disbanded the volleyball team back then, I could have come first in the exam, I could definitely have gone to Tokyo, I could definitely have gone to a great university!

But I don't regret it now.

I'm at the University of Tokushima, and I hope to finish three years' worth of material in one year. Once I finish that, I can take the exam for a master's degree. With a master's, I can go to Tokyo, I can go to a better university, and I can study physics!"

This is a person who never gives up on his dream.

And sure enough, during this process, he learned very fast.

Studying at the University of Tokushima, he learned so quickly that he wanted to leave as soon as possible. Then he met a professor named "Tada Osamu," who taught semiconductors. What he taught was exactly this "dragon ball," the blue LED: how to build it and what it could do.

Wow! When Nakamura Shuji heard this, he thought:

"This is my mission! This is what I'm meant to do!"

This person instantly had fighting spirit.

With that fighting spirit, the later part of the story shows another side of this guy's personality:

When it came time to apply for a master's program, he still chose the University of Tokushima. He didn't go to Tokyo, and he didn't go to a better university to study physics.

Why?

Because in his third year of university, he started dating a local girl, and they got married.

That girl said:

"I don't want to leave Tokushima. I don't want to leave my hometown."

So Nakamura Shuji said:

"Forget Tokyo! I'm staying in Tokushima!"

Even after graduating, he couldn't leave Tokushima. He had promised his wife.

So what now? Go find a job.

Tokushima didn't have any big companies, so he joined a small company called "Nichia Chemical," a provincial business.

The small company had about 200 people, most of them local farmers from Tokushima. When farming season came, hardly anyone would show up to work at the company.

So Nakamura Shuji ended up doing research and development there.

On his first day in R&D, he went straight to his boss and said:

"I want to make a blue LED!"

Why Are Western Steaks Always Rare?

2025-10-21T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. This episode is about steak.

"How would you like your steak cooked?" This is probably a puzzle many Chinese people face when they walk into a Western restaurant to order steak. Wait, isn't steak just beef? Shouldn't it just be cooked until it's well done and brought out? Why do they keep asking how I want it done?

In the Western world, steak doneness is classified into five levels based on the temperature at the center: blue, medium-rare, medium, medium-well, and well-done. Americans and Europeans have an almost fanatical love for the range from medium-rare to medium-well, the steak that's "seared on the outside, pink inside, tender, and with red juices that run when you cut it." When steak arrives at a Chinese dinner table, though, a medium-well steak with no visible "blood" is the bottom line for most people.

So why do Americans and Europeans skip well-done and insist on "half-raw" steak? And how is steak different from ordinary beef?

Picture eating a steak. A cooked steak sits on your plate, with a knife and fork on each side. You stab the fork into the steak to hold it in place, use the serrated knife to cut it into small pieces, red juices run out, and you bring each bite to your mouth and chew slowly. The whole sequence flows smoothly and elegantly. It really is elegant.

But this "stereotyped," civilized way of eating steak doesn't have a long history. It only became popular in the early twentieth century. The word steak comes from the Scandinavian word for a thick slice of meat placed on a grill. In the Middle Ages, steak wasn't particular about which cut you ate or about etiquette. Meat was simply set over an open fire, and ordinary people ate it with their hands. Yes, steak was originally finger food. Forks didn't become common in Europe until much later.

Today, "steak" refers more to high-quality beef from parts like the sirloin and rump, usually cut into thick slices and cooked by grilling or pan-frying. Even the idea that "steak is better than beef in general" has been quietly instilled in consumers' minds. This "steak is more premium" impression was shaped by commerce and table manners together.

In the second half of the nineteenth century, as the major railroad lines crossing the United States from east to west were completed, the Atlantic and Pacific were linked by commercial arteries. Snowflake beef from the Chicago stockyards began to sweep across the country. Beef gradually came to be shaped by Americans as a food that symbolized power, masculinity, race (in the social context of the time), and economic success. Based on flavor, tenderness, juiciness, maturity, and marbling fat content, the US developed a grading system for beef, from canned minced meat up to snowflake steak. Only the top three grades, Prime, Choice and Select, with marbling of roughly 3% to 11% and fine, firm, tender meat, were suitable for steak. The parts of a cow that meet these conditions are mainly located above the sides of the spine.

Europe and America have a highly developed livestock industry. Cattle weren't raised to plow fields, but for milk and meat. So the muscles along the sides of the spine are mainly used to control the bending of the backbone and rarely move vigorously, and their muscle fibers are finer. On top of that, selective breeding of beef cattle has increased the fat content along the back. Faced with steaks rich in marbling and tender, shrewd butchers moved away from the rough method of "grilling until fully cooked over a high flame." Among grilling, roasting, braising, and boiling, they chose "pan-frying and grilling" as the methods that best bring out the aroma of fat and preserve texture. They cut the most tender tenderloin from the loin and the sirloin from the back loin separately, giving them elegant names. Combined with the refined dining rituals of French cuisine, steak consumption was tied to social class: clean, crisp napkins, silverware with engraved patterns, and a glass of tannic, aged Barolo from Italy. The once-rough barbecue thus completed its "class ascent."

So why do Westerners love "half-raw" steak? Here we need to introduce a concept: myofibrillar protein. It takes part in muscle contraction and affects the meat's water retention, firmness, and tenderness. Research on how cooking doneness affects myofibrillar proteins in beef notes that as core temperature rises, the surface hydrophobicity of myofibrillar protein keeps increasing. Put simply, it becomes better at "locking" juices inside the meat. At the same time, the myoglobin and pigments in the muscle begin to denature. Using a metal pan as the heat conductor, high heat creates the Maillard reaction on the surface of the steak, forming a roasted aroma. Keeping the interior pink and red in a half-raw state creates an excellent contrast in texture and flavor. This range generally corresponds to medium-rare through medium-well, with a juicy, tender mouthfeel. But when the core temperature exceeds about 65°C (past medium-well), myofibrillar proteins contract sharply, and the meat loses its juices like a sponge being wrung dry, becoming dry and tough. It's no wonder many people find well-done steak tiring to chew.

Interestingly, medium-well, the doneness many Chinese people prefer, sits right at the threshold where protein denatures. It avoids the psychological discomfort of red juices while still keeping much of the tender texture.

For many older Chinese people, steak isn't a familiar ingredient. But in the Chinese culinary context, steak with its "fancy outfit removed" is essentially beef tenderloin, known in Chinese as beef strips (牛柳). The basic approach to cooking Chinese-style beef tenderloin is to slice, cut strips, or mince it, and then either stir-fry it in hot oil or blanch it in boiling water. It's usually cooked through much more thoroughly.

So the question is: if steak tastes best at medium or medium-well, why doesn't traditional Chinese cooking have anything like a thick-cut, half-raw pan-fried steak? This may have to do with the high shear force of Chinese yellow cattle. Simply put, tenderness is perceived as "how easy it is to bite and how much residue is left after chewing." Chemically, it's measured by shear force: the higher the shear force, the less tender the meat. One study notes that the shear force of the most common local Chinese yellow cattle is about 8 kg, while Angus cattle come in at only about 4 kg. Frankly, the tenderness of yellow cattle really does fall short of some typical beef breeds.

But this also has to do with the resource endowments of civilizations. China's traditional agriculture was based on intensive, meticulous cultivation, and cattle were first seen as efficient farm tools, almost equivalent to today's high-load tractors. "Cattle are the foundation of farming," and raising them was more about labor than eating. Ancient China also long banned the private slaughter of farm cattle, so ordinary people rarely had a chance to eat beef. Even when they did, it was usually an old or overworked plow ox. Plowing is high-intensity labor, so farmers had to breed and select sturdy, strong yellow cattle to do the job. Day after day of hard work made their muscles more developed, and their muscle fibers thicker and longer. That's good for farming, but it makes the meat coarse and firm to eat. Even the most tender cut of the whole animal, the tenderloin, is no exception. Looking at historical paintings of "ox plowing a field," you can see that the strength of the neck and back makes the longissimus dorsi (including the area corresponding to the tenderloin) the main load-bearing muscle in plowing.

By contrast, meat breeds like Angus and Wagyu either spend their lives grazing on pasture to fatten up before slaughter, or eat feed in a barn to put on meat, and hardly work at all in their whole lives. This keeps their muscle fibers fine. Because breeding has gone in different directions, traditional Chinese cattle, bred for farming, developed muscular, hardworking yellow cattle, while Europe and America, breeding for meat, developed more tender varieties. Add to that the fact that "they have to do farm work" further weakened the meat quality of yellow cattle.

Still, as a country that is "the best at eating," China, even when dealt a hand where beef texture doesn't have the advantage, uses clever cooking to make it delicious. The approach we mentioned earlier is to slice the tenderloin thinly as preparation, then stir-fry in hot oil or blanch in hot water, shortening the cooking time and reducing protein contraction and juice loss. The representative dishes are stir-fried yellow beef and Chaoshan beef hot pot. Faced with the same question of "how to make beef tender," Chinese and Western cooking, each drawing on its own resources and culinary traditions, has given two delicious answers: the West found its answer in "thick cuts with precise temperature control," and the East found its answer in "knife work and quick cooking."

Today, with American steak culture spreading around the world, thick-cut steak has become a common ingredient in China too. When in Rome, do as the Romans do: Chinese people who can't get used to the classic half-raw style have worked out their own "new Chinese steak" methods, such as marinating before frying, wrapping in a thin layer of starch to lock in moisture, or simply dropping the steak into a hot pot and dipping it in sauce. Whatever the method and ingredient, when we pick up a piece of medium-well steak as a compromise, or boldly try a medium-rare one as an adventure, the clink of knives, forks and chopsticks might be carrying the most moving harmony: the pursuit of ultimate flavor always transcends the distance of culture and time, reflecting humanity's longing for a good life.

Alright, that's it for this episode of Cold-Knowledge Blind Box. See you next time.

Newton's Other Side

2025-09-27T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. The person we're talking about today is...

If he were around today, he would definitely count as an academic superstar. He's the name you couldn't avoid in school. Physics class covers mechanics and mentions him. Math class covers calculus and can't skip him. Even astronomy, when talking about universal gravitation, has to start with him. Yes, he is Isaac Newton, the man countless people call "the greatest scientist in human history." But you may not know that this scientific giant's life is far more dramatic than what the textbooks say. Before he turned 26 he had already made discoveries that changed the world, but after 30 he became obsessed with stock trading and turned into a "leek" (a sucker, as the slang goes), and he also put on a series of "academic palace dramas" with the great minds of his time. Today, let's set aside the "perfect Newton" from the textbooks and talk about a life that was both great and "real to the point of being uncomfortable."

(Sound effect: a soft rustle of turning pages)

First, let's talk about Newton's "cheat-code start." In 1665, a great plague broke out in London. Yes, you heard right, the famous "Great Plague of London," a disaster that killed more than 80,000 people, and the whole city was in a panic. At that time Newton, 22 years old, was studying at Trinity College, Cambridge. When the university saw the situation, it quickly sent the students home to avoid the disease. If that happened today, plenty of people would probably be stuck at home scrolling on their phones and ordering takeout. But who was Newton? He shut himself in the attic of his family home in the countryside and began a mode of "closed-door training."

Want to guess what he did during those 18 months? Let me put it this way: those two months were worth more research than most people's entire lives! First he came up with the "generalized binomial theorem," which later became a basic tool of advanced mathematics. Then, even more outrageously, he started working on calculus (yes, the subject that today's college students dread the most). Although it wasn't formally named yet, he had already thoroughly grasped its core ideas. More importantly, he set his sights on two "ultimate questions": first, why does an apple fall to the ground (which later led to universal gravitation), and second, what on earth is light (which later led to his theory of optics). Later, people called these 18 months Newton's "year of wonders," like a martial arts novel where the hero falls into a cave, finds a secret manual, and in one night opens all his blocked meridians. Except Newton's "secret manual" was his own brain.

(Sound effect: a bell tolling, symbolizing a return to campus)

When the plague ended, Newton went back to school to continue his studies, and he happened to take over the position of his teacher Barrow, becoming the Lucasian Professor of Mathematics at Cambridge. This title was no small thing. It was like the "academic throne" of Cambridge, and everyone who held it was a heavyweight. For example, Stephen Hawking, who wrote A Brief History of Time, held it for 30 years. You'd think such an impressive figure would have packed lecture halls, but Newton refused to play by the rules. His lectures were so "hardcore" that students couldn't follow them, and fewer and fewer people came to listen, until eventually almost no one signed up. You might think this would hurt his career. On the contrary! With no one bothering him, he became even more focused. He built the world's first "reflecting telescope," which was far clearer than the refracting telescopes of the time. He systematically organized his theories of optics. He was even invited by the Queen of England to tea. A true academic superstar.

(Sound effect: tense violin strings)

But good days didn't last long. Newton soon ran into his lifelong nemesis, Robert Hooke. Hooke was no minor figure. He was a few years older than Newton, and at that time he was already a "heavyweight" in the Royal Society of England. He discovered "Hooke's law" (the one stating that the restoring force of a spring is proportional to its deformation). He was also a master of the microscope, and even the word "cell" was coined by him. Yes, the "cell" you learned about in biology class was first named by Hooke as he stared at a piece of cork. If the two had cooperated, they might have made even more great discoveries. But unfortunately, one had a hot temper and the other was a nitpicker, and when they came together it was like Mars crashing into Earth.

Their first clash was over optics. Newton believed light was made of "particles" (later called the "corpuscular theory"), while Hooke supported the "wave theory" (along with Huygens and his school). Newton wrote a paper to rebut him, and the very first paragraph directly named and insulted Hooke, which made Hooke furious: "What makes you think I'm wrong? I'm the grandfather of the microscope!" Even worse, Newton's great work Opticks, which he had been holding back for years, could have been published much earlier. But Hooke and Huygens teamed up to attack him, and Newton got so angry that he locked the manuscript in a drawer and didn't publish until after Hooke had died. By then Huygens had also passed away, and no one was left to argue with him. His "corpuscular theory" then dominated the scientific world for over 100 years, until Einstein proved "wave-particle duality" in 1905. (So it turned out that neither Newton nor Hooke had been entirely wrong.)

(Sound effect: low, dramatic narrative music)

The most famous conflict was over the "law of universal gravitation." Nowadays we love to tell the story of "Newton gets hit by an apple, then discovers universal gravitation." But honestly, that's probably made up. Newton's niece, wanting to brag about her uncle, invented the story that an apple hit him on the head, and later Voltaire embellished it in a book, and the whole world went along with it. In reality, Newton studied gravity because he had read Hooke's and other scientists' discussions about planetary motion. In particular, Hooke had already proposed the "inverse square law" (that gravitational force is inversely proportional to the square of the distance), but Hooke wasn't strong enough at math, so he could only make a rough guess.

Newton was different. He pulled out calculus, the great weapon, and in nine pages of paper he worked out that if gravity followed the inverse square law, then planetary orbits must be ellipses (which was later proven to be completely correct). He wrote his results up as Mathematical Principles of Natural Philosophy, which became the "bible" of science. But when it came time to publish, he needed financial help from his good friend Halley (the same Halley after whom Halley's Comet is named). By then Hooke wasn't having it: "I said the inverse square law long ago! You must have borrowed my ideas!" He asked Halley to plead with Newton on his behalf: "At least mention my name in the book, would you?" Newton reluctantly wrote a line in a note: "Hooke also discussed similar problems." But Hooke felt it wasn't enough and kept pushing the issue. Newton then went fully dark. In the second edition of the book, everything about Hooke was deleted. Hooke later died bitter and frustrated. And after Newton became president of the Royal Society, he used the society's move to destroy Hooke's manuscripts and portraits. Today we don't even know what Hooke looked like. Some guess that he was badly hunchbacked. And Newton's famous line, "If I have seen further, it is by standing on the shoulders of giants," which was actually written to Hooke, may have been a sarcastic jab: "With your hunched back, you couldn't even reach my shoulders!"

(Sound effect: a mocking chuckle)

Newton's skill at picking fights wasn't limited to Hooke, either. He also clashed with the director of the Greenwich Observatory, Flamsteed. This director spent 40 years observing 2,935 stars, with records so precise they were almost unbelievable, but because he was such a perfectionist, he kept delaying publication of his star catalogue. In 1680, two comets appeared in Europe. The director judged that it was the same comet returning, and Newton at first disagreed, but later changed his mind and never once mentioned the director's contribution. Even worse, Newton's good teammate Halley (yes, the same Halley of Halley's Comet) secretly gave Newton the star catalogue that the director had not yet published, and Newton used it right away. In 1712, when he became president of the Royal Society, he and Halley published the star catalogue together. The director was so angry that he paid out of his own pocket to buy back 400 copies and burned 300 of them on the spot. It wasn't until six years after his death that his widow had the authorized star catalogue printed.

(Sound effect: a tense standoff)

And then there was the "calculus war" with the German genius Leibniz. We now know that the two independently invented calculus (Newton used his "method of fluxions," while Leibniz used simpler notation). But at the time, to win the title of "inventor," Newton had the Royal Society anonymously publish a report saying Newton's method was superior. Later it was discovered that the "anonymous reviewer" was Newton himself! Leibniz was furious, the scientists of mainland Europe all sided with Leibniz, and British scholars stubbornly stuck to Newton's notation system. As a result, British mathematics fell behind the rest of the world by more than a hundred years, and the center of academic gravity slowly shifted to France.

(Sound effect: soothing music)

But let's not focus only on Newton's "dark history." His achievements before the age of 26 were epoch-making, whichever one you pick: calculus laid the foundations of modern mathematics, universal gravitation explained the laws of celestial motion, his optical theory let us see the nature of light, and reflecting telescopes are still used in observatories today... In 1696, a Swiss mathematician named Johann Bernoulli deliberately gave all of Europe's mathematicians a hard problem, the "brachistochrone problem," and secretly sent it to Newton hoping to humiliate him. Newton got home at four in the afternoon, saw the letter, stayed up all night, and sent the answer back by four in the morning, signed "an old Englishman" (Newton was 54 at the time). When Bernoulli received the answer, he was astonished: "From this claw, I recognize the lion!" (In other words: this style of solving the problem can only belong to Newton!)

(Sound effect: warm closing music)

So what was the real Newton like? He was a genius: before he turned 26 he already stood at the peak of science. He was also an ordinary human: he held grudges, he was jealous, he fell out with peers over reputation, and in his later years he was obsessed with alchemy (trying to turn base metals into gold) and lost his fortune in stock trading (it's said he lost so much he didn't even have underwear left). But it is exactly this "complexity" that makes him more real. Scientific progress has never come from "perfect saints." It has come from people who dare to think, dare to fight, and who are even a bit of a "troublemaker."

So the next time you hear the story of "Newton and the apple," you can smile and tell your friends: "Don't believe it. That might have been made up by his niece, or it might have been a bit Voltaire added." But don't forget that what truly made Newton great was not the apple, not Hooke, not any single argument. It was the 18 months he spent during the plague, and every page of formulas he wrote in that lonely attic, and his eternal questioning of "why" about this world.

(Music fades out)

Well, that's all for today's "Chronicles of Scientists." I'm your host, [Your Name], and I'll see you next time.

Chatting About the Little Traps in Thinking

2025-09-02T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box.

Today we're talking about a really interesting topic:

Where do our brains tend to fall into little traps while thinking?

Put simply, you think you're being rational, but your brain has already been quietly leading you astray.

So let's take apart some of these "pits" in our thinking together.

Cognitive Dissonance

Let's start with the first one, cognitive dissonance.

The classic example is the fox. It can't reach the grapes, so it turns around and says:

"Hmph, those grapes are sour."

Why does this happen?

Because there's a conflict between "I want to eat the grapes" and "I can't get the grapes."

To make itself feel better, it changes its mind:

"Actually, I didn't want those grapes anyway."

We do this ourselves all the time.

For example, someone says with their mouth, "Rich people are all greedy," but deep down they really want to become rich.

When that goal turns out to be out of reach, they comfort themselves by putting it down.

The psychologist Leon Festinger said there are three main ways people deal with cognitive dissonance:

change their behavior;
change their beliefs;
or simply distort the facts.

Put simply, it's about making yourself feel comfortable.

But if you rely on it too much, you drift further and further from the truth.

The Spotlight Effect

The second one is the spotlight effect.

Here's an example:

You get a little stain on your shirt, and you feel like everyone is staring at it.

A girl goes on a date, her makeup has smudged, and she gets incredibly nervous.

But in reality, other people may not have noticed at all.

After all, everyone is busy caring about themselves, and nobody has time to keep staring at you.

So don't worry too much about other people's opinions.

The world isn't paying that much attention to you. Just relax.

Hindsight Bias

The third one is hindsight bias.

As soon as something happens, people very easily say: "I knew it all along!"

For example, when a stock goes up, you say you saw it coming.

When it goes down, you also say you had a feeling about it.

In truth, you didn't really understand anything at the time.

It's just that once the result is out, your brain automatically "edits your memory," making you feel smart.

This kind of illusion is quite dangerous,

because it makes us ignore complexity and think problems are much simpler than they are.

Framing Effect

The fourth one is the framing effect.

The same meaning, phrased differently, can produce completely different results.

Take buying popcorn, for example:

A small bucket is 3 yuan, a large bucket is 7 yuan, and many people will choose the small one.
But add a "medium bucket for 6.5 yuan," and most people will think:

"Wow, paying 0.5 yuan more for the large bucket is a better deal."

There are also more everyday examples:

"After we get married, you earn the money and I spend it."
Rephrased, it becomes:

"After we get married, you take care of earning money to support the family, and I take care of being beautiful."

Doesn't that instantly sound much nicer?

That's the power of the framing effect.

The Illusion of Exponential Growth

The fifth one is the illusion of exponential growth.

Here's a brain teaser:

If you fold a sheet of paper in half 50 times, how thick do you think it would be?

Many people would guess 1 meter, or 10 centimeters.

But in fact, its thickness could stretch from the Earth all the way to the Sun!

This is what happens when the brain lacks intuition for exponential growth.

So we often underestimate the power of long-term accumulation.

Whether it's investing or learning, if you keep at it even a little bit,

over a long enough stretch of time, the results can exceed your imagination.

The Barnum Effect

The last one is the Barnum effect.

It's when vague descriptions feel accurate no matter who they're applied to.

For example:

"You are a kind person, but sometimes you also feel fragile."

It seems to fit anyone.

That's why horoscopes and personality tests often feel incredibly accurate.

In fact, they just use ambiguous words to let you find yourself in them.

Ending

Alright, today we talked about several thinking traps:

cognitive dissonance, the spotlight effect, hindsight bias, the framing effect, the illusion of exponential growth, and the Barnum effect.

These psychological phenomena remind us:

Don't trust your brain's first reaction too much, because it often secretly leads you astray.

Learn to stay alert, learn to adjust, and life becomes a lot easier.

This is Cold-Knowledge Blind Box. Next time we'll keep talking about Nvidia, looking at how it unexpectedly entered the AI track and built an unshakable business empire. See you next time!

Breaking Down Common Thinking Fallacies

2025-08-18T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. In the last few episodes we talked about the early startup story of NVIDIA. This time, let's switch topics and take a break, and talk about how to recognize and break down the logical fallacies we run into in everyday life.

I. Why does learning to tell logical fallacies apart matter so much for us? It's because many logical fallacies are very well hidden,

and often hard to notice.

As the old saying goes:

"You get sold out and still help them count the money!"

But, if we learn to recognize them,

the benefits are huge!

When shopping, you can avoid merchants' tricks,

which effectively saves you a lot of unnecessary money.

• At work, your judgment improves,

and you can pin down the core of a problem more precisely,

which means getting promoted faster.

• When making decisions, you avoid detours,

which means seeing through appearances to the essence of things.

• Even when arguing online,

you can quickly spot the logical holes in the other side's argument,

achieve a dimensionality-reduction strike, and take the moral high ground.

• And even in daily life, staying clear-headed and rational,

makes every judgment and every decision more reliable!

II. The Straw Man Fallacy

Because logical fallacies are so hidden and hard to spot,

once you fail to recognize them, the harm can be considerable.

Let's start with the first one:

—the straw man fallacy.

What is the straw man fallacy?

What harm does it cause?

And how does it affect us?

【Definition】

The straw man fallacy is: fabricating a "false position" that closely resembles the original view, and then refuting it.

Like this: you can't beat a real person, so you make up a straw man and beat it to a pulp!

So it works by:

• distorting the original meaning

• exaggerating, or even inventing out of thin air, the other side's view

to attack the other side.

It is a typical thinking fallacy.

【Is it common?】

It's everywhere in life and online!

【Example 1】

• A: We should reduce carbon emissions.

• B: So you mean we should stop all industrial activity and go back to the Stone Age?

【Example 2】

• Parent: Play less games, and spend more time studying.

• Child: So you want me to study nonstop and work me to death?

【Example 3】

• Colleague A: Let's add a bit more free discussion time during meetings, so everyone can communicate fully.

• Colleague B: So you want us to sit in meetings all day and not do anything else?

【Example 4】

• Someone says we should build high-speed rail,

and someone replies: You want to waste all our money on the bureaucrats?

• Someone says we should build schools and hospitals,

and someone replies: Education is already so involuted, why not reform that first?

• Praising the Apple phone's looks gets you labeled "worshipping foreign things";

praising the Huawei phone's looks gets you labeled "ignorant chives" (a dupe).

【Harm】

The straw man fallacy quickly turns communication into mutual attack and cursing.

• You talk facts, they talk position;

• You talk reasoning, they talk emotion;

• You talk general trends, they talk individual cases.

The goal of such people was never to reason properly.

It's to distort and exaggerate, to inflame your view,

to deliberately shift the focus and build a "straw man,"

and then "defeat" it,

so that they feel they have "won."

Next time you meet this kind of person,

just throw this episode at them!

III. The Red Herring Fallacy

Next, the second one:

—the red herring fallacy.

What is a red herring?

How is it different from the straw man fallacy?

【Story】

A "red herring" is a kind of red smoked fish with a strong smell.

In police dog training, officers would drag a strong-smelling trail of it,

to make the police dog veer off its original tracking route.

【Definition】

The red herring fallacy is:

using a topic that "seems related but is actually irrelevant"

to divert attention.

【Example】

• A: I adopted a stray dog yesterday.

• B: Then why don't you go take care of the elderly at a welfare home?

【Difference from the straw man fallacy】

Common ground: both avoid the real issue.

Difference:

• Straw man: distort the other side's meaning, then attack it;

• Red herring: bring in a new topic, to cover up the focus.

【More examples】

• Parent: Why did your grades drop?

Child: But some classmates don't play with their phones at all, and their grades dropped too!

• Netizen: The country should invest more money in developing education.

Reply: You're unpatriotic! You want to cut national defense spending, so we get invaded by foreigners?

In politics, this is also used often:

A politician throws out a "red herring," and the public's attention instantly swings away,

the argument escalates,

and the original topic is forgotten.

IV. The Attribution Fallacy

The third one:

the attribution fallacy.

Huang Renxun and NVIDIA, Part 01: Whose Wife Agreed to the Startup First?

2025-07-24T00:00:00Z

Hi everyone, welcome to this episode of Cold-Knowledge Blind Box. NVIDIA has recently been allowed to resume sales of the H20 graphics card, and CEO Huang Renxun has come to China for business talks. As the head of the world's most valuable company, he is someone many people are curious about. So we're starting a special series on Huang Renxun and NVIDIA.

Okay, let's get started!

Huang Renxun is a source of Chinese pride, so surely he had a good elite education? That's half right. His parents made a serious mistake when choosing a school for him.

At the time, Huang Renxun's parents were still in Thailand processing their immigration papers. Because the political situation there was unstable, they had to leave the young Huang Renxun with relatives in the United States first. This was the 1970s, before the internet, so information moved slowly. The relatives were also recent immigrants who didn't speak English, and his parents were on the other side of the ocean.

As a result, the family wrongly assumed that every boarding school in America was the kind of elite aristocratic boarding school they had imagined. They spent everything they had to send Huang Renxun to a school called Oneida Friends School. In reality, it was a mountain reform school that used Christian principles to reform young offenders with a history of violence.

A Hard School Life

Huang Renxun and his older brother were stunned as soon as they arrived. Huang Renxun was only nine years old, and his roommate was already 17, covered in tattoos and knife scars. By then it was too late to withdraw. In that era, bullying was routine, and Asian kids were especially targeted.

Apart from classes, the two boys had to do labor as part of their "reform":

His older brother worked on the farm, growing tobacco
Huang Renxun was responsible for cleaning the toilets in a three-story dormitory building

Turning Enemies into Friends

His roommate was a delinquent teenager, and there was no way to change rooms. Since he couldn't beat his opponent, he had no choice but to join them. When Huang Renxun was very young, his mother picked ten random words from a dictionary every day and made him memorize them. Nine-year-old Huang Renxun used that skill to teach the 17-year-old American delinquent English words.

The two quickly became good friends. The older boy looked out for Huang Renxun, taught him weightlifting and fitness, and hit back for him whenever someone picked a fight.

The Impact of the Reform School Years

Two years later, his parents successfully immigrated to the United States from Thailand, rescued him from the reform school, and transferred him to a proper public school.

Those two years at the reform school played a major role in shaping his character:

He admits that this period forged his tenacious, street-fighter personality
It is also why NVIDIA has nearly gone bankrupt several times and still always pulled through

To repay his old school, Huang Renxun wrote a $2 million check in 2019 to build a new dormitory building there.

The Toilet-Cleaning Skill

Huang Renxun holds a Silicon Valley record that nobody has broken. He has admitted that he is the Silicon Valley CEO who has cleaned the most toilets, and the one who is best at it, thanks to the toilet-cleaning skills he built at the reform school.

Later, while working a part-time job in high school, he went to Denny's, a cheap American restaurant chain, and continued cleaning toilets. Denny's was a 24-hour diner where a meal cost about $10, and many customers were North American long-haul truck drivers working the night shift, so you can imagine how clean the restrooms were.

While working at Denny's, he learned:

How to balance quality and efficiency
How to cope with chaos in the kitchen
How to communicate with customers

In 1993, Huang Renxun and two partners sketched out NVIDIA's startup plan on a restaurant placemat at Denny's. To commemorate the moment, Denny's later launched an "NVIDIA Breakfast."

College Years

Studies and Romance

An early bloomer, Huang Renxun graduated from high school at 16 and entered Oregon State University to study electrical engineering. There were almost no women in engineering. In his class of 250, only three were women, and Huang Renxun managed to catch the attention of his future wife, Lori.

With 247 men chasing three women, that's an elimination rate of 98.8%. Becoming friends with a delinquent older boy at nine, and winning over a girlfriend in college, both show that he had outstanding interpersonal skills from a very young age.

The two married right after graduating and have now been married for 41 years, with two children. A first love that lasted a lifetime suggests Huang Renxun put all of his energy into his career.

Job Hunting and Career

After graduating, Huang Renxun's job hunt didn't go smoothly at first, and Texas Instruments turned him down. Interestingly, when he interviewed at TI, another Taiwanese man, Zhang Zhongmou, had already risen to global vice president there. By the time Huang Renxun sent in his résumé, Zhang Zhongmou had just left TI to return to Taiwan and found TSMC.

If Zhang Zhongmou had gone back to Taiwan a few years later, Huang Renxun might well have followed him and become a founding member of TSMC's startup team.

Although Huang Renxun didn't get into TI, he was hired by AMD. He worked at AMD during the day and studied for a master's degree at Stanford on weekends. Even so busy, he and his wife still found time to have a son and a daughter.

Because his time was limited, it took him eight years in total to finish his master's. Most people would have given up long before. Huang Renxun said: "For some things I want to see change right away, but for others I have infinite patience, and I'll keep at them."

Career Turning Point

From AMD to LSI

Although Huang Renxun stuck it out for eight years to earn his master's degree, he was already thinking about leaving AMD just a year after he started. At the time, AMD's chips were still designed by engineers by hand, using calculators, and Huang Renxun saw clearly that this approach had no future.

By contrast, another company, LSI, had developed a computer-aided design (CAD) system that let customers log in remotely and design chips themselves. So Huang Renxun decided to leave and join LSI.

If he had stayed at AMD for eight years, the way he did for his master's, he might now be an ordinary Chinese engineer whose greatest achievement is a nice house in Silicon Valley.

The Wisdom of Persistence and Letting Go

How do you tell which things to stick with and which to give up? Huang Renxun's explanation: "Greatness has nothing to do with being smart. It depends on character."

The major choices at life's crossroads ultimately rest on the character he built at nine, cleaning toilets and facing bullying. The two years at the reform school taught him to be a tenacious street fighter, not to accept the status quo and passively endure.

In his later biography, Huang Renxun wrote: "I hope young people can go through enough pain and hardship."

How NVIDIA's Three Founders Met

Priem's Path as a Genius

After joining LSI, Huang Renxun was sent to Sun Microsystems to work on a project, and there he met the other two NVIDIA founders: Curtis Priem and Chris Malachowsky.

Curtis Priem was a computer genius. When he graduated from high school, he was admitted to three universities at once, one of them MIT. He decisively turned down MIT, ranked among the world's top three, and chose RPI, a technical school ranked around 70th in the United States.

The reason was that the school had just bought one of the most advanced IBM 3033 mainframes of the time, which gave him a chance to program on it. After he enrolled, he spent so much time on the machine that its heat could have warmed the entire building through the winter.

The Timing

When Priem was in college, Steve Jobs released the Apple II. The machine proved that computers didn't have to live in school computer rooms or corporate offices. They could sit on every ordinary family's desk as a consumer product. That also meant ordinary users would soon demand better graphics.

Priem's first job was designing the most advanced graphics cards. In 1984, the card he designed could deliver 640×480 resolution, a big step up from the previous generation's 640×200, and the number of colors jumped from 16 to 256.

Setbacks at Work

But like many technical geniuses, Priem had terrible luck at work. Although he could design the most advanced graphics cards, he kept landing at bad companies that gave him no stock options, while handing him plenty of free exposure to office politics and management squabbles.

Eventually, after many twists and turns, Priem joined Sun Microsystems.

The Birth of the GX Graphics Card

A Secret Project

At Sun Microsystems, Priem met his first champion, an executive who had a secret project of his own. He planned to develop advanced graphics hardware behind the backs of Sun's senior management. This meant moving part of the graphics processing work off the CPU and onto a piece of hardware that would later be called a graphics card.

A Fight Over Technical Direction

Such a bold idea sparked a battle over technical direction inside the company. Fights over technical roadmaps at tech companies were as intense as ideological struggles in a communist revolution.

Sun's top leadership wanted to focus on improving CPU performance, which they saw as the core business of server computers. So the project had to be carried out in secret.

Building the Team

Faced with such a difficult technical challenge and an enormous amount of freedom, Priem said just one thing to his boss: "Give me a team."

So the boss recruited a brilliant engineer from HP, who would later become another NVIDIA co-founder: Chris Malachowsky. He would be responsible for turning Priem's brilliant ideas into concrete chip designs.

To make sure the secret project moved forward smoothly, LSI, an important partner of Sun Microsystems, also sent a rising star of the company, Huang Renxun, to join the project's integration and manufacturing work.

At this point, the destinies of NVIDIA's three founders had crossed.

Getting the Team to Gel

Hi everyone, welcome to Cold-Knowledge Blind Box. Picking up where we left off, the three NVIDIA founders' destinies had crossed. In this episode, I'll keep going with how NVIDIA was founded and its early startup story.

Priem was responsible for the blueprint of the GPU, and he is American. Malachowsky was the team's engineer, responsible for turning the GPU from a blueprint into a real product, and he came from the former Soviet Union. Huang Renxun handled project management.

A typical scene was Priem and Malachowsky staging a Silicon Valley version of the US-Soviet standoff in the office. After every loud argument, Priem would storm off in a sulk, and the engineers on the team would blink nervously and ask, "Are we about to break up?"

Huang Renxun's answer: "Intense arguments may be uncomfortable, but to get the very best ideas, you have to embrace conflict."

This later became NVIDIA's corporate culture.

The Groundbreaking GX Card

The Product

In 1988, this secret team produced a groundbreaking product: the GX graphics card. How impressive was it?

To show off the card's hardware power, Priem made a flight-combat game in his spare time, featuring the F/A-18 Hornet. It is said to be the first flight simulation game in human history. Priem even bought real satellite data to build the game's battlefield terrain, and the game supported LAN multiplayer.

Comparing the Specs

What resolution did this air-combat game run at? 1280×1024.

Six years later, the famous Taiwanese RPG Chinese Paladin, from Softstar, reached a maximum resolution of only 320×200. At the time, only the GX card could fully support the outrageous graphics demands of this air-combat game. It was pure showing off.

Commercial Success

Faced with such an outstanding product, Sun's senior management changed its hostile attitude toward graphics cards. Soon the card was priced at $2,000 and sold as an optional upgrade kit for servers.

The flight-combat game was sent to every Sun sales office. Salespeople demonstrated the servers' excellent graphics performance to corporate customers and crushed their competitors.

The upgrade kit sold like hotcakes and quickly became the standard default configuration for Sun servers. LSI, where Huang Renxun worked, saw its revenue jump from $262 million to $656 million.

Huang Renxun was promoted to department director for this successful project.

The Trigger for Starting a Company

Infighting at the Company

But after these young people brought Sun commercial success, the company quickly sank into endless infighting and political struggles. For two years it released no new products and lived off the old GX card.

Priem, who had contributed enormously, was pushed out inside the company and was even moved to tears at one point. (He's a tech nerd, so everyone understands.)

The Startup Idea

Malachowsky, his good buddy at the company, had had enough, and suggested that the two of them leave Sun together to start a company. But these two tech nerds had no business sense. At that point, they both thought of the same person at once: Huang Renxun, whom they had worked with on the GX project two years earlier.

Timing Analysis

In hindsight, the timing of their startup was ideal. In 1992, personal computers got two revolutionary developments:

The PCI bus
The Windows 3.1 operating system

The spread of personal PCs thus entered an explosive phase.

All three of them knew the startup would be about graphics for personal PCs, but the specific business direction was still a blank.

The Business Plan at Denny's

Drafting the Plan

At the end of 1992, the three began writing their business plan at a Denny's in East San Jose. Huang Renxun's usual order there was a sandwich called the "Super Bird," with turkey, melted Swiss cheese, tomato, and his favorite, bacon.

Huang Renxun's Hesitation

At first Huang Renxun seemed a bit undecided, because his job was going smoothly and he had just been promoted to director. The other two partners, however, were in a tough spot. Anyone would think: "Why should I give up my bright future to start a company with you two?"

Fortunately, Huang Renxun was a gamer himself, and Priem and Malachowsky soon convinced him: if they developed graphics cards for gamers, the company could bring in more than $50 million in revenue in a very short time.

The Wives' Showdown

The next question was which of the three would quit first. This led to a funny episode: the Wives' Showdown. Each of their wives wanted the other men to quit first.

In the end, the one who broke the deadlock was Priem, who was doing the worst at work and was about to be fired anyway.

Quitting to Start the Company

Leaving in Stages

December 1992: Priem officially resigned. (Not only Huang Renxun, even his good buddy Malachowsky hadn't left yet.)
March 1993: Malachowsky finished the GX card upgrade and also resigned. His boss, the one who had secretly had him develop the GX card, gave him a parting gift: permission to take one Sun server with him to his new company. A few days before he left, the resourceful Malachowsky used company funds to upgrade the machine to the largest memory, the biggest hard drive, and the largest monitor available.
June 1993: Huang Renxun also officially resigned. He had just turned 30 that year.

Founding NVIDIA

The three set off together on a great voyage. When they registered the company, each of them put in $200 and bought a third of NVIDIA. The company's latest market value is around $3 trillion.

Priem lived in the suburbs, where the house had more room, so he cleared out a room and moved Malachowsky's server in. NVIDIA officially opened for business right in his house.

The Early Team

The three had already built a bit of a name for themselves in Silicon Valley, so soon after they left, a group of talented people gathered around them. Twelve employees on the early team worked without any salary.

The company's most urgent task was to build a first product to show, then take the demo to venture capital firms to raise money and pay the first salaries.

The Sequoia Capital Pitch

Preparing the Pitch

If there are startup founders watching who are also trying to raise money, you may be wondering: when pitching to VCs, is the product demo more important, or is the founding team's ability to improvise in the moment?

The answer: neither matters.

A Train Wreck

The three founders' first major pitch to a VC was a huge train wreck. The person interviewing them was Don Valentine, the boss of Sequoia Capital, now the world's largest venture capital firm, which had also been an early investor in Google and Apple.

For the pitch, Huang Renxun brought a modified GX card as a concept demo. Although the card had been out for four years, its performance still crushed any Windows graphics card on the market. They even brought an early VR headset to demonstrate a game.

Valentine's Doubts

But what the NVIDIA team didn't know was that Don Valentine most disliked flashy product demos. He had seen too many products with impressive demos end up losing badly in real commercial competition.

After the demo, Valentine asked: "Are you a game console company, a computer graphics company, or an audio company?"

Priem was stumped on the spot, then delivered a long speech full of technical detail to prove that NVIDIA was the sum of all of the above. Most of what he said was correct. NVIDIA later did achieve "one card, many uses," with a single graphics card serving game consoles, computer graphics and audio processing all at once.

What he really wanted to say was this: if one chip can handle many tasks, the cost of hardware development will drop dramatically.

Valentine was unhappy with an answer that sounded like evasion. He said: "You have to choose one, or you'll fail, because you don't know who you are."

The three young men were speechless, and the atmosphere in the room was extremely awkward.

A Visionary Technical Idea

Valentine went on: "What will NVIDIA look like ten years from now?"

The tech nerd Priem answered again: "We will have a unified shader architecture." What he actually meant was that future graphics cards would no longer be just graphics accelerators. They would become high-performance parallel platforms for general-purpose computing.

Today's GPUs, through CUDA and the unified shader architecture, can work together with CPU memory to efficiently handle the large-scale matrix computations that AI needs. But in the early 1990s, the idea was far too advanced. Valentine looked completely baffled, and even Huang Renxun and Malachowsky couldn't follow what he was talking about.

Choosing a Foundry

Then Valentine asked which company they planned to have make the graphics chips. Huang Renxun said they planned to use the European chipmaker STMicroelectronics, though at that time the company itself was nearly bankrupt. Valentine recommended TSMC in Taiwan instead.

An Unexpected Investment

The Pitch Failed

After the pitch, all three were deeply dejected. Huang Renxun, as CEO, took responsibility for the failure on his own shoulders. Malachowsky later described the pitch as being like a courtroom hearing before a judge.

The Real Reasons They Got the Investment

Unexpectedly, though, Sequoia agreed to invest very quickly.

First reason: The CEO of LSI, where Huang Renxun had worked before, called Valentine and gave a highly favorable assessment of Huang Renxun as a person. Every venture capitalist knows that a startup team's reputation in the industry matters more than how it performs in a pitch.

Second, and most important, reason: Sequoia had just invested in the famous EA and seen it go public successfully, earning a huge windfall, so the firm was very bullish on the future of the gaming market. It had also invested in one of NVIDIA's main rivals, and Huang Renxun's team believed they could beat S3, which was strong in 2D graphics, by building 3D graphics cards. Investing in NVIDIA was effectively Sequoia hedging its bets on both sides, so it couldn't lose.

Closing the Round

In June 1993, NVIDIA raised a total of $2 million in Series A funding from Sequoia Capital and another venture firm, Sutter Hill.

After the contract was signed, Valentine said to Huang Renxun: "Investing in you went against my instincts, but your old boss, the CEO of LSI, suggested I give you some money to try. If you lose all my money, I will definitely find someone to take you out. Your business plan is badly written, but your reputation in the industry will get ahead of you and give the VCs a good first impression."

Wrap-Up and Looking Ahead

This shows why Sequoia Capital is so formidable: its boss was able to make many counterintuitive, even counter-human investments.

Next episode preview: I'll cover the story of how NVIDIA, after getting the money, competed with 3dfx and its Voodoo graphics card.

If you're interested in the book The Nvidia Way, don't forget to like the video and add it to your personal collection. Now let's bring this vanished text back with technology. See you next time!

The God of Summer: The Wild Truth About Watermelon

2025-07-05T00:00:00Z

Hi everyone, welcome to Cold-Knowledge Blind Box. This summer has been brutally hot, so this time we're skipping the miracle medicines and talking about the most common summer fruit: the watermelon. It's a giant fruit everyone loves in summer, and everyone knows it. When summer arrives and you bite into a slice, nothing cools you down and quenches your thirst quite like it, right?

So we should really get to know it properly first. Watermelon's origins and history are full of interesting stories. And as a fruit, it's actually quite unusual. Once you've learned about it, you might feel differently the next time you eat one. Give it a try.

In biological classification, the watermelon is a eudicot in the order Cucurbitales, family Cucurbitaceae, genus Citrullus. That makes it a relative of the gourd.

A lot of the melons you know belong to this family: winter melon, watermelon, pumpkin, cucumber, luffa, and muskmelon are all Cucurbitaceae.

At this point some listeners might be wondering how these melons got their names. Why is the watermelon called "xigua" (西瓜, "western melon") in Chinese? The reasoning is pretty casual, and it works the same way as the name for pumpkin, "nangua" (南瓜, "southern melon"). Neither was originally native to China. Nangua came in from the south, so it's called "southern melon." Xigua came in from the west, from Central Asia, so it's called "western melon." That's really all there is to it.

The English name for watermelon is "watermelon," which translates literally as "water melon," and that's a very fitting name, right? It refers to its extremely high water content. About 92% of a watermelon is water. Compared with other fruits, it's much juicier, and there's no way to eat a watermelon without dripping. Everyone has experienced that. That's also why it's so refreshing, and why it's called watermelon.

The Japanese name is the most fun of all: "スイカ" (suika). The word actually came from Chinese. It's a phonetic borrowing of "xigua," imitating how "xigua" was pronounced in certain southern Chinese dialects. And here's the coincidence: in Japanese, that sound can also be understood as "water melon" (水瓜), which happens to match the English name, watermelon. So it's a double meaning, a pun that just happened to work out.

Now, the watermelon's love of water is also directly connected to where it came from. Let's keep going.

The watermelon's birthplace is Africa. Africa has many wild watermelon species in the genus Citrullus. They mostly grow in arid regions, in sandy soil. Some look very much like modern watermelons, though they're usually much smaller, the kind you could hold in one hand. The color and stripes? One look and you'd know it's a watermelon.

But once you cut one of these wild watermelons open, it's a completely different story. The flesh inside isn't red at all. It's mostly white or pale green. Some are very hard, others are soft like a sponge, and they taste bitter and astringent. Everything about them says: this is not something you can eat directly.

So this is puzzling, right? Why would anyone ever think of growing this thing? It's hard to imagine.

A similar question came up in our corn episode. Corn's ancestor also looked inedible, as hard as tiny pebbles. Scientists guess that it was burned, popped like popcorn, and that's when the Indigenous peoples of the Americas noticed it and discovered its value.

So what was the potential value of the wild watermelon? Its water.

In the Kalahari Desert in southwestern Africa, there's a wild watermelon called "tsamma." During the dry season, local Indigenous people treated these melons as a water source, like little water bottles, very convenient to carry. Later, besides collecting the wild ones, they also began growing some themselves as a water reserve, which was vital for surviving the dry season.

Of course, storing water is also the plant's own survival strategy. In the rainy season, it soaks up as much water as it can and stores it in its flesh. In the dry season, it uses that water to attract all kinds of animals. Once the animals eat it, they help spread its seeds. That's its purpose.

So this trait is especially useful in dry regions. Humans and animals alike love watermelon. The likely reason humans first started cultivating and breeding it is precisely this water. That's the hypothesis, at least.

Now, to be more specific: the genus Citrullus has seven living species. So which of them is the wild ancestor of the modern watermelon? To understand the origin of the watermelon, we have to start with ancient Egyptian tombs more than 4,000 years old.

When did humans first start growing watermelons? The earliest clear record comes from ancient Egypt. The Egyptians had domesticated the watermelon at least 4,000 years ago. Watermelons in their tomb paintings already look a lot like today's: elongated, unlike the round wild ones. That suggests they were cultivated by people.

Then the next question: which wild watermelon did the ancient Egyptians domesticate? That puzzled scientists for a long time. Several wild species could have been candidates.

Here's another example. In West Africa, people also grew a wild watermelon called the "egusi melon." Locals didn't use it for water, and they didn't eat its flesh as fruit. They grew it for its seeds, which are very numerous and very nutritious, rich in fat and protein. West Africans make a traditional dish from them called egusi soup. People used to wonder whether the egusi melon might be the ancestor of the modern watermelon, but that was only speculation.

Then, finally, in 2019, scientists found a clue.

In ancient Egyptian belief, death was not the end. The dead went on to the heavens. So their tombs were stocked with the essentials for the journey: bread, honey, meat, and also clothes, money, even pets, as grave goods.

And what about drinking water along the way? That's right: watermelon.

The ancient Egyptians placed watermelons in tombs so the dead would have something to drink on the road. This applied to pharaohs too. In Tutankhamun's tomb, for example, watermelon seeds and paintings of watermelons were found, which shows that watermelons were already very common in ancient Egypt.

So the clue scientists found comes from an ancient Egyptian tomb about 3,500 years old. When that tomb was excavated, fragments of watermelon leaves were found on the mummies. Archaeologists preserved them.

In 2019, a team in Germany studied these fragments and found they were so well preserved that high-quality DNA could still be extracted. Genetic analysis showed that this watermelon was closely related to a watermelon subspecies from Sudan called the "Kordofan melon."

But because Sudan has been unstable and at war for a long time, finding the wild ancestor of the watermelon would have to wait.

Beyond the watermelon fragments, scientists also found two very important gene mutations.

One is a mutation in the gene that produces cucurbitacin. Why are wild watermelons bitter? Because they contain cucurbitacin, the source of the watermelon's bitterness. This mutation shows that by that time, watermelons had already stopped being bitter.

The other is a mutation in the gene that produces lycopene. Lycopene, also known as tomato red, is what makes tomatoes red, and it's also the source of the watermelon's red color. The mutation in the lycopene gene shows that by that time, watermelon flesh was already red.

Some studies have also found that the gene that makes watermelon sweet is linked to the lycopene gene. That means the watermelons ancient Egyptians ate 3,500 years ago may already have been quite delicious, not just a source of water.

Making watermelon tasty also set the stage for it to spread around the world. If it had only been good for storing water, other places might not have needed it at all. So the delicious watermelons we can eat today owe a great deal to the foundation the ancient Egyptians laid.

Since ancient Egypt, watermelons have been bred around the world for thousands of years, and today there are over a thousand varieties. The flesh isn't always red, and the shape isn't always round. You can find all kinds.

And since the watermelon is the representative of giant fruits, naturally a lot of people are passionate about one thing: growing them as big as possible.

The current Guinness World Record for the largest watermelon was grown in 2013 by a farmer in Tennessee, USA. It weighed an astonishing 159 kg. That's ridiculous, and who knows how they ate it.

Some are big, and some are expensive. Certain watermelon varieties are considered premium. For example, in Japan there's a watermelon called Densuke, a black-skinned variety said to be extraordinarily delicious. Before it goes on sale, it has to be polished and buffed. That's how particular they are.

And how expensive is it? In 2008, a Densuke watermelon sold at auction for 650,000 yen. Pretty eye-opening.

Some people find the seeds annoying when they eat watermelon and wish it had none. So seedless watermelons appeared on the market.

Seedless watermelons were first invented by the Japanese around 1939. The method was to treat the seeds or seedlings of an ordinary diploid watermelon with an alkaloid called colchicine, turning them into tetraploid plants. Then they crossed these tetraploids with diploids to produce triploids. Triploids can't form seeds, so they became seedless watermelons. That's the principle.

Although the watermelon is just an ordinary fruit, some people even turned it into a tool for discrimination.

The cause goes back to the American Civil War we talked about in the last episode, on cotton. After slavery was abolished, many Black Americans no longer worked as enslaved laborers on cotton plantations. They started growing and selling watermelons to make a living.

The plantation owners, who could no longer enslave them, were resentful. So they spread propaganda to mock and ridicule Black people, claiming they were lazy and satisfied with just a slice of watermelon. In short, it was an extremely arrogant display.

As a result, some people began using watermelon eating to discriminate against Black people and to show off their own sense of superiority. Even now, some still bring it up, which is really absurd.

Old jokes more than a hundred years old really should have been retired long ago. Alright, that's it for this episode of Cold-Knowledge Blind Box. Thanks for listening. See you next time.