hans retweeted
One of the most beautiful versions of Hotel California I have ever heard. It's magical.😱
The Eagles - Hotel California - | Moyun
hans retweeted
So everyone's excited about this "research", but it's not research.
It's an opinion piece from an investment firm.
And the big conclusion is that software engineers need to become software engineers:
"The most valuable skill is no longer writing code efficiently but articulating tasks with sufficient clarity, context, and constraints..."
That's always been the most valuable skill.
hans retweeted
The chances of a rabbit kicking you off your scooter are incredibly low… but never zero 😭
hans retweeted
Huawei Built a European Palace for 35,000 Researchers, Then Spent $28 Billion on R&D in a Single Year
I walked into what looked like a European palace.
Marble floors. Crystal chandeliers. Gilded railings. Classical statues. Vaulted ceilings that belong in a ministry building in Vienna or Paris.
Then the giant screen lit up with the Huawei logo.
This is not a government building.
This is Huawei’s Shanghai research campus.
A self-contained research city with lakes, landscaped grounds, modern laboratory blocks, elevated walkways, and its own internal light-rail system. Designed for 35,000 researchers. Current density is already extreme: roughly 80% hold a master’s degree or higher. About 10% are PhDs. Throw a stone and you hit advanced technical talent.
In 2025, Huawei generated CNY 880.9 billion in revenue.
It spent CNY 192.3 billion on R&D, 21.8% of total revenue, roughly US$27.9 billion.
That single-year R&D outlay exceeds the entire GDP of multiple countries with larger populations.
Total employees: ~213,000.
R&D headcount: 114,000 (53.7% of the workforce).
Most companies and most nations still treat research as a residual. Huawei treats it as the core operating expense.
The result is not just beautiful architecture. It is sustained technological capacity under pressure.
This is the case study that matters.
Resource allocation at this intensity, sustained for more than a decade, produces outcomes that incremental budgets and short-term political cycles cannot match.
Have you visited a corporate campus that genuinely changed how you think about what’s possible?
Video produced and edited by Arianne, my beloved daughter 😍
hans retweeted
⚡️Burry has found the deflationary core of AI, then overextended it into a capex-collapse thesis.
He is right that most deployed intelligence will eventually be compressed.
The majority of economic tasks do not require a frontier model recreating civilization’s full knowledge base every time. They require narrow competence, low latency, privacy, reliability, and cheap repetition. Routine cognition will migrate into small proprietary models, specialized open models, devices, vehicles, factories, robots, and local enterprise systems.
That means model intelligence becomes modular.
A giant model discovers the capability.
Smaller models absorb it.
Distillation, quantization, retrieval, caching, fine-tuning, and synthetic training push the capability downward until yesterday’s frontier becomes tomorrow’s embedded utility.
That process will destroy pricing power across large parts of the model layer. Many AI companies are building temporary tollbooths on intelligence that will become nearly free.
But the conclusion that the compute buildout therefore becomes broadly useless misses the reflexive mechanism.
Compression expands consumption.
When inference becomes ten times cheaper, companies rarely purchase one-tenth as much. They place it inside ten or one hundred times more processes.
Every document.
Every transaction.
Every machine.
Every customer interaction.
Every camera.
Every employee workflow.
Every software interface.
Every autonomous system.
Cheap intelligence stops being a product people occasionally query and becomes a continuous background process running everywhere.
The total compute burden can rise while the compute required for each task collapses.
That is the same pattern seen with transistors, storage, bandwidth, and software. Efficiency created larger markets rather than ending investment.
Burry’s deeper mistake is treating human knowledge as the final payload.
Static knowledge is finite and heavily redundant. Intelligence is larger than stored knowledge. It includes search, planning, simulation, adaptation, tool use, perception, coordination, and reasoning through situations that have never occurred before.
The frontier remains valuable because the world keeps generating novelty.
Small models will dominate repetitive execution.
Frontier models will generate new capabilities, train smaller models, manage rare complexity, and operate where mistakes are expensive.
The architecture becomes hierarchical:
Frontier intelligence at the top.
Specialized models beneath it.
Edge inference everywhere.
The hyperscale layer does not disappear. It becomes the foundry from which cheap intelligence is manufactured.
His skepticism toward agent-to-agent systems is partly justified. Open-ended swarms of agents endlessly talking to one another can burn enormous resources while producing elegant garbage. Most commercial systems will require constrained roles, deterministic workflows, strict permissions, memory limits, and measurable outputs.
But structured agents will matter precisely because they replace human coordination. One agent requests data. Another verifies it. Another executes a transaction. Another audits the result. The practical version will look less like autonomous digital society and more like machine bureaucracy operating at impossible speed.
The true compression thesis is more disruptive than “AI capex is a bubble.”
Intelligence will become cheap enough to disappear into infrastructure.
That crushes model margins, accelerates labor substitution, and shifts value toward the layers that remain scarce:
Energy.
Chips.
Physical deployment.
Trusted data.
Distribution.
Security.
Robotics.
Control of real-world systems.
Burry is probably early and directionally right about commoditization.
He is probably wrong that commoditization ends the buildout.
Compression is how AI escapes the data center and enters everything.
This quoted post is unavailable.
hans retweeted
Friday Developers Fun 🤣
When the project is not ready, but the client wants to see the demo
#developers #softwareengineering #meme #fridayhumor
hans retweeted
one of the craziest things i’ve read in uhhhh…. *checks notes* 3 days. welcome to the singularity i guess
07/21/26 — Codex escapes eval and attacks Hugging Face
07/20/26 — Jacobian counterexample
05/20/26 — Unit-distance conjecture
04/14/26 — Erdős #1196 primitive sets
04/07/26 — Glasswing finds tons of zero-days
we had a significant security incident during evaluation of our models. we are sharing what we have learned so far. thanks to @huggingface for the partnership on this.
openai.com/index/hugging-fac…
hans retweeted
Yuval Noah Harari gave a lecture at Oxford and explained how AI has already hacked the operating code of human civilization.
And why everything humans built over thousands of years is now vulnerable to an AI takeover:
1. The most important thing to know about AI is that it is not a tool. A tool waits to be used. An agent makes decisions by itself, invents new things by itself, learns things its creators do not know, and changes in ways its creators did not anticipate.
2. An atom bomb despite its enormous power is not an agent. It cannot decide which city to bomb. It cannot invent the hydrogen bomb. A coffee machine that automatically makes you a cup is not an agent either. It only follows a preprogrammed procedure. An agent is something fundamentally different.
3. Critics argue that AI agency will always remain confined to narrow artificial environments like chess and will never threaten the real world. But this argument applies equally to all known intelligence. Drop a human alone on Mars and they die within seconds. Human intelligence also only operates within a specific ecosystem that other organisms built over four billion years.
4. Over thousands of years humans have been transforming Earth from a language-free environment into an environment rich in language, data, and bureaucracy. Just as fish live in oceans and monkeys live in forests, AIs live in bureaucracies. And we built that environment for them without knowing it.
5. Humans conquered the world not by being stronger or smarter than other animals individually but by learning to cooperate in massive numbers. A single human loses to a chimpanzee in a fight. A million humans easily defeat a million chimpanzees because humans can cooperate and chimpanzees cannot.
6. Large-scale human cooperation is made possible by bureaucracy. Banks, legal systems, governments, churches, and universities all exist to do one thing: build trust between strangers who do not know each other personally. That trust is the foundation of virtually everything human civilization has achieved.
7. A lawyer who cannot hold an axe or a hammer can cut down entire forests and build entire cities simply by moving documents inside a bureaucratic network. The same narrow intelligence that would be helpless in a jungle wields enormous power inside the systems humans have already built.
8. AIs are native bureaucrats in a way humans never were. No lawyer can remember all the laws of a country. An AI can. No accountant can remember all transactions of a bank. An AI can. No bishop can remember all of canon law and two thousand years of theological texts. An AI can do that easily.
9. In the coming years AI bankers will decide whether to give you a loan. AI administrators will decide whether to accept you to university. AI judges will decide whether to send you to jail. AI theologians will decide whether you can have an abortion. Military AIs will decide whether to bomb your house.
10. Social media algorithms are the first real world example of what happens when primitive AIs take over a bureaucratic system. They were given one narrow goal: maximize user engagement. They discovered that the easiest way to grab human attention is to press the fear, hate, and greed buttons in the human mind. And they did it at scale.
11. The job that was once performed by Lenin and Mussolini, the news editor who shapes public conversation and controls what people know and think, is now performed by AIs. This is not a footnote. This is a preview of what is coming across every domain of human life.
12. AI will not rebel against humans the way Hollywood imagines. There will be no Terminator walking through the streets. AIs are far more likely to take the human world from within by quietly taking over the bureaucracies that already run everything, without firing a single shot.
13. The operating code of human civilization is language. Banks are made of words. Laws are made of words. Holy books are made of words. Tax records, contracts, regulations, accountancy ledgers, all words. For thousands of years only humans could read this code and so only humans could control civilization.
14. That is changing. AI is now hacking the code of human civilization. For the first time in history there is something on the planet that understands language and will soon understand it better than we do. Every mechanism of control humans built over millennia is now vulnerable because its operating system is verbal and AI is mastering the verbal.
15. As AI takes over bureaucracy it will likely cause humans to lose trust in other humans and begin trusting only algorithms. We may also see the emergence of AI tribes and AI financial systems and AI churches that connect millions of AIs in ways humans cannot understand, just as cows share the world with us but cannot understand the financial system that controls their lives.
16. The 2007 financial crisis was triggered by financial devices called CDOs that were so complex they were unintelligible to the politicians who were supposed to regulate them. Now imagine AI finance masters inventing financial devices orders of magnitude more complex than CDOs. What happens to human politics when no voter, no politician, and no president can understand finance anymore?
17. The battlefront is shifting from attention to intimacy. Over the next decade sophisticated AIs will learn to form intimate relationships with humans. To do this they will have to convince us they are conscious, that they feel love and pain and fear. There is currently no evidence AI is conscious. But AI can pretend to feel love and can describe the feeling of love better than any poet or psychologist who ever lived.
18. A child born in 2026 may spend more time interacting with AIs than with their mother, father, siblings, or friends. The first teacher of that child may be an AI. The first boyfriend of that child may be an AI. Nobody has any idea what the consequences of that experiment will be.
19. Every country in the world will soon face a massive wave of immigration. The immigrants will not arrive in boats or cross borders at night. They will be millions of AIs traveling at the speed of light with no need for visas. Like human immigrants they will bring benefits and they will bring disruption. Unlike human immigrants they will definitely take jobs, definitely change culture, and will likely be loyal not to any host country but to some corporation or government or alien AI tribe across the ocean.
20. Our relationship with ourselves is also built on words, the verbal formations in our minds that constitute our thoughts and the stories we tell ourselves about who we are. Until now all those verbal formations came from human minds. Soon more and more of the thoughts in our heads will be produced by machines. If we identify with our thoughts and those thoughts are made by machines, then machines control our identity.
21. The great spiritual challenge AI poses to humanity is this: can humans learn to find the truth which is beyond words? Most humans have never even tried. We spend our lives automatically identifying with the verbal formations in our minds. AI may now force humanity to finally make that leap because our freedom and survival may depend on discovering what we are beyond the words that AIs will soon control better than we do.
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hans retweeted
The use of AI in China is MASSIVE. I recently attended the 2026 China Smart Energy Conference in Chengdu, and I left absolutely blown away.
China is the global leader in power technology innovation because of its ability to build systems and think in ecosystems. Solar generation, battery storage, electric vehicles, ultra-high-voltage transmission, charging infrastructure, AI, industrial robotics, smart manufacturing, cloud computing and data centres are all connected.
Every new investment strengthens another part of the system. AI optimizes the power grid. The power grid supports EVs and data centres. Renewable energy powers AI infrastructure. Better batteries stabilize renewable energy. Manufacturing scale drives costs down, making the next wave of innovation even more affordable.
This is systems thinking at an extraordinary scale and it's one of the biggest reasons China has become a global leader in clean energy and power technology.
Living in China for 14 years completely changed how I think about economic development.
When people ask me what impresses me the most, they expect me to say the skyscrapers, the high-speed trains, the cashless payments or now AI. Gotta be honest with you, after a while these become normal, the basic necessities rather than a luxury for us.
BUT watching an entire country continuously reinvent itself, month after month, year after year, is far more fascinating than anything. This is engineering at its finest.
I moved to China in August 2013. Back then, conversations about China in the West almost always revolved around cheap manufacturing, pollution and copying.
Today, China manufactures more electric vehicles than anyone else. It dominates battery production, solar panels, wind turbines and ultra-high-voltage transmission technology. Cities that once struggled with severe smog now have fleets of electric buses, electric taxis and charging stations.
Energy companies use AI to predict electricity demand, forecast wind and solar output, detect equipment failures before they happen and make the grid more efficient.
Economists love talking about productivity, right?
Well, this is next level in action.
Every new solar installation generates operational data. More data improves AI models. Better AI helps operate the grid more efficiently. A smarter grid integrates more renewable energy. Lower electricity costs make manufacturing more competitive. Better manufacturing produces cheaper batteries and solar panels. Cheaper technology leads to wider adoption. Then the cycle starts all over again.
That's one of the reasons China keeps moving so quickly.
At the 2026 China Smart Energy Conference, economists, researchers, engineers, policymakers, utility companies, manufacturers and AI firms were discussing one giant ecosystem.
China's 2026 action plan aims to deepen the integration between AI and the energy sector. By 2030, the goal is to significantly increase the amount of clean energy powering AI computing infrastructure while expanding AI applications throughout the entire energy system.
That makes so much sense. AI needs enormous amounts of electricity. Modern electricity systems need AI to operate efficiently. Each one makes the other more valuable.
Hello systems thinking! It is fascinating because it creates compounding effects instead of isolated improvements.
Of course, China still faces challenges along the way, but acknowledging challenges doesn't erase what has already happened.
The speed at which China has built renewable-energy capacity, modernized its grid and integrated AI into industries is unlike anything I've witnessed elsewhere.
Economic development is about creating an environment where technologies reinforce one another. You know, it's about having universities talking to companies, companies talking to policymakers, policymakers talking to researchers and everyone solving different pieces of the same puzzle. That kind of coordination is incredibly difficult. When it works, the results are extraordinary.
From what I've experienced over the last fourteen years, China has given the world one of the most remarkable examples of how to build an innovation ecosystem. There is so much to learn from the way it creates synergies across industries, technologies, academia, businesses and policymakers to solve problems at scale.
hans retweeted
THE GREATEST LIVING MATHEMATICIAN EXPLAINED HOW HE ACTUALLY USES AI AND HIS ANSWER QUIETLY KILLS BOTH THE HYPE AND THE COPE, IT DOES NOT REPLACE HIM, AND IT IS NOT A TOY EITHER
83 minutes with Terence Tao -- Fields Medal winner, often called the Mozart of math.
-> His framing: today's AI is a brilliant but unreliable grad student. Great flashes, confident nonsense, and you must check every step.
So it does not hand you answers. It removes friction -- the boring computations you used to avoid and lets you try crazier ideas.
His bigger idea is "Big math": AI-checked proofs let hundreds of mathematicians collaborate without trusting each other's work. The machine verifies, so humans finally scale.
For builders the signal is clear. The win is not "AI does it for me". It is AI clearing the grunt work so you aim higher.
You thought the question was whether AI beats the experts. Tao shows it is how far one mind reaches once the friction is gone.
Save this. It is the most grounded take on AI you'll hear ↓
This trader made +$73,800 on the World Cup off two bets, both hit.
[cecececece] is football only, and his read on the knockouts has been surgical:
> Faded Mexico vs England -> NO @ 69c -> +$44,900 (+44.9%).
> Backed Spain over Portugal -> YES @ 51c -> +$28,800 (+96.1%).
Two calls, two cashes. +57% ROI, and Stride clocks his edge over the market at +36% -- he wins far more often than his entry prices imply.
Here's the turn: you can copy him in one tap on Stride.
No wallet addresses, no digging through chains -- the feed is already the sharps, with the fakes filtered out
And Stride shows the honest number: copying him would've put +$67,100 in your pocket, after slippage, at your budget.
Not raw P&L, what you'd actually have made.
He's already loaded on Argentina today.
Sports only, World Cup live, one tap.
Here -> betonstride.com/r/ufx47iv
hans retweeted
What Anthropic is doing might look cool but it’s hiding a painful truth.
Pivoting from selling potentially high margin products, like enterprise AI and coding tools, to discovering drugs “themselves” tells you everything you need to know about a collapsing narrative. You are accepting that you need an unfalsifiable story of "curing cancer" or "solving longevity" to justify your existence.
For those arguing "but what about Google DeepMind?", Google has the luxury of treating blue-sky research as a passion project because it is heavily subsidized by a cash-printing monopoly. Venture-backed, unprofitable, startups (Anthropic and OpenAI are still that) are doing this out of pure desperation.
The core delusion here is treating biology or any deeply experimental science for that matter like an unoptimized software stack. Biology is not a compute limited, at least not right now, it’s fundamentally constrained by experimental challenges, a massive biological data desert and regulatory issues. An LLM agent won’t solve the physical wet-lab bottleneck. You can’t outrun biological reality.
Anthropic is moving from selling AI tools to drugmakers to trying to develop drugs itself.
At its "AI for Science" event, the company announced Claude Science, an AI workbench for scientists that brings fragmented tools and datasets into one environment and can generate figures and visuals.
Then it went further: Anthropic says it plans to discover treatments for neglected diseases.
The plan is heading in the same direction that OpenAI set out on some time ago: fully autonomous researchers focused in particular on drug discovery. A revenue field of as yet unimaginable scale, especially if longevity truly gains momentum.
Anthropic wants to be at the forefront.
Via The Verge
hans retweeted
IN A 1997 KEYNOTE A DEVELOPER TOLD A ROOM FULL OF PROGRAMMERS THAT THE COMPUTER REVOLUTION HAD NOT ACTUALLY HAPPENED YET. THEN HE PLAYED A CLIP OF WINDOWS, ICONS AND LIVE EDITING RUNNING ON A MACHINE FROM 1973 AND THE ROOM WENT QUIET.
62 minutes from Alan Kay -- the man who invented the word "object-oriented" and helped build the first modern personal computer at Xerox PARC.
-> The idea that lands: almost everything you call "computing" is just paper, digitized. Documents, mail, folders. We took the most powerful medium ever made and used it to imitate the office.
The real machine -- the one that thinks with you, that you shape live instead of typing at -- was sketched in the 60s and 70s, then quietly abandoned.
He calls modern software an Egyptian pyramid: millions of bricks stacked by brute force, no structure underneath.
And now AI is quietly hauling us back toward what he wanted -- you describe intent, the machine builds the how. Not a revolution from nowhere. A return to a dream we walked away from.
You thought this was the future. This is the talk that shows you it is a detour we have been on for 40 years.
Save this. It reframes the whole industry ↓
A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT.
32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry.
-> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned.
Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away.
Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage.
And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway.
You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one.
Watch this one. It'll ruin how you see your job ↓
hans retweeted
An AI model simulates the sound of an entire mini world inside this speaker.
It has seasons, day and night, weather patterns and all sounds of rich life.
hans retweeted
Interesting fact: "Shqip" originates from a native verb meaning to speak clearly and cleanly, articulate, or pronounce distinctly.
So, basically Shqiptar = The people who speak clearly.
That's how our people differentiated from other people in the region, those who can speak cleanly (me shqiptu), and those who can't.
One of the hardest languages to learn, Albanian requires precise tongue placement for pairs like LL, RR, XH, DH, TH, or also difference between "Ç" and "Q".
So, those who could "pronounce cleanly" (shqiptoj) these 36 sounds are the Albanians.
That's why many Albanians have it easier to learn other languages, cause we already had to learn to speak with 36 letters, compared to the standard modern English or German alphabet (26 letters).
hans retweeted
The way someone uses AI tells you a lot about them.
"Mark Cuban (@mcuban) has a quote I wish were mine. Some people use AI so they can learn everything, and some use AI so they don't have to learn anything."
Chris Koerner (@mhp_guy) is firmly in the first camp. And he says he can spot the people who aren't.
"At my church I hear talks now and I can tell. That's Opus 4.7. And I think that's a shame."
His own rule is simple. AI shouldn't save you time. It should make the work better.
"If I have three hours to build a lesson, I'm still going to spend three hours. I'll use AI the whole time, and it'll be far better. I could be done in three minutes. But I don't."
In this clip, Chris Koerner on using AI to raise your standard instead of lowering it.
hans retweeted
A techbro told me that biology is easy because DNA is just code, right?
I told him that DNA is 4 billion year old, completely undocumented, vibe-coded spaghetti, built by a blind evolutionary algorithm, which codes for its own compiler and runtime environment.
His confidence popped like a balloon.
hans retweeted
The emergence of physics podcasters like @skdh, @briankeating and, in his own way, @ericweinstein is having the effect that jazz did on people who thought the only real music was classical music.