Your company is not slow because the team is lazy. It is slow because every founder meeting produces a different version of reality. Four calls last week. Useful ones. By Friday nobody could agree what actually got decided, who owned it, or what “ship it” even meant. The work did not fail in the room. It leaked in the hour after, across Slack, notebooks, and whoever typed the notes. That is the most expensive bug inside a small business. You keep paying for meetings and buying back the same decisions. I built the working version on rocket.new with @rocketdotnew

This is the part founders feel in their week. No second meeting to remember the first one. No operator reconstructing the call at 11pm. No “I thought you had this.” One working app. Real list. Names on it.

Researchers at Oxford argue that LLMs can't invent anything. It's impossible mathematically. The paper is called "Theory Is All You Need." Teppo Felin and Matthias Holweg take the famous "Attention Is All You Need" title and flip it. Their argument is that AI predicts from the past, while humans reason forward into the future, and those are two different kinds of thinking. Start with the numbers. The authors estimate a large language model trains on roughly 13 trillion tokens. A human reading at 150 words a minute would need about 164,000 years to get through that. A child hears around 20,000 words a day and roughly 36.5 million words in their first five years. It's the same task with wildly different data, and the child still ends up with language that goes far beyond anything they heard. Their point is that the model learns which words tend to follow other words. It becomes a mirror of what people have already written. It doesn't build a theory of how the world works, so it can't step outside its training data. The paper's sharpest thought experiment makes this painful. Imagine an LLM trained in 1633 on every scientific text ever written up to that point. Ask it about Galileo and heliocentrism. Thousands of years of geocentric texts would swamp Galileo's ideas, so the model would tell you he's wrong. It would also rate Tycho Brahe's astrology as more credible than the idea that the Earth moves, because more people had written about astrology. Then there's flight. In 1888 the scientist Joseph LeConte looked at bird data, noted that no bird above 50 pounds could fly, and concluded humans couldn't either. Lord Kelvin, then president of the Royal Society, said he had not the smallest molecule of faith in aerial navigation. The New York Times estimated in 1903 that flight was one to ten million years away. Nine weeks later the Wright brothers flew. The Wrights didn't have better data. They had a theory. They broke flight into three problems, lift, propulsion, and steering, built their own wind tunnels, and generated the data that didn't exist yet. Wilbur wrote in 1900 that he had been "afflicted with the belief that flight is possible to man." Every prediction machine on Earth would have told him no. The authors call this the data belief asymmetry. Every real breakthrough starts with someone believing something the existing data says is wrong. A system trained to minimize surprise can't do that by design. They're not anti AI. They say AI will win at routine, repetitive decisions that extrapolate from the past, which is most decisions. They're just pushing back on the idea that you should replace humans with algorithms whenever possible, which is a direct quote from Kahneman. I use these models every day and this matches what I see. The new stuff comes from the human at the keyboard who decides the data is wrong. LLMs don't think, you do!

DEEPSEEK HAS JUST KILLED THE ENTIRE CODING AGENT INDUSTRY It's called deepseek-harness It's the most complete framework for creating code agents Open source. Claude's most complete plan costs $200 a month. This is FREE And it comes with a brutal idea Everything is a plugin The model The tools The sandbox The UI Even the agent's own loop You can swap out any of those pieces just by tweaking the config Without touching the base code One single command and you have the local web interface: npx @deepseek-ai/dsh web Compatible with DeepSeek, Claude, GPT, Gemini and whatever else you want to plug in It reached over 160 thousand stars in just a few days A frontier lab just dropped for free the exact layer that other companies are selling as premium product I'll leave the repo in the comments

Two economists mathematically proved that AI will destroy the economy. Researchers from Wharton and Boston University published a terryfiying paper called "The AI Layoff Trap." They mapped out the economic end-game of the AI transition, and it exposes a fatal flaw in competitive capitalism. When a company replaces a worker with AI, it captures 100% of the wage savings. But that displaced worker is also a consumer. When they lose their job, they stop buying things. The company gets all the savings, but the loss of consumer demand is spread across the entire economy. If there are 20 competitors in a market, a CEO only absorbs 1/20th of the economic damage their layoffs just created. So every single rational CEO has a mathematical incentive to automate as fast as possible. They can literally see the cliff approaching, and they still step on the gas. It triggers an unavoidable Prisoner’s Dilemma. If you don't automate, your competitors will, and they will crush you on price. It doesn't just hurt workers. It destroys the businesses, too. The economy gets trapped in an automation arms race. Companies fire their workforce to stay competitive, until the entire consumer base is completely hollowed out. At the limit, the paper concludes: “Firms automate their way to boundless productivity and zero demand.” And the scariest part? The researchers mathematically tested every popular fix. Universal Basic Income? Fails. It raises the living standard but doesn't change the corporate incentive to cut jobs. Retraining? Fails. Worker equity? Fails. The paper proves that more competition actually makes the collapse happen faster. And "better" AI makes the damage worse. The only thing that mathematically stops the collapse is a targeted automation tax, forcing companies to pay for the purchasing power they destroy before they automate the job.