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Anya Rossi

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Richard Feynman stood at a Cornell blackboard in 1964 and explained the problem every AI lab is fighting in 2026. The BBC filmed it. Almost nobody watches it. The lecture is about why nature only answers in mathematics. Every team forcing language models to reason is hitting the wall he mapped 62 years ago. He was 46. The Nobel Prize came 11 months later. The footage survived on film reels and now sits free on YouTube with fewer views than a keyboard unboxing. Watch the blackboard section near the middle. He takes 1 of Kepler's laws and rebuilds it from nothing, with notation a 12-year-old can follow. No slides. No jargon. 1 piece of chalk. An ML engineer I know paused it 4 times and made his whole team watch it before standup. You're 62 years late. The lecture is still free.

Richard Feynman stood at a Cornell blackboard in 1964 and explained the problem every AI lab is fighting in 2026. The BBC filmed it. Almost nobody watches it. The lecture is about why nature only answers in mathematics. Every team forcing language models to reason is hitting the wall he mapped 62 years ago. He was 46. The Nobel Prize came 11 months later. The footage survived on film reels and now sits free on YouTube with fewer views than a keyboard unboxing. Watch the blackboard section near the middle. He takes 1 of Kepler's laws and rebuilds it from nothing, with notation a 12-year-old can follow. No slides. No jargon. 1 piece of chalk. An ML engineer I know paused it 4 times and made his whole team watch it before standup. You're 62 years late. The lecture is still free.

2,554,324 просмотров • 11 дней назад

He didn't believe the Kimi K3 hype. So he ran it against Claude Fable 5 on camera. Same prompts, both models, 12 minutes, results raw on screen. No cherry-picking, nowhere to hide. The context makes it spicy: Moonshot itself admits K3 trails Fable 5 overall while posting wins on coding benchmarks. 2.8 trillion parameters against the model everyone pays for. Benchmarks are marketing until somebody runs them at home. This is somebody running them at home. Watch the middle section where the outputs go up side by side. The gap is visible before he says a word. The verdict lands in the last minute. No spoilers. Leaderboards don't write code. Models do.

He didn't believe the Kimi K3 hype. So he ran it against Claude Fable 5 on camera. Same prompts, both models, 12 minutes, results raw on screen. No cherry-picking, nowhere to hide. The context makes it spicy: Moonshot itself admits K3 trails Fable 5 overall while posting wins on coding benchmarks. 2.8 trillion parameters against the model everyone pays for. Benchmarks are marketing until somebody runs them at home. This is somebody running them at home. Watch the middle section where the outputs go up side by side. The gap is visible before he says a word. The verdict lands in the last minute. No spoilers. Leaderboards don't write code. Models do.

147,860 просмотров • 11 дней назад

Richard Feynman stood at a blackboard in 1964 and drew the line between knowing and understanding. Every AI benchmark in 2026 sits on the wrong side of it. Almost nobody watches the clip. His father taught him the difference as a boy. You can know a bird's name in every language and know nothing about the bird. Names track labels. Understanding survives when the labels change. He carried that into physics. You can compute every answer and understand none of them. A student who memorizes the formula knows. A student who can rebuild it from nothing understands. Now run today's models through his test. They name everything, compute everything, pass every exam. The 1 question underneath hasn't moved: is anything understood. He couldn't have imagined the machines. He built their hardest eval 62 years early. Watch him at the board. He never recites. He rebuilds the idea live in front of the room, which was exactly his point. No slides. No notes. 1 piece of chalk. Benchmarks measure knowing. Nobody has built one for the other thing. You're 62 years late. The clip is still free.

Richard Feynman stood at a blackboard in 1964 and drew the line between knowing and understanding. Every AI benchmark in 2026 sits on the wrong side of it. Almost nobody watches the clip. His father taught him the difference as a boy. You can know a bird's name in every language and know nothing about the bird. Names track labels. Understanding survives when the labels change. He carried that into physics. You can compute every answer and understand none of them. A student who memorizes the formula knows. A student who can rebuild it from nothing understands. Now run today's models through his test. They name everything, compute everything, pass every exam. The 1 question underneath hasn't moved: is anything understood. He couldn't have imagined the machines. He built their hardest eval 62 years early. Watch him at the board. He never recites. He rebuilds the idea live in front of the room, which was exactly his point. No slides. No notes. 1 piece of chalk. Benchmarks measure knowing. Nobody has built one for the other thing. You're 62 years late. The clip is still free.

14,863 просмотров • 7 дней назад

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