Knuth’s shadow over CS makes this a huge turning point.
First semester Berkeley EECS, grinding through TAOCP (only 3 vols then), when it clicked: “Wait… that’s Jenny’s dad!” (HS friend with his daughter in Palo Alto)
@karpathy@karpathy WASM: most deployed IR on Earth. Your post sparked this → https://t.co/2gU2V86h24 Biology-inspired self-improving agent: LLM synthesizes WASM funcs, persists in db, shifts compute from probabilistic/$ → deterministic/¢. Grows like QT45 RNA self-replication for code.
Just posted a preprint for a project I've been working on at the UCSC Genomics Institute, "CytoVerse: Single-Cell AI Foundation Models in the Browser". In particular it explores latent space sharing in science:
Preprint: https://t.co/tPh3kA72at
Tool: https://t.co/j0ETfHhAUh
@VigilantFox Super complex problem with no magic fix.
Personal responsibility for our health (like Bill said) is huge and 100% in our hands.
Admin costs? That’s a beast we can’t tame solo; our only real lever is the voting booth.
🚨TESLA AUTOPILOT BEATS HUMAN DRIVERS BY A MILE
New data shows Tesla’s Autopilot might actually be a better driver than… well, you.
According to Tesla’s 2025 safety report, cars using Autopilot only crash about once every 6.69 million miles.
A rate of 0.15 crashes per million miles.
For comparison, human drivers crash at 1.42 per million miles, and even Teslas without Autopilot hit 1.04.
Even Waymo’s self-driving cars, which have an impressive record, average 0.21 crashes per million miles.
In short:
Humans = crashy.
Tesla Autopilot = chill.
So next time someone says they don’t trust “robots” on the road, remind them the robots are 10 times less likely to wreck your car.
Source: @Tesla, @xfreeze
The @karpathy interview
0:00:00 – AGI is still a decade away
0:30:33 – LLM cognitive deficits
0:40:53 – RL is terrible
0:50:26 – How do humans learn?
1:07:13 – AGI will blend into 2% GDP growth
1:18:24 – ASI
1:33:38 – Evolution of intelligence & culture
1:43:43 - Why self driving took so long
1:57:08 - Future of education
Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!
Wonder if the same holds for different content - specifically biorxiv or general scientific content vs. the more consistent financial reporting in SEC filings.
🧵 1/ Recent hype suggests long-context LLMs remove the need for retrieval in RAG pipelines—"just put all your docs in context." We tested this theory rigorously in finance, focusing on SEC filings.
Spoiler: Retrieval & chunking strategies still dominate.
It's 2025 and most content is still written for humans instead of LLMs. 99.9% of attention is about to be LLM attention, not human attention.
E.g. 99% of libraries still have docs that basically render to some pretty .html static pages assuming a human will click through them. In 2025 the docs should be a single your_project.md text file that is intended to go into the context window of an LLM.
Repeat for everything.
@jeremyphoward Great musicians don’t play thinking about scales and theory, they transcend to a latent space: “First you learn the instrument, then you learn the music, then you forget all that s**t and just play.”
Charlie Parker
HHS on the blockchain? Maybe its time for some sunlight on what works, doesn't and how much $. It was a decade too early back in 2018: https://t.co/zIN0JHvvoS
I'm surprised this hasn't caused more waves post DeepSeek R1. Has anyone verified it independently? Feels like we are at the 'compile the compiler' phase. Another step down for Mag7?
🤔 How many examples does an LLM need to learn competition-level math?
Conventional wisdom: 100,000+ examples
Our discovery: Just 817 carefully chosen ones 🤩
With pure SFT, LIMO achieves:
57.1% on AIME
94.8% on MATH
LIMO: Less is More for Reasoning 📝
🔗 https://t.co/FE1lhsuAgA
Bitcoin in El Salvador Field Report:
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#cryptocurrency#bitcoin#elsalvador