if web2 landing pages were like web3 startups

At the last NIPS held in Denver in 2000, a very prominent ML researcher asked at dinner "what is the most important thing we've learned in ML?" My answer: "the power of gradient descent." His dumfounded facial expression revealed how stupid he found my answer to be.

Troubling news from Shanghai right now.

Apparently I've been on Twitter for 14 years. I wonder how many more years it will survive? 🧐

If you recognize this sculpture you did not party enough in college.

This is an outstanding achievement. The first Foundation model for this sort of task that I know of. Thank you for making it available Yann LeCun!

Elon Musk Interesting point, but an example might make it clearer. Can you think of a prominent person who's currently wasting his talents in software when he could be working on manufacturing and heavy industries?

Politicians who can't distinguish between people who got rich playing zero-sum and positive-sum games literally don't understand the first thing about wealth.

You've seen some of the puzzles o3 failed, but have you seen the attempts? Yesterday, OpenAI's o3 dramatically beat the SOTA at ARC Prize. But there were 34 tasks that even it couldn't solve with 16 hours of thinking. I've compiled and analyzed all of o3's mistakes below 🧵

To forecast AI progress, I think it's relevant to ask yourself: what can be learned? I've been exploring the taxonomies of problems by learnability. Here's a proposal: 𝟭. 𝗘𝘅𝗮𝗰𝘁-𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝗯𝗹𝗲 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, i.e., domains where the rules are fully known,

Fun read on "the death of reading" by Adam Mastroianni: "Reading has already survived several major incursions, which suggests it’s more appealing than we thought. Radio, TV, dial-up, Wi-Fi, TikTok—none of it has been enough to snuff out the human desire to point our pupils at

Your work tools are now interactive in Claude. Draft Slack messages, visualize ideas as Figma diagrams, or build and see Asana timelines.

I love this post. I'm a "messy middle" kind of guy. The opening is easy, and dreaming about the end game if fun. But it's the middle that separates the wheat from the chaff.

Three weeks ago there were rumors that one of the labs had completed its largest ever successful training run, and that the model that emerged from it performed far above both internal expectations and what people assumed the scaling laws would predict. At the time these were

My setup is evolving: - Ghostty is now my default terminal - I run a surprising amount of compute-heavy work (I like picking apart games), so I complement that with btop - Thanks to Opus, all of my HPC stuff is now written in Rust. I'm able to squeeze every single drop out of my

In practice, I let Gemini Deep Think and Opus 4.6 collaborate on the science, with my intent as the guide. My workbench is Yatzy, since it's mathematically interesting yet very easy to play. I use DT as my "theorist" and Opus as my "experimentalist". I prompt them to write as two

I've decided to become more active on X again after years of being away ✍️ Also: I was shocked to find that I've had an account on the platform for over 17 years 😱 I'm getting old... Anyway, why am I returning? Well, as complicated as my emotions about the new management might

As of last week, I'm an investor in Anthropic through Stellatrion Partners Fund IV! 🎉 There is a lot of debate about a possible AI-bubble. In general, I find that opinions don't count for much unless you have skin in the game. Saying stuff is easy 💬 Well, I'm now walking the

Relived that Hungary stared into the abyss and turned back to the light. Authoritarian mafia bosses are a bad choice of leaders...