World’s Most Iconic Graduate School Recommendation Letter ✍️

Fascinating article about Navier-Stokes solution and what mathematicians have learned … “so far humanity has learned very little from the solution. Although they believe the proof to be technically correct, the dense, 166-page manuscript drafted by AI is proving to be a difficult read. "So far it's been very difficult to really extract any human understanding from this new AI proof," said James Maynard, a mathematician at the University of Oxford. The paper is not written for humans," said Javier Gómez-Serrano, a mathematician at Brown University who uses AI in his own research. He said he believes the proof could help advance the field after "some serious re-writing," but "as of today, the paper doesn't teach us much."” npr.org/2026/09/22/nx-s1-596…

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

I hereby rename "Computer Science" to "Super Science." Putting "Computer" in front makes it sound like a fake science, but it's very real, super real, the realest of the real

Opus 5.5 communicates more naturally, addressing some of the most common feedback we heard on Opus 5. It puts the most important information up front and follows the writing rules you give it, which makes long sessions easier to follow.

you cannot out-anti-butthole me

when i was 17, i flew 4,236 km to san francisco and turned down the most selective Ivy League program instead of college, i lived out of wework, hacked on AI models, while figuring out how to get an o1 visa as a teenager 🇺🇸 luckily i met @DavidSHolz who was kind enough to give me a place to sleep and warm clothing. while there i watched him quit his job to start a discord server... i thought mf was crazy. that became @midjourney at 18, i joined a hacker house and lived with @karpathy, @polynoamial. @Mascobot, and other brilliant ai researchers, scientists, engineers, founders it was amazing- whenever a new ai model dropped (whisper, dalle, stable diffusion) we invited our friends over to hack. in 2022, @8enmann (founder of an new ai lab) came over to our house to hack and invited us to try his new Slackbot as one of its first users. that became @claudeai, which launched publicly in 2023 i learned how to build by making dumb projects with cool tech alongside dope people - like putting stable diffusion into a minecraft server and showing karpathy homeless mark zuckerberg generated in real time with blocks my projects proved to other ppl that i could build stuff, which got me the opportunity to work with @naval who sponsored my o1 visa (ty 🐐) after much visa anxiety getting my o1 and airchat getting acquired, I started giving out "Alien of Extraordinary Ability" hoodies as a meme which serendipitously started @extraordinary. now Extraordinary helps the smartest people in the world come to 🇺🇸 and powers frontier companies like @Ramp, @Cognition, @llmh, @a16z building in san francisco has been incredible. There's no other place like it in the world - the concentration of genius talent is insane. ppl are so willing to help you if you are earnest. i couldn't be where i am today if i stayed in Canada or went to college! today @gaganbiyani, @bhorowitz, @pmarca, @eriktorenberg and @a16z are launching the Horowitz Andreessen Acadamy man i wish i had something like this when i first came to san francisco. it was so hard doing it myself i'm honored to be a mentor for the upcoming college opt-outs who want to spend their time building frotnier technology and making a dent on the universe! LFG

Just learned that the Paper Assistant Tool (PAT) that ICLR uses for auto-generating llm-reviews is powered by Gemini 2.5 Deep Think. To which I respond: What makes Gemini 2.5 DT believe it's qualified to judge the paper I wrote with Fable 5.1 Max and GPT 6 Astra Ultra?