Py_nk

i am eternally frustrated by my inability to simply be better. my discipline to improve remains fulfilling, yet undeniably inadequate as my insatiable wants forever loom with contempt

kinda crazy how there's a million sorting algorithms and a solid chunk of them are actually practically used in different situations but there's really only one distributed consensus algorithm and basically everything is built on top of it with minor variations

we will likely find out soon whether the Linux kernel is offense-dominant or defense-dominant

You don't truly understand the magnitude of the potential impact of powerful AI on the world unless you are aware, and have fully internalized, that senior leadership and most researchers at the frontier labs *actually believe* the following: 1. Existing AI is already significantly speeding up AI research. Very soon (this year), AI will very likely take over *ALL* aspects of AI research other than generation of novel research ideas. Soon (within the next 2 years), AI will very likely take over *ALL* aspects of AI research, period. This means hundreds of thousands of GPUs working 24/7 to discover novel ideas at the level of, or better than, the likes of Alec Radford, Ilya Sutskever, etc. The thread below presents a conservative timeline: AI researchers will "meaningfully contribute" to AI development in 1-3 years. 2. Many (but, as far as I can tell, not all) executives and researchers at the frontier labs believe that fully automated AI research will kick off recursive self-improvement (RSI), wherein the AI models will autonomously build better and better AI models, with human oversight (for safety reasons), but increasingly with no human input into the research or implementation of that research. From the thread below: "'[h]umans vs AI on intellectual work is likely to be like human runner vs a Porsche in a race', likely very soon" - but replace "intellectual work" generally with "AI research" specifically. RSI is a complicated and messy thing to consider, both because there will be compute and energy constrains and because there are unknowns (will there be diminishing returns from greater intelligence of the models? if so, when will these diminishing returns become meaningful? is there a ceiling to intelligence that we don't know about?). But suffice to say that, if RSI *is* achieved in a way that many leaders/researchers at the frontier labs believe is possible, *THE WORLD MAY BECOME COMPLETELY UNRECOGNIZABLE WITHIN JUST A FEW YEARS*. This is subject to various bottlenecks; as the thread below correctly notes, "[i]nstitutional, personal & regulatory bottlenecks will bind very hard", and much also depends on continuing progress in areas like robotics. 3. On ~the same timeline as full, end-to-end automation of *ALL* aspects of AI research (within the next 2 years), AI will also become capable of making significant novel scientific discoveries *IN OTHER FIELDS*. This is why Dario Amodei, Demis Hassabis et al. believe that it is possible that all diseases will be curable within 10 years. (One account of how this might be possible is set forth in "Machines of Loving Grace".) The point is that an LLM that is capable of significant novel insights in the field of AI research should likewise be capable of significant novel insights in at least some (and perhaps all) other fields. The thread below notes: "AI for automating science [is] very early" - obviously true, but I think some changes may be right on the horizon. Overall, and again from the thread below: "'a million scientists in a data center' will think much more quickly than humans, on almost any intellectual task; this will happen in the next 2-10 years." This is ~the same timeline as that presented in "Machines of Loving Grace". Many will be tempted to dismiss all this as "just hype", "they are just trying to raise money again", etc. But no! - the above, in fact, presents the *actual beliefs* of senior leadership and many researchers at the frontier labs. Again, they genuinely think that AI research will be automated soon. Many of them genuinely believe that RSI is achievable in the not-too-distant future. And they genuinely see a real path towards AI significantly accelerating science, curing diseases, inventing new materials, helping to solve key global issues from poverty to climate change, etc., etc. Whether the frontier labs' beliefs are correct is, of course, a separate question. I personally have historically tended to take public statements by OpenAI, Anthropic and Google at face value and quite seriously. As a result, I was not surprised when LLMs won gold in the IMO, IOI and the ICPC competitions last year, or when Claude Code/Codex started taking off, or when Anthropic and OpenAI started releasing significantly better models every 1-2 months, or when some of the best coders became reliant on Claude Code/Codex in their daily work, or when LLMs became significantly helpful to scientists in fields like math and physics in the last few months. The trajectory has been ~the same as that publicly predicted by the frontier labs. We have been accelerating. And, as of right now, all signs are indicating that the acceleration shall continue and that full automation of AI research and, potentially, RSI are firmly on the horizon.

one of the reasons it’s hard to learn with LLMs is they don’t explain common failure modes necessary for intuition see this random dudes blog vs sonnet 4.6 on attention

these 3 properties totally characterize the determinant.

Really impressive work! It took just a few hours to formalize two classic algorithms in game theory. One of them took me three weeks, and I wrote nearly 2K lines Lean code, while Aristotle agent only spent three hours and used 400 lines. I am so surprised

Yesterday, John Morgan and I completed our “Covering Spaces Project”, whose goal was to formally (in Lean @leanprover, of course) prove the Fundamental Theorem of Algebra (that polynomials have complex roots) using only continuity: covering spaces, trivializations, and winding numbers, instead of the Mathlib proof that goes through complex analysis / Liouville. We started the morning about 30% through the proof, and tried playing with the Codex VS code extension (running GPT-5.4, extra high reasoning). We had already written a complete natural language blueprint (last June, at the Simons @SimonsFdn Lean workshop) and were meeting for about an hour a month, leisurely working through the details by hand (John wanted to learn the process). We’d used AI before (mostly Claude) to help move things along, but this was the first time that we met after I turned on Codex. I started by asking for Codex to give me just the formal *statement* corresponding to our next unfinished leaf in the dependency graph. It thought for 5 mins, and gave what looked like a reasonable statement. So I said, ok, can you now give a formal proof? It thought for 10 mins, and came back with a full proof, including helper lemmas. I asked it to add natural language around the helper lemmas so we’d see what they’re doing in the blueprint. It thought for 5 mins, and did it. We went on to the next statement, then the next proof, and it got those too. It continued like this for an hour or so, and we jumped to about 60% done with the project, amazing progress! I had to run to get on a train to DC (for the DARPA meeting). Once in my seat, I decided on a whim to just tell Codex to keep working on the whole file and get as far as it could, stopping to ask me for help if it got stuck. I left VS Code running in the background, while working on other things. After an hour, I remembered to check back on what progress Codex had made. It was done. And so was the project! Now I’m having Codex go through the whole proof all over again, remove all our bespoke definitions and statements, and make it clean and Mathlib-ready. Many more hours of iteration later, and it’s ready to go as a PR. Wild wild times we’re living in!

someone traced the all the non profit grants for the age verification laws old.reddit.com/r/linux/commen…

Three.js is CPU limited by JavaScript. Bevy on the other hand… > compiles to WASM > cache optimized ECS game engine > code first > WebGPU + WebTransport > also builds to native: windows, Linux, macOS, android, iOS WASM has a way to go still, lacking multithreading and it needs to be glued by JavaScript, but this is being worked on.

Now we're up to 117 formal solutions to Erdos problems, up from single digits just four months ago. Over 3/4 are powered by Aristotle.

i wish i would've taken more math classes

I can suggest an equation that has the potential to impact the future: P = NP + AI This equation combines Stephen Cook’s famous equation P=NP, which relates problems that can be solved in polynomial time (P) with those that can be verified in polynomial time (NP), with the addition of AI (Artificial Intelligence). By including AI in the equation, it symbolizes the way in which the ability to use RL post-training on solved problems means that any problem which is programmatically verifiable (NP) necessarily becomes one that can be algorithmically automated (P). My equation highlights the potential for AI to saturate all possible evals while still eluding capability in domains that lack straightforward verification.

Most people don’t hate math. They hate the cognitive friction of missing prerequisites.

this finally clocked for me after opus 4.6/codex 5.3. if you're a CS major who is: 1) not at a t5 cs school 2) not in the top decile of your class with regards to engineering talent/ability 3) not in the weeds of learning this new ecosystem of skills/MCPs/parallel agent orchestration techniques (i.e., the majority of CS majors) your swe job prospects are extremely bleak. basically non-existent.

박사 동기 중에서 매일 오전 9시에 착석해서 밤 9시까지 논문을 쓰는 사람이 있었음. 눈이 와도 비가 와도 그 루틴을 반복했음. 그렇게 4년을 살더니 동기 중 유일하게, 졸업식도 하기 전에 학계에 자리를 잡았음. 난 그렇게 살진 못할 것임. 하지만 묵묵한 꾸준함이라는게 얼마나 중요한지 느꼈음

Math textbooks are written in a pointlessly obtuse way. Gemini does an incomparably better job. My professional opinion is that all undergrads learning real analysis should give up reading baby Rudin, and simply learn analysis from Gemini instead

an example of software that falls into the genre of poweruserslop: Obsidian

It’s funny how Java developers aren’t worried about AI because they know their managers don’t actually care about accomplishing anything

Legitimately, a math problem that might take you 10 years might take Terence Tao 10 seconds. But it's ok, there are billions of math problems, more than Tao can solve in a lifetime, so if you like math your job is just to find one Tao happens not to be interested in