KolenCh
Joined 5 years ago
Karma
348
Submissions
383
Member since
Sep 18
Recent submissions
Comment · 3 days ago
The most interesting bit to me is how they focus on addressing the scale and reliability issue in the agentic era by adding more resources and rewrite the "primitives on how they built the foundation" as they call it. The numbers are impressive! Let's hope we're going to get a more reliable GitHub soon (they say early next year I think.)
The number: 180x faster against old architecture.
More at https://blog.kolen.dev/link/github-universe-2026
Comment · 5 days ago
Interesting that the physics result is not released yet. From the limited wordings, “unified completion of the Standard Model of particle physics” is probably SU(5), which is experimentally disproved as one describing our universe. So the wording sounds a bit confusing, ie they are not proving or discovering Grand Unified Theory (GUT), and there’s no Higgs. So it is not really the standard model, but models that’s closely related to that. It is still an impressive result. IMO at the level of the recent quasi Riemann hypothesis level result, shy of getting the millennium prize (no mass gap) but is actually a very meaningful result, both of which arguably more impactful that the one they solved a millennium prize problem (Navier–Stokes).
More comments in https://blog.kolen.dev/link/openai-shepherds
Comment · 22 days ago
> refuse to engage
You speak of this as if the default is to engage.
To make an analogy: math is like OSS. You are free to fork and create your own feature, maintainer has no obligation to look at your PR and improve it to a state that’s worth merging. Eg This happened to Zig where the Bun team maintained a fork for a while. And I’m sure there’re countless examples. An instance of a maintainer finding a PR so interesting and keep digesting it and eventually even give a talk about it, is irrelevant. It is entirely their free will to do so.
Comment · 1 month ago
Somehow I’m expecting to see Bousso’s work here. Eg
Comment · 1 month ago
Software engineers have learnt to read the reaction to coding agents as a split between people who love the craft and people who care about the product. Seen through that lens, mathematicians upset about OpenAI’s Navier–Stokes result look like craft-lovers in denial, moving the goalposts. I think that misreads them. “After Math”, by Silvia De Toffoli and Eamon Duede, argues that mathematics was never after the answer: the understanding is the product. Then I extrapolate to research software, where I’d argue the same holds for code that establishes scientific results.
Comment · 1 month ago
My previous submission covers the Navier–Stokes “incident” itself. Here I want to look at a parallel: AI agents are finding security bugs and producing mathematical proofs faster than the people working in these fields can absorb them.
Can mathematics borrow from coordinated vulnerability disclosure? I’d propose a registry that records work as it develops, with timestamps that others can verify, together with circles that gradually widen before public release. This would give people a record of their contribution without having to rush an unfinished proof out. But it only helps if we also give credit to ideas and partial results.
Comment · 2 months ago
Have you read the article? It has an unbalanced emdash: `word— another word`. A novice mistake that an LLM likely will not commit nowadays.
In standard typography—depending on which guide you need to follow — it can only be padded by space on both side or neither.
Comment · 3 months ago
I used to distro-hop a lot and in recent years settled with RHEL. The key change over the years is how fragile the supply chain becomes, so while distro like Arch Linux is a great learning experience, I just want to put a layer of maintainers who have higher stake than others between me and the world.
(RHEL has a free tier for developers limited to 16 instances.)