Navier-Stokes Announcement

Post by nomel » Sat, Sep 12, 2026, 8:54 PM UTC

program_whiz wrote: post #231

jacobrussell wrote: post #167

Earlier quoted context omitted.

I don’t think solving a millennium prize problem can be reduced to some DoorDash economics of “spent Y to make X.” What if it took someone their entire professional career to solve one of these problems, would it not be worth it by the same logic?

Surprisingly this is actually rather fitting in terms of time scale. When you consider it took ~10,000 agents 88 hours, or 880,000 hours to solve. That's 14.5 years in agent time of continuous 365/24/7 processing. Of course, humans solve things much more efficiently (and didn't also need the massive pre-training of every expert on the planet for 1,000,000,000 human years equivalent). But yeah, human researchers can s…

Post by [deleted] » Sat, Sep 12, 2026, 9:03 PM UTC

vdomi wrote: post #196

famouswaffles wrote: post #177

Earlier quoted context omitted.

None of the frontier labs care about Chess as it's already a solved problem. If they did, the models would be much better. It's really not that hard. Google has a paper on grandmaster level chess without search from transformers. Better obviously means better, like how they became better than they were 6 months and a year ago.

I would describe better as how much of my work I can delegate to the agent. Right now I'm delegating much more to Astra high than 6 months ago to Opus 4.6. Every dev has this feeling, it's weird to even argue what a better model/harness means.

Post by athrowaway3z » Sat, Sep 12, 2026, 9:14 PM UTC

auggierose wrote: post #209

athrowaway3z wrote: post #154

Earlier quoted context omitted.

Something like this sketch work for you? peer(X, 0) :- founding_peer(X). electorate(T, count ) :- peer(Y, T). support(X, T, count ) :- candidate(X), peer(Y, T), recognizes(Y, X, T+1). peer(X, T+1) :- support(X, T, Votes), electorate(T, Total), 2 * Votes > Total.

Still waiting for a definition. People giving nonsense reviews is a feature of the peer review system though, yes.

Post by qlte » Sat, Sep 12, 2026, 9:26 PM UTC

famouswaffles wrote: post #177

fragmede wrote: post #87

Earlier quoted context omitted.

No it isn't. Best and worst and ill-defined anyway but the chess ELO score of various LLMs has fluctuated up and down, it's not been montonically increasing. What is the best answer to "how do I make cocaine"? The models are getting larger, with more compute and RAM backing them, but that doesn't automatically make them better if you don't define how you're measuring better-ness.

None of the frontier labs care about Chess as it's already a solved problem. If they did, the models would be much better. It's really not that hard. Google has a paper on grandmaster level chess without search from transformers. Better obviously means better, like how they became better than they were 6 months and a year ago.

Post by HarHarVeryFunny » Sat, Sep 12, 2026, 9:32 PM UTC

vatsachak wrote: post #221

HarHarVeryFunny wrote: post #215

Earlier quoted context omitted.

I also think they overestimate how much of most jobs can be done by an LLM (a text generator!), and how much importance and reliance most companies put into soft skills, and non-linguistic understanding/feels, both during interviewing (are they Googley-enough?) and afterwards. For example, how do you reconcile return-to-work mandates with the idea that companies are going to be happy with faceless remote workers? Wha…

Yeah. Not to mention the paper-clip maximizing that RL induces in them. I had 5.6-Luna use a parser combinator lib in order to find out that it imported it but wrote its own parser, so it technically followed my instructions. Imagine that paper-clip maximizing happening over millions of tasks. I think we are just entering the LLM + Human era.