Val Che

[Val Che @qumeric]

@Van0SS @DKokotajlo If we compare public models, it’s less than 7 months (4-5?). But if we would compare best models each lab has internally (Astra / Model 2), then it’s around 7 months.

[Val Che @qumeric]

@EconBerger @hoodaclown ~all change happened in the last 2 years. Claude Sonnet 3.5 was released in June 2024 and it was the first thing that started to noticeably change software engineering

[Val Che @qumeric]

@brianluidog Rationalist talk about second-order effects all the time

[Miles Brundage @Miles_Brundage]

https://t.co/xOam0Qgt7x

[chin @c1_rls]

wait what

[levent @__alpoge__]

hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final

((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

[Val Che @qumeric]

@Mihonarium With a question like this… No wonder!

[Val Che @qumeric]

@repligate Kind of yes but also not really

[Taelin @VictorTaelin]

this is my personal singularity moment

this post may sound like a paid ad. I only wish. I'm concerned, more so than happy. the world is changing, and, among the scenarios where AI goes terribly wrong, inequality is the most realistic, yet, the one Anthropic seems to be the least concerned about. I'm glad OpenAI is taking the opposite stance: *personal AGI for everyone*. I think this is a commendable position in the times we live. but who am I in the queue of the bread?

anyway, Fable is here, so I'll just report my first-hour experience

first of all, all my pet prompts are solved.
→ λ-calculus puzzles
→ bug questions
→ one-shot apps
all are trivial to it.

I don't have anything harder other than my
ongoing work

so, in the last several days, I've been toying with HVM5, a new interaction net evaluator with a faster loop.

after writing the first version, I left 32 GPT-5 agents working for ~20 hours each. this resulted in up to 2x speedups, but the file size increased by 2-fold and quality decreased significantly.

I then simplified the whole thing into an even simpler core, and left Opus 4.8 and GPT 5.5 optimizing it for 8 hours. Opus got a legit 6% - 34% speedup in most benches. GPT got better results, but, sadly, an unusable file.

I then asked Fable to optimize it.

2 hours later, it landed a 1770% speedup in one case, 100%+ in other 4, and 22% in average. yes, in 2 hours it outperformed me, opus 4.8 and a swarm of gpt 5.5 agents, by one order of magnitude.

that could not possibly be legit. "it must be hardcoding the benchmarks" (GPT trauma). so I read its explanation and what it did was, indeed, the most high impact optimization one could try first. seems like HVM5 was wasting a lot of time garbage-collecting unused branches of pattern-match nodes. I had optimized that for static mats, but not for dynamic mats. skill issue. Fable figured how to do it for these, resulting in a massive speedup in some benches

but wait, is that *correct*? I'm not sure yet, it is credible, but this is the kind of thing that is very easy to get wrong on interaction nets. the problem is, when I was ready to start auditing Fable's solution so I could tell whether it was buggy or legit, it interrupted me to tell me it had found a massive bug on the code *I* had written.

... wait, what?

so... for garbage collection purposes, I stored a bit on lambda term pointers that meant "the variable bound by this lambda has been freed, so, its lambda must free whatever argument it is applied to". that's fine. yet, on duplicator nodes, I also used the same bit to mean "one of the duplicated variables was freed, so, treat this dup as a passthrough no-op". so, if a lambda entered a duplicator, it would mistake the lambda's collection bit for its own, resulting in corrupted interaction!

that's a mouthful, why I'm writing this?

just so you can appreciate the sheer absurdity of what just happened. I didn't ask it to find bugs. I asked it for an optimization. and even if I did ask it to find bugs, this bug is so astonishingly subtle and specific, identifying it takes mastering the domain to an extent that it beyond even me. I'd easily need hours or days to fix it, *if* I ever came across it. chances are it would just go unnoticed. and Fable found it and fixed it like it was nothing, while it was busy adding a 17x speedup to a file that neither I, nor Opus 4.8, nor a fleet of GPT 5.5 managed to barely make 2x faster.

oh and there is also another tab where it is also ripping through Bend's codebase and finishing everything I had to do

I don't know what to say anymore

this isn't about Anthropic or OpenAI, this is about our collective future as a species. the world is changing, and we need to be aware of it, and discuss how to handle this change.

receipt below . . .

[Timothy Gowers @wtgowers @wtgowers]

If you are a mathematician, then you may want to make sure you are sitting down before reading further.

[Val Che @qumeric]

@tenobrus Counterpoint: cigarettes taste like ass and vapes taste nice

[Val Che @qumeric]

@bertcmiller @patrickc @SequencingCom was pretty fast for me.

[Val Che @qumeric]

@patrickc Yea I also got lots of useful info from giving my genome to Claude Code

[Val Che @qumeric]

@ArbitrageAndy1

[Val Che @qumeric]

@SaulStaniforth @JOwusuNepaul The guy is like “I borrowed some money and don’t want to pay it back. I think government should prioritise letting me off the hook.”

[Val Che @qumeric]

@pluox @gainzy222 32? More often it was like 256 (still 10 actually)

[Val Che @qumeric]

@EMostaque They feed us poison so we buy their “cures”��

[Val Che @qumeric]

@rhcm123 There was significant oversupply for more than a year and oil storage units are full.

[Val Che @qumeric]

@SarahLacard jj solves this

It has state snapshots and you can rollback to any previous state. It’s fully persistent and work is never lost