21–30 of 88 posts
> propose, implement, measure, keep the wins Pretty much what I did to let Codex with gpt5.4xhigh improve my fairly complex CUDA kernel which resulted in 20x throughput improvement.
Concretely, what interesting changes did it make to achieve such a significant improvement?
> The frontier is the verifier. Um, yes? The big value that AMD had in the x86 market over competitors was their verification model. This has been known for decades. > 3-seed nextpnr P&R on a Gowin GW2A-LV18 (Tang Nano 20K) — median Fmax × CoreMark iter/cycle = fitness Every single "improvement" is basically about routing around how absolutely abysmally bad the Gowin FPGAs are. Kudos to that, I guess? Gowin FPGAs hav…
Earlier quoted context omitted.
Wtf, this has a name now? I thought of this exact idea literally months ago but never had the time to do any experiments on it. At the time I dismissed it as potentially being incredibly expensive for the improvement you do get, and runs into typical pitfalls of evolutionary algorithms (in the same way evolution doesn't let an organism grow a wheel, your LLM evolution algorithm will never come up with something that…
This is not a new idea at all, many many have had it, no one really can claim it
Earlier quoted context omitted.
This is not a new idea at all, many many have had it, no one really can claim it
Stigler's law of eponymy https://en.wikipedia.org/wiki/Stigler%27s_law_of_eponymy
> The same observation had previously also been made by many others.
Earlier quoted context omitted.
Wtf, this has a name now? I thought of this exact idea literally months ago but never had the time to do any experiments on it. At the time I dismissed it as potentially being incredibly expensive for the improvement you do get, and runs into typical pitfalls of evolutionary algorithms (in the same way evolution doesn't let an organism grow a wheel, your LLM evolution algorithm will never come up with something that…
Genetic algorithms have existed since the 60s / 70s, e.g. computers learning to play a game. LLMs aren’t particularly guide at it. I think hyperparameter tuning may actually be a kind of genetic algorithm.
Hyperparam tuning is usually done by Bayesian Optimization though.
Has anyone actually written a verifier for a business / project?
> "If you can write the rules down, an agent will satisfy them faster than your team will." a fantastic opportunity to become the next next big thing and write a verifier verifier. at the hypothesized inflexion point where AI instantly performs exactly as commanded, what happens to heavily regulated industries like medical? do we get huge leaps and bounds everywhere EXCEPT where it matters, or is regulation going to…
Earlier quoted context omitted.
Genetic algorithms have existed since the 60s / 70s, e.g. computers learning to play a game. LLMs aren’t particularly guide at it. I think hyperparameter tuning may actually be a kind of genetic algorithm.
Hyperparameter tuning could be done by genetic algorithm. I think it’s a bit of a category error to say that it is a genetic algorithm though. Hyperparam tuning is usually done by Bayesian Optimization though.