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hazard

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Show HN: I trained a chess engine to play like humans

14 ポイント·投稿者 hazard·2 か月前·3 コメント

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hazard
·12 日前·議論
After being in the workforce for decades, this whole issue is just so incomprehensible to me.

I went an ungrad school that was top-5 in engineering. But my experience - and in the experience of other people I've talked to - formal undergrad education was, and always has been, a farce. At best, you learn through working on projects that are meaningful to you and learn "how to be an adult" (and later, you learn how to manage the enormous financial debt you acquired). But more typically, it's pure credentialism - no one cares what your grades were, only what school you graduated from.

The amount of actual learning that goes on from classes is minimal, but somehow we can't shift the overton window away from this silly game of grades that don't measure anything meaningful.

After graduating, I've was asked about my grades exactly twice in my life -- once when I applied to a master's program, and at one job interview (the company had a policy of asking about GPA for anyone who graduated less than 10 years ago).

I'm pro-education but anti-school, and all this nonsense makes me this way even more.
hazard
·14 日前·議論
This looks great, but I'd really like to see associated exercises (and solutions) to make it useful for self-study
hazard
·2 か月前·議論
> Some engineers, given a fixed token budget, generate exponentially more (and better) output. Other engineers waste their tokens. The variance is enormous, and unlike most performance variance, it is now directly measurable. HR has never had a clearer signal of leverage.

This doesn't make sense to me. "A clearer signal of leverage" implies an objective way to measure software engineering output, which has been the white whale of engineering management for the last 50 years.
hazard
·2 か月前·議論
The 100-point buckets are fine-tuned on blitz games from users at that Lichess rating. Lichess ratings tend to be a bit high compared to FIDE/USDF/chess.com ratings. There's a good post at https://chessgoals.com/rating-comparison/ comparing them
hazard
·3 か月前·議論
> The reason that the rich were so rich, Vimes reasoned, was because they managed to spend less money.

The premise is just false. The parable might be true when comparing, say, lower class vs lower-middle-class, or lower-middle-class to middle class. But the difference between upper class and middle class is not "spending less money." It's a vastly different net worth that comes from inheritance, building / running businesses, investments, etc.

The boots theory focuses on the costs, but the real difference comes from the income & net worth
hazard
·3 か月前·議論
I'm always a bit confused by "written in X" as a feature. Rust devs are the worst about this, but not the only offenders.

The language is not a feature!

I'm a C++ programmer, and even I don't care what language the applications I use are written in.

"Fast" is a feature, not that it's in C++
hazard
·6 か月前·議論
And of course equity futures immediately dropped on the news
hazard
·6 か月前·議論
https://www.1e4.ai/

A transformer-based (but not LLM) chess model that plays like a human. The site right now is very rudimentary - no saving games, reviewing games, etc., just playing.

It uses three models: * A move model for what move to make * A clock model for how long to 'think' (inference takes milliseconds, the thinking time is just emulated based on the output of the clock model) * A winner model that predicts the likelihood of each game outcome (white win / black win / draw). If you've seen eval bars when watching chess games online, this isn't quite the same. It's a percentage based outcome, rather than number of centipawns advantage that the usual eval bars use.

Right now it has a model trained on 1700-1800 rating level games from Lichess. You can turn it up and down past that, but I'm working on training models on a wide variety of other rating ranges.

If you're really into computer chess, this is similar to MAIA, but with some extra models and very slightly higher move prediction accuracy compared to the published results of the MAIA-2 paper
hazard
·6 か月前·議論
tldr appears to be that if you work to fatigue it doesn't matter if you fatigue out with high weights vs low weights