Original Summary

How did I do it? Jev can't generate a move. It's a classification model, so it only picks from options you hand it. That turns out to be easy to work around. Any position has a fixed set of legal moves, so I generate all of them in code and let Jev choose one. Then I built a small app with a basic UI and played it. First I wanted a number, so I ran it against 600 rated Lichess puzzles. It found the right move about one time in five. Random guessing gets one in twenty, so there is real chess knowledge in there. The number that changed my mind was a different one. Jev scores the same on 800-rated puzzles as on 2200-rated ones. Flat, 16% against 23%. A human gets worse as puzzles get harder, and that is what a rating measures. Jev doesn't notice the difference, because it isn't calculating. It finds mate in one about one time in ten. You see it on the board immediately. Jev opens sensibly. It develops, it castles, it takes free material every time I leave something hanging. Then it stops thinking. Game one, it grabbed my e4 pawn with a knight, then took f2 with the same knight two moves later. Both moves look like winning something. The second one cost it the knight, and I was a piece up by move five. Game two, it parked a knight on e5 with my queen sitting on d4 instead of retaking it. It isn't playing badly. It isn't playing. It picks the move that most looks like a chess move, in under half a second, and never asks what I would do next. So chess is a bad use case for a model like Jev, and that's the point. Every one of those moves took under half a second, and when the answer is already in front of the model instead of three moves away, that speed is the whole reason you'd reach for one. You can find the code for this project in my GitHub repo https://github.com/SyedZawwarAhmed/jev-chess   submitted by   /u/Ashamed_Bit8774 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/20 01:00:00