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fcten · V2EX

做了一个大模型 leaderboard 网站 最近一个月 CodeX 疯狂送重置，token 根本用不完，顺手做点东西。 地址：[知行录]( https://leaderboard.cn/) 排行依据主要为模型官方基准测试成绩。非主观排名。 数据会持续更新。如果有点用，欢迎各位 v 友收藏～

@nodeloc_cc · X

🌈 7月，你好，MODELOC上线算力池。 MODELOC自上线以来，已检测2000余次，覆盖600+中转站，为众多AI用户提供的使用参考。 MODELOC近期进行了改版，上线了算力池及市场。 加入算力池 查看帖子： 用 MODELOC 便宜地调各家大模型：一次讲清它的价格体系

davidtsong · HN

Benchmarklist: track AI benchmarks (2.4k+), models, and capabilities

u/trekhleb · Reddit

I kept pasting the same question into ChatGPT, Claude, and Gemini in three tabs; so I built a Yes-Brainer — a council of AI models, that answer your question in parallel, debate to consensus, or get judged to a verdict. submitted by /u/trekhleb to r/SideProject [link] [comments]

fristovic · HN

She watched me look at model rankings and asked what do the numbers mean... I literally had no good way of explaining it to her so I just came up with something that is approximately in the same ballpark as some of the benchmarks out there lol

cdnsteve · HN

Tokenstead, find AI models for your hardware

fran-mora · HN

I gave 5 AI coding agents one prompt: grow a banana plant through its whole life in three.js: sprout, leaves, flower, fruit, rot, then pups that restart the loop. It's deceptively simple and yet very hard to get right from procedural code: you have to write working three.js and understand how the plant is actually built; how it hangs, ages and decays. Get the biology wrong and the code renders something weird. These are agents, not bare models (Claude Code and Codex for now). They can use tools, including playwright to check their work and improve it.

@intheworldofai · X

On the World of AI Bench (vibe-coding composite): Claude Fable 5 → 85.2 GPT-5.6-sol → 82.4 kimi-k3 → 81.5 Moonshot’s K3 just walked in and claimed bronze on one of the toughest coding-focused leaderboards out there.

@Sunkanmii27 · X

@MBrew26730 · X

Dataset cleaning + fine tuning + continual learning at