Building a Chess Coach
TL;DR · AI Summary
Magnus Carlsen's chess app uses Stockfish, tactical detectors, and an LLM to evaluate and explain moves.
Key Takeaways
- LLMs are not suitable for direct position reasoning; dedicated engines should ha
- Stockfish evaluates positions, detectors extract tactics, and the LLM translates
- User feedback is injected into development via Slack, improving iteration effici
Outline
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Magnus Carlsen's app needed to explain moves, not just evaluate them.
Stockfish evaluates, detectors extract tactics, and the LLM translates into English.
User feedback is injected into development via Slack for prompt and model optimization.
Mindmap
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查看大纲文本（无障碍 / 无 JS 友好）
- Building a Chess Coach
- 系统组件
- Stockfish - 评估
- 检测器 - 提取战术
- LLM - 翻译
- 反馈机制
- 用户标记错误评论
- Slack 注入开发流程
- Claude Code 会话优化
Highlights
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LLMs hallucinate when asked to reason about positions, so they split the work.
The feedback loop is the interesting part: users flag bad commentary in the app, it posts to Slack and injects into a running Claude Code session via Channels.
During the demo Anant was merging the PR from his phone.
Magnus Carlsen's app needed to explain chess moves, not just evaluate them. LLMs hallucinate when asked to reason about positions, so they split the work. Stockfish evaluates, detectors extract tactical concepts, the LLM just translates into English. Sub 3 https://t.co/0jswUVxICN" / X
AI Engineer on X: "Building a Chess Coach Magnus Carlsen's app needed to explain chess moves, not just evaluate them. LLMs hallucinate when asked to reason about positions, so they split the work. Stockfish evaluates, detectors extract tactical concepts, the LLM just translates into English. Sub 3 https://t.co/0jswUVxICN" / X
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Building a Chess Coach Magnus Carlsen's app needed to explain chess moves, not just evaluate them. LLMs hallucinate when asked to reason about positions, so they split the work. Stockfish evaluates, detectors extract tactical concepts, the LLM just translates into English. Sub 3 seconds on Gemini Flash. https://youtube.com/watch?v=FlzpEG HNVKQ… The feedback loop is the interesting part. Users flag bad commentary in the app, it posts to Slack and injects into a running Claude Code session via Channels. Claude investigates, tweaks prompts or detectors, regenerates, asks questions back in Slack. During the demo Anant was merging the PR from his phone.
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