ChessRabbit – The AI Chess Analysis Platform

Hacker News

Hi HN! I wanted to share with you a chess analysis tool that I was building for the past week. First of all I want to explain what is the problem that I'm trying to solve: Chess Engines such as stockfish are superior for us humans, but it's not self explanatory. A player can review their game by stockfish and it will tell them that some moves are very bad and losing, but why? This is the problem that we are trying to solve. ChessRabbit acts as a chess agent with stockfish evaluation tools. It takes every move that the player plays and it reasons about it, with the help, validation, and confirmation of Stockfish. So instead of showing the user: "Blunder", we show them: "This is a blunder because your opponent plays Rook to d6, attacking your queen and leaving your bishop on d5 overloaded and lost. You should have played Qd4 to protect your bishop in the center and maintain your coordination." Give it a try and let me know if you have a feedback!

Visit product View on Hacker NewsAug 28, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, plain · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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