FireChess – Find the chess mistakes you keep repeating

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Hey HN, I'm a solo dev and I built FireChess because I kept losing to the same openings without realizing it. Lichess and Chess.com both have great post-game analysis, but they review one game at a time. I wanted something that looks across hundreds of games at once and says: "You've played this position 14 times and lose 70% of the time — here's what to play instead." What it does: - Scans your Lichess or Chess.com games (up to 5,000) - Finds repeating opening leaks — positions where you consistently pick the wrong move - Cross-references the Lichess opening explorer to tell you how popular/sound each line is - Detects missed tactics and endgame mistakes across all your games - Runs Stockfish 18 (WASM) entirely in your browser — no server-side engine needed - Includes a drill mode so you can practice the correct moves until they stick - The free tier gives you 300 games at depth 12. Pro ($5/mo) unlocks 5,000 games, higher depth, and full tactics/endgame scanning. Tech stack: Next.js, Stockfish 18 WASM, Lichess Explorer API, Stripe. All engine analysis runs client-side in your browser — game data is only stored server-side if you save a report. I have a short trailer here: https://www.youtube.com/watch?v=m7oUz7t8uZA Would love feedback on the analysis quality or anything else. Happy to answer questions about the Stockfish WASM integration or the pattern-detection approach.

Visit product View on Hacker NewsFeb 25, 2026

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

89%89% 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: stripe, open · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% predicted probability of success on AppSumo, 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.

Incorrect prediction on native model

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