I made app that makes chess books interactive

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Get for Android: https://play.google.com/store/apps/details?id=ai.chessvision... Get for iOS: https://apps.apple.com/us/app/chessvision-ai-ebook-reader/id... How does it work? 1. Open a chess PDF, DjVu, or ePub file 2. Wait for the processing to finish (only needed the first time) 3. Double-tap any chess diagram to interact and analyze The app is a mobile version of https://ebook.chessvision.ai/ which I built some time ago. Over time I've received many requests to create a native mobile experience, so here it is. To make it easy for everyone to try out the app, I’ve created a small PDF book with 18 mate in 2 puzzles from r/chess. You can claim the book directly within the app or download it from https://chessvision.ai/mates_in_2_from_rchess.pdf , then open it in the app. Have fun solving!

Visit product View on Hacker NewsSep 24, 2024

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

94%94% 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.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos

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

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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.

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