ChessBoss – enhancing physical chessboards with computer vision

Hacker News

We built ChessBoss at the TechCrunch Disrupt hackathon this week. It didn’t make the top 10 but I thought Hacker News might think it’s pretty cool. https://devpost.com/software/chess-boss There are these really cool smart chessboards that can suggest moves and track your games... but they’re $400 and weigh 19 pounds. And of course there are apps that can analyze games but tracking and inputting games by hand is a huge pain. Or fully-digital chess apps... but board games are way more fun in real life! We wondered: “why can’t you just do that in software and bring the best parts of chess apps into the real world?” So we did! A camera passes a feed of the board through our machine learning model which interprets the state of the board and passes it off to Stockfish to display move suggestions in real time. We didn’t quite get to recording the state over time in PGN but we hope to continue this project and add that soon! Would love to know what you think. We’re working on enhancing other board games with computer vision as well; if you want to help us beta test sign up at https://boardboss.com

Visit product View on Hacker NewsOct 4, 2019

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

91%91% 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.

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

65%65% 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 HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apps · Missing: agents, macos, agent

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% 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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time, smart, real world · Missing: web3, chat, crypto

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