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A fast command-line tool for solving Queens puzzles

Hacker News

A fast command-line tool for solving Queens puzzles

I've been hooked on Queens puzzles ( https://www.linkedin.com/games/queens/ ) for the last few months, and decided to try and build a solver for them. Rather than doing a traditional solution search, I tried to model it instead on how humans solve these puzzles, iteratively eliminating and confirming possible squares for Queens. And since this was a side-project, I decided to go overboard, and try and make it as fast as possible (avoiding HashMap/HashSet in favor of bit fields, for example).

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% 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: ide, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
37%37% 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.

Incorrect prediction on native model

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