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Slitherlinks.com – Play Slitherlink Puzzles Online

Hacker News

Slitherlinks.com – Play Slitherlink Puzzles Online

Hi HN, I built Slitherlinks.com — a site to play Slitherlink puzzles (also known as “Loops” or 数回) in the browser. Some features: • Daily puzzles with varying difficulty (easy → hard). • Multiple grid sizes: 5×5 up to 15×15. • Accounts, trophies, and leaderboards if you like competition. • Simple interactions: click to draw/erase a line, right-click (or Ctrl+click) to mark an edge with ×. • Tech stack: Next.js + TypeScript + PostgreSQL + Phaser.js, responsive design (desktop & mobile). I enjoy logic puzzles a lot, so I wanted to create a smoother, more accessible Slitherlink experience. This is my first time sharing it here, and I’d really appreciate your feedback: • Is the UI/UX intuitive? • How is the mobile experience? • Any features you think are missing? Try it here: https://slitherlinks.com Thanks!

Visit product View on Hacker NewsAug 21, 2025

Actual performance

2points

3comments

Did not reach leaderboard

Launch Intel predictions

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

86%86% 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.

AppSumoStrong fit for a featured deal · Strong signals: intuitive · Missing: plus, platform, reviews

68%68% predicted probability of success on AppSumo, 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

45%45% 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 · Missing: mac, agents, macos

24%24% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

21%21% 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

14%14% 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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