Flexboxle – A Daily Puzzle with Flexbox

Hacker News

I built Flexboxle, a daily puzzle game where you use Flexbox to rearrange blocks to match a target goal state. How it works: * Your goal is to arrange a set of blocks into a specific pattern using Tailwind CSS Flexbox classes (and <divs>) * After every submission, every block is graded as perfect (green), close (yellow), or wrong (gray) * You have 6 attempts per puzzle * The game checks block positions, not your specific CSS so there can be multiple solutions that work * There is a new puzzle every day so come back tomorrow for a new challenge The trickiest part of building this game was making it playable on mobile. Highlighting sections, navigating in an editor, and adding in common HTML characters like <, >, /, and = are easy on desktop but pretty annoying to do on mobile. I eventually settled on a quick add panel with all of the controls you need so you can easily apply classes, group elements together in a div, and see all of the supported classes. Let me know if you have any feedback! Enjoy!

Visit product View on Hacker NewsJan 31, 2026

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

82%82% 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: new, using · Missing: mac, agents, macos

50%50% 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% 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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