Ml

Mlut – Tailwind CSS alternative for custom websites and creative coding

Hacker News

Mlut – Tailwind CSS alternative for custom websites and creative coding

Hey HN community! I want to tell you about my open source project: mlut. This is the best CSS framework for custom websites and creative coding. It is similar to Tailwind in that it is based on the Atomic CSS approach, but in some respects it surpasses all popular analogues. mlut helps to create projects with individual and non-standard designs, where old-school frameworks are not suitable and LLMs perform poorly. The project is the result of in-depth research and over 1,000 hours of work. Details about the study can be found here: https://dev.to/mr150/atomic-css-deep-dive-1hee Key features: - Short and consistent naming. If you know CSS, you almost know mlut. The abbreviations are based on the popularity of CSS properties and are compiled according to a single algorithm - Rich and native syntax. It is like Vim for CSS. Conveniently create complex styles with a powerful syntax that is conceptually close to CSS - Written in Sass. Leverage the full power of the preprocessor in your handwritten CSS and easily link it to utility classes You can create even pure CSS art using utility classes! With mlut it is possible — check out our gallery on the website: https://mlut.style/arts I would appreciate any feedback, and especially stars on GitHub: https://github.com/mlutcss/mlut

Visit product View on Hacker NewsFeb 25, 2026

Actual performance

5points

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, coding · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, 000, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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.

Incorrect prediction on native model

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