mlut – Atomic CSS toolkit with Sass and ergonomics

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Hi HN community! I want to tell you about my open source project: mlut. This is an Atomic CSS toolkit with Sass and ergonomics for creating styles of any complexity. Main features: - Strong naming convention - Almost arbitrary by design - Great ergonomics - Handy extension A detailed breakdown of the features can be found in the readme on GitHub. And if you want to dive deeper into the technical part, check out this article: https://dev.to/mr150/atomic-css-deep-dive-1hee . This is a transcript of my talk at the tier 1 JavaScript conference (HolyJS). In the meantime, I'll list a couple more features of the tool: - Written in Sass and includes all its benefits - JIT (on-demand) and AOT mods are available - Fully customizable: change tokens, utilities names and any settings - Easy to integrate in existing project. No name collisions with your CSS in the most projects. Increasing specificity in one line or for one utility. If you want to learn a bit about the history of the project, you can check out this article: https://dev.to/mr150/mlut-a-new-word-in-the-utility-first-cs... I would appreciate any feedback, and especially stars on GitHub!

Visit product View on Hacker NewsAug 20, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · 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.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos

51%51% 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

43%43% 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

43%43% 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

18%18% 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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