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Nimble.css minimal classless CSS library for great-looking default HTML

Hacker News

Nimble.css minimal classless CSS library for great-looking default HTML

Source code: https://github.com/Leftium/nimble.css --- # About https://PicoCSS.com was a great starting point for projects, but there was some friction: - Global styles leaked into other components (like Userfront widgets or @vincjo/datatables) - Too many breakpoints: I just wanted a single breakpoint for phones, otherwise responsive based on content/screen width. - Too many CSS variables polluting dev tools. - No longer maintained: SASS warnings polluting terminal output. - Missing concept of "surfaces." --- I just wanted a classless CSS library that worked well with https://open-props.style . Basically a nicer-looking Open Props Normalize[1]. So I built Nimble.css! (Assisted by Claude.) PicoCSS-inspired aesthetics combined with Open Props curated values. [1]: https://codepen.io/argyleink/pen/KKvRORE

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Actual performance

4points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, user, single · Missing: mac, agents, macos
76%76% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
52%52% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: widgets · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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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