Neat – Minimalist CSS Framework

Hacker News

Today I fixed a couple little bugs and released a new version of my minimalist CSS framework. I built this to scratch my own itch and I've been using it for a few years as the starting point for most of my own websites. I noticed I hadn't shared this with HN for about 18 months (5 or 6 minor version changes) so I thought it might be okay to do that now. Edit: Err, I missed a post from 7 months ago. Previous Posts: https://news.ycombinator.com/item?id=32990838 (7 months ago) https://news.ycombinator.com/item?id=24594381 (Sept 25, 2021) https://news.ycombinator.com/item?id=26308052 (March 1, 2021) https://news.ycombinator.com/item?id=25660317 (January 20, 2021) https://news.ycombinator.com/item?id=23221957 (May 28, 2020)

Visit product View on Hacker NewsApr 26, 2023

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

76%76% 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

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

22%22% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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