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SubReply CSS – An exprimental classless CSS style that I am working on
Hacker NewsSubReply CSS – An exprimental classless CSS style that I am working on
Hey HN! Recently I came across SubReply[0], a social media site, and its CSS style is really nice. So nice that I decided to rip it and modified it to become a classless CSS style. I spend about a few hours modding the CSS, removing/replacing class-based part, fixing bugs, and add additional features, such as custom blockquotes. The style will needs some more work, but basically it has done what it needs to do. I would love hearing your feedbacks and comments on the project! [0]: https://subreply.com
Share cardActual performance
3points
2comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released58%58% 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, podcasting52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos50%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, intuitive48%48% 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, personal39%39% 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, revenue19%19% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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