SnipCSS – Extract CSS from any website section, turn into components

Hacker News

Hello HN! SnipCSS is a Free Chrome extension (with pro version) that I think many web developers would find useful! If you ever tried to copy styles from devtools to recreate a design you know it's very tedious to select each element needed and copy styles. SnipCSS automates that better than anyone. Nothing else merges/splices styles into a usable snippet that stays true to the original design. After getting the extension working well enough to collect snippets, I took it a lot further. I added features to scope the CSS, templatize repeating items, inject lorem ipsum data, and then convert snippets to React/Vue with ChatGPT. I'm even building a template directory of snippets based on the designs I've collected from random websites. Making a universal tool to componentize anything on a web is a big project so it took me a couple years to get here. During that time I've gotten very little feedback. I really suck at talking to users. So please let me know your thoughts and leave a comment!

Visit product View on Hacker NewsJul 25, 2023

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

50%50% 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 · Strong signals: users · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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