I built an imperfect copy of CSS Scan

Hacker News

While I was learning CSS, I always felt the urge to inspect and edit the styles of the elements right there in the browser. Opening developer tools seemed frustrating. I looked for tools to do that. I found CSS Scan but it was way expensive. Finally, I built a tool on my own for my use. It is not perfect. But now I can inspect and copy the styles of the elements as well as edit them right there in the browser. I will continue adding updates to it as I learn more about CSS and programming as a whole. But it is my first ever project and I am quite happy to share it.

Visit product View on Hacker NewsNov 30, 2022

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · 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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

30%30% 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

17%17% 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.

Incorrect prediction on native model

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