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Reader Mode without the boring parts

Hacker News

Reader Mode without the boring parts

By boring I mean the text extraction that makes every website look the same and that sometimes fails. Arguably I've spent too many months on this detail, but in addition to the standard DOM iteration, Unclutter uses a CSSOM iteration, patching of mobile styles, CSS word blocklists, and crowdsourced element selectors. Plus an animation system to move text to its "uncluttered" position. Also interesting might be the article "library", a lightweight read-it-later list. It uses screenshots instead of titles & thumbnails, and drag & drop to move articles around. And there's an integration with Hacker News, where every top-level comment with an article quote in it automatically gets converted into an annotation.

Visit productView on Hacker NewsNov 4, 2022
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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% 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: new · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released
54%54% 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: plus · Missing: platform, intuitive, reviews
42%42% 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
35%35% 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
21%21% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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