Unclutter – Reader mode, but better

Hacker News

Hey everyone! In the last months I've been working on Unclutter, a modern reader mode browser extension. In contrast to all existing approaches, it unclutters articles by modifying their CSS instead of extracting the text content. This results in a more visually pleasing result that reuses the original article style. The idea is to remove friction so you use the reader mode more often. There are a few more features around saving articles automatically and taking highlights -- more details are on the website. The extension has about 400 active weekly users right now, mostly from organic web store traffic. Monetisation has proven to be hard and for freemium there would need to be much higher numbers anyways. Do you think I should keep working on the project?

Visit product View on Hacker NewsNov 23, 2022

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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.

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

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% 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

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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