Left Unread – A Chrome extension to never leave someone unread again

Hacker News

98% of the landing page was written by ChatGPT. 75% of the Chrome extension was written by ChatGPT. But... 100% of it was built from a problem I personally faced as unread conversations were getting far below in my inbox. What do you guys think of this Chrome extention I made (with the help of ChatGPT, obviously): http://leftunread.top/ The use case for the extension is the following: On Twitter (twitter.com/messages) and on Messenger (messenger.com/t/...), hide all read conversations to bring unread conversations to the top of the inbox. Does it convey well the needs and benefits, and make you want to install it? Much appreciated! PS: Feel free to roast it as much as you'd like, I won't be offended... But ChatGPT might be LOL.

Visit product View on Hacker NewsMar 14, 2023

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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: inbox, chatgpt · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

28%28% 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 · 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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