I made a tool that helps curate links your friends share

Hacker News

Hey HN! I used to spend hours consuming content, and realized that the content didn't end up making me feel happier or more productive. AI generated bloat content is increasing, and I've realized that the quality density of the content that my friends send me is way higher. This is why I built Curatee - a way to follow the content that people you know share. The (hyperbolic) mission can be found at https://www.curatee.co/mission , and an example page at https://www.curatee.co/u/het . I am currently working on a chrome extension, auto summaries, and an iOS app for the same. Also trying to solve the cold start problem by creating accounts that fetch and post content themselves (think for example the DeepMind blog). I'd love to hear your feedback, suggestions, and criticism! I know the problem exists, I'm just not sure about the solution I built.

Visit product View on Hacker NewsDec 30, 2024

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Launch Intel predictions

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

84%84% 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 · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

Hacker NewsMay not resonate with HN audience · Strong signals: exist, 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.

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

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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