Track Awesome List – Get Awesome List Updates Daily

Hacker News

I find myself often wanting to know the recent updates of some awesome list such as awesome-selfhosted [0], so I developed this tool to track the updates of more than 600 awesome lists and provide RSS feeds. The code is also open source [1], I hope this small tool can help you. And I also provide a client-side search [2], so you can search all awesome projects there, I implemented it through wasm supported by morsels[3]. [0]: https://www.trackawesomelist.com/awesome-selfhosted/awesome-... [1]: https://github.com/trackawesomelist/trackawesomelist-source [2]: https://www.trackawesomelist.com/search/ [3]: https://github.com/ang-zeyu/morsels

Visit product View on Hacker NewsNov 4, 2022

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: open source, ide · 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: host · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

13%13% 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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