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Create your own custom directories easily (e.g. AI tools, courses list)

Hacker News

Create your own custom directories easily (e.g. AI tools, courses list)

Hi Hacker News community, Keeping track of all the software links, example like AI tools, can be overwhelming. That’s why I developed a tool to help you manage them effortlessly. With this tool, you can easily create directories to organize and save your favorite software links. It’s simple, efficient, and you can even share your curated collections with others. Interested? Try it out and see how it can simplify your workflow: https://chromewebstore.google.com/detail/cokeep-extension/gp... Thank you!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released
25%25% 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 HuntUnlikely to reach the leaderboard · Strong signals: google, new · Missing: mac, agents, macos
23%23% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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