Ke

Keep Me On Top – Keep up to date with what you care about

Hacker News

Keep Me On Top – Keep up to date with what you care about

Hi HN! Keep Me On Top generates a RSS/Atom feed of your favorite content (Reddit, Hacker News, Lobsters, etc.). You can also receive a daily/weekly email with a summary of new content. This is my first product, and I still have a lot to learn! Any kind of feedback is welcome :) Site: https://www.keepmeon.top P.S. my stack is: Django, PostgreSQL, Celery and Redis.

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Actual performance

1points
1comments
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, email · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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