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Magic – automatically keep your inbox clean

Hacker News

Magic – automatically keep your inbox clean

Magic automatically sorts away unsolicited sales emails and newsletters into a separate folder in your Gmail inbox. We're starting with two simple categories, but want to branch out to helping people better organize their inbox. Question for the community: What frustrates you most about your inbox? What do you often miss?

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: new, inbox, email · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting
39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
38%38% 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: way, para · Missing: mobile apps, ios, personal
29%29% 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
21%21% 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
12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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