Reaching inbox zero has never been more fun

Hacker News

The average person receives around 120+ emails per day. This makes it almost impossible to stay on top of your emails. This is where InboxSwipe comes in. InboxSwipe is the easiest and most fun way to clean up your Gmail inbox. We present all your emails in a tinder style card view where you can swipe away to take several actions on your email (deleting, unsubscribing, marking as read, etc.) You can choose from 10+ actions and assign it to any swipe gesture you want. We also have AI powered reply feature so that you can respond and discard emails with just a click of a button. Try it out and let me know what you think.

Visit product View on Hacker NewsMar 21, 2025

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: 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.

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

70%70% 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

49%49% 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 · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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

18%18% 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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