A new metaphor for email – the dustpan

Hacker News

Wouldn't it be nice if emails "expired" after some predefined time period? Most emails would probably work this way, after a sale expired or a password reset link was clicked the emails should "self-destruct" to create more room in my inbox. The value of keeping an email in your inbox decays exponentially. This is idea behind Mail Sweeper. It installs a Dustpan to your inbox. The Dustpan collects unimportant emails based on your criteria and automatically moves them to trash after 30 days (or more if you want). It's designed to help save money by freeing up Gmail storage space (so you don't pay for extra storage) and prevent you from wasting time manually cleaning up your inbox. The product is not a subscription, just a small one time payment and easy 2 minute setup.

Visit product View on Hacker NewsMar 27, 2024

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, inbox, email · Missing: mac, agents, macos

84%84% 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

81%81% 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: ide, io · Missing: https docs, excited, just released

42%42% 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

37%37% 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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% 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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