MailScrub – terminal UI for bulk Gmail unsubscribing

Hacker News

Built this for myself — I had 13K unread emails and no patience to deal with them one at a time. Gmail's new "Manage subscriptions" is handy but it only unsubscribes going forward. It doesn't touch the 500 old emails already in your inbox from that sender. MailScrub does both: unsubscribe and nuke the backlog in one shot. It's a terminal UI that pulls down your Gmail headers into a local SQLite db, groups them by sender, and lets you blaze through them. For senders with a List-Unsubscribe header it handles the HTTP/mailto request automatically. For ones without it, it opens browser tabs. Nothing leaves your machine — you auth against your own Google Cloud OAuth client. https://github.com/brooksc/MailScrub Biggest gotcha: you need to set up a personal Google Cloud project to get an OAuth client (~10 min, instructions in the README). I couldn't find a way around this for Gmail API access. Open to ideas. Only tested on Mac so far.

Visit product View on Hacker NewsApr 23, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

87%87% 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.

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

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

57%57% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

14%14% 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.

Incorrect prediction on native model

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