Em

Email Client with Backups

Hacker News

Email Client with Backups

Hello HN, I made an email client for macOS/Linux focused on one thing: owning your emails locally. It backups .emls in maildir locally, as well as highlights mismatches in reply to address to displayed. Alternative view's for email like explorer - date/sender based. Email insights - top senders, frequency graphs. Local usage stats - so you could see how gmail is capping the 2500 Mb a day. Lots of layouts to customize to your liking. Live online demo - no need to download. Highly recommend having a look and playing around a bit. Links: - Website: https://mailvaultapp.com - Live demo: https://mailvaultapp.com/demo/?lang=en - GitHub: https://github.com/GraphicMeat/mail-vault-app - Discussions: https://github.com/GraphicMeat/mail-vault-app/discussions/1 Happy to answer any questions about the Tauri/Rust, IMAP architecture or anything else.

Visit product View on Hacker NewsSep 12, 2026

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, 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

48%48% 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 · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, macos, email · Missing: agents, agent, cursor

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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.

Correct prediction on native model

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