TabMail. Open-source AI email for Thunderbird and iPhone

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Hi all, I'm the sole developer of TabMail. I wanted my email client to have AI features, and ended up making a Thunderbird add-on and an iPhone app. I've now open-sourced both under MPL 2.0, and I wanted to share them here to get some community feedback, if possible. I made a short video of both, since it's easier to show what they do than explain everything in a post. Hopefully you like it: https://tabmail.ai/demos/tabmail-promo.mp4 You can review a prepared reply, tell it what to change, or ask questions about your inbox and calendar. On TB, I wanted to do most of this with the keyboard. On the phone, I wanted to just talk to it :) Code: Thunderbird: https://github.com/TabMail/tabmail-thunderbird iOS: https://github.com/TabMail/tabmail-ios Note: I have the AI backend closed for now, since it does need me to fish compute from LLM providers, etc. Still, I made the email/calendar processing ZDR (zero data retention) since, as a solo dev, I didn't want to go through the headache of storing anyone's email or calendar data. The content can be sent for AI processing, but isn't kept on TabMail's servers. Search stays on-device. Anyhow, please try it out! Just trying to share how I wanted email to be with AI features :) Happy to answer questions. Downloads: https://tabmail.ai PS. The Thunderbird add-on uses the Experiments API for things that aren't supported in MV3, so I'm hosting it on my website for now.

Visit product View on Hacker NewsSep 16, 2026

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

94%94% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs

51%51% predicted probability of success on TrustMRR, 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

45%45% 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 · Strong signals: host · Missing: plus, platform, intuitive

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

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

17%17% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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