Local Email Client for AI Horseless Carriages

Hacker News

The AI Horseless Carriages article spurred a lot of conversation about how we should just be giving users the system prompt box [0], and we were pretty surprised that a bunch of email clients didn’t pop up following this pattern [1]. So we went ahead and created a local [2] email client that you can run that processes your inbox with your own handwritten rules. It lets you label and archive based on natural language rules. You can draft responses with your own drafting prompt, and there’s a “research sender” option that uses web search to get public info on a sender. You can customize any of the prompts to fit your needs. We’d love to hear what you think and PRs/issues are welcome! [0] https://news.ycombinator.com/item?id=43773813 [1] Superhuman seems to be pulling on this thread [2] uses OpenAI for this version, client runs locally, ollama support soon!

Visit product View on Hacker NewsJul 24, 2025

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

83%83% 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: user, new, inbox · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: soon, users · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, 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 · Strong signals: users · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

Incorrect prediction on native model

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