Text-me: a personal agent that learns your pace

Hacker News

Hey, I built text-me because every productivity app I tried gave me one more place to be and maintain. Meanwhile my life actually happens over email, messages, calendar, college, Kindle, reading, and Substack. And I always have to be somewhere else. So now I send everything to one place, like texting a friend. It separates life from work, and every week it analyzes and gives you a place to start instead of another dashboard. You can connect your email and calendar through Plow. It always asks before changing anything in your calendar or sending a message. And the part that interests me most is that I don't have to keep re-sorting twenty things anymore, because it remembers. If I say some activity takes me 45 minutes on average, but I actually take 60, it learns that. So next time, if I write 45 minutes it puts 60, and it keeps adapting my calendar to that. And every morning it can send a newspaper with everything that matters to me: the people I need to answer, what changed overnight, what I have to solve today. It can even send something funny, and that goes to my Kindle. Of course it can go to the printer, email, or the chat, but I send it to my Kindle. It's open source and works over iMessage or SMS. I built it during a hackathon, and it came out of my own problems. Now I want something that learns the real pace of life. I'd love your feedback on what you think should improve.

Visit product View on Hacker NewsSep 19, 2026

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

92%92% 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: para · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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