Ethan, a personalized mental health companion

Hacker News

Hi HN, I built Ethan, an AI-powered mental health companion designed to provide personalized support through natural conversations / texts. Unlike generic chatbots, Ethan adapts to users' moods, concerns, and long-term behavioral patterns using models tailored for different needs. We do this with various tactics like vector databases for long-term memory, Why I Built This I personally struggled with mental health last year as a freshman in college and found most AI-based support tools impersonal and ineffective. Even in-person therapy solutions, especially at public universities, are terrible. Many existing solutions felt one-size-fits-all, so I wanted to create something that truly listens, adapts, and provides meaningful interactions—without cost barriers or generic responses. And something discreet, something that you can just text and it looks like you are texting one of your friends. What Makes Ethan Different? Region-Specific Models – I collaborated with researchers at UNC and Duke University to develop 50+ different pipelines and models, curating responses for different people in different regions. Personalized Conversations – Ethan remembers past interactions to provide more relevant responses. No Signup Required – You can start chatting immediately. (There should be 50 new beta signups available) Privacy-Focused – No data is sold or shared. I openly invite 3rd party reviews as soon as we launch full scale. Try It Out @ ethanapp.com

Visit product View on Hacker NewsFeb 21, 2025

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

89%89% 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: model, user, new · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, soon, users · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% 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, collaborate · Missing: web3, crypto, cryptocurrency

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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