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I built a mental health app to fix mine

Hacker News

I built a mental health app to fix mine

Hi HN, I’m still in college and new to the startup world, but here is my first shot at a side project: Milburn, an integrated therapy+journal tool( https://milburn.vercel.app/ ). I built Milburn bc every mental health tool I could find was missing something: the therapy apps didn’t have voice/memory/too expensive, and the journal apps were always a PITA to sync with therapy. We trained the chatbot’s model on specific therapy modalities(I personally need CBT sessions, but we also have others like DBT), and then created a prompted journal, added a feedback loop, voice capabilities, and a triage questionnaire. We’re clearly still at a super early stage and new to this lol, so we would appreciate any feedback on the tool/idea: What resonates? What’s missing? Is this different enough to the myriad other mental health apps on the market? I’d especially appreciate insights from those who’ve worked in mental health tech or have experience with similar tools. We’re also looking for beta users so drop me an email at [rkrithik@seas.upenn.edu](mailto:rkrithik@seas.upenn.edu) if you’d like some free access!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos
85%85% 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: created · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: 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, apps, users · Missing: mobile apps, ios, entrepreneurs
45%45% 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
27%27% 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 · Missing: arr, mrr, revenue
27%27% 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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