Dara is an AI Parenting Coach: All parenting questions answered

Hacker News

Hi HN, I'm Beyza! I'm a psychologist and my sister is an AI researcher. Together we are building LLMs fine-tuned for coaching parents. Parenting is a demanding journey, where parents need actionable guidance to overcome everyday challenges like tantrums, sleep, or eating problems. Parents might think their only options are pricey therapy sessions or crossing their fingers with generic advice that might not hit the mark. It's tough finding something that's both helpful and affordable. We aim to provide real-time support for any questions they have and help prevent that all-too-common parental burnout with on-demand conversational therapy. Unlike generic LLMs, our models are specialized in psychological conversations with parents. Our models have long-term memory for personalization, and they are reduced to run-at-the-edge devices. I'd love to hear your feedback. Feel free to ask any questions or share your suggestions! Thanks for checking it out, Beyza

Visit product View on Hacker NewsMay 20, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos

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

46%46% 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

37%37% predicted probability of success on AppSumo, 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

29%29% 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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