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StayWithMe – Digital Legacy

Hacker News

StayWithMe – Digital Legacy

Hi HN, Roman and Oleg here, and we want to share the StayWithMe project with you all! Our software allows people to pass their life experiences, lessons and stories through generations by answering questions by categories, after which it creates a digital memory of the person, which their grand kids or other family members can interact with to learn about their ancestry. Demo: Join the waitlist at kai-tech.com * we will reach out to you so you can try Tech Stack: Voice - ElevenLabs.io Facts Extraction - Kor python library LLM - GPT 4o Transcription - Deepgram What We’d Love Feedback On We’d love to hear your thoughts, feedback, or any questions you have! We’ll be around to respond to comments and discuss further. Thank you for taking the time to check it out, and we are looking forward to your insights!

Visit product View on Hacker NewsJan 20, 2025

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: elevenlabs · Missing: mac, agents, macos

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

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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.

Correct prediction on native model

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