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Connecting People Through AI-Powered Video Sentiment Matching

Hacker News

Connecting People Through AI-Powered Video Sentiment Matching

Hi HN, I’d like to share www.kuky.com, a peer support network that connects people through short, self-recorded videos and matches them using sentiment analysis powered by large language models (LLMs). We’re building Kuky to help users find others who genuinely understand their emotional journey—not through swiping or likes, but through shared human stories. In this short Loom demo (link above), I walk through how: Users create a profile by uploading 3 videos: an intro, their mental health journey, and their likes/dislikes LLMs analyze each video for emotional tone, key themes, and psychological markers Based on this, Kuky intelligently connects users with similar experiences and emotional alignment We're passionate about creating a safe, empathetic space for authentic conversation—especially for people dealing with mental health challenges. For more background, here’s a feature on Kuky in Women Love Tech: https://womenlovetech.com/kuky-the-mental-health-app-were-sw... Would love your thoughts on the concept, the matching algorithm, and how you’d imagine using something like this. Thanks, Armin

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
73%73% 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 · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, users · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
52%52% 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
34%34% 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
20%20% 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.

Incorrect prediction on native model

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