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Lessie AI – Your People Search AI Agent

Hacker News

Lessie AI – Your People Search AI Agent

Hi HN! I’m Yanjun, co-founder of Lessie AI. We built Lessie AI as a People Search AI Agent — designed to help you find anyone in seconds: - Influencers for campaigns - Partners for collaborations - Experts for insights - Founders or investors to connect with Instead of spending hours doing LinkedIn / Google searches, Lessie automates the whole workflow: - Understand your request — type what you need, any role or profile - Search across multiple data sources — trying different strategies to find the right people - AI review + scoring — profiles are automatically filtered and ranked so you see the most relevant first - Generate outreach in seconds — on-brand, personalized, ready to send We’re currently in early access — looking for feedback from the HN community. Would love to hear: - What your biggest pain points are when trying to find people - What kinds of people you most wish you could find more easily - What other data sources you’d want integrated - How you’d use an AI agent like this in your workflow - Any feature ideas (or concerns!)

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

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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: agent, google · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, google · Missing: mobile apps, ios, entrepreneurs
28%28% 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
19%19% 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
17%17% 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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