Chat with AI copies of all HN users, like sama, dang, and even yourself

Hacker News

Hey HN, I'm Chris. I've been working on creating personalized AI chatbots by grounding them in real conversations. Feeling inspired by antirez's recent post ("Reproducing Hacker News writing style fingerprinting", https://news.ycombinator.com/item?id=43705632 ), I wondered: could I use the HN comment data referenced in that post to create virtual versions of... every user on Hacker News? A ~week later, here we are: https://lexikon.ai/dataset/hn The database contains every user who has commented at least 200 words, and when you search for a user, we'll process their history in realtime to create a personalized AI chatbot of them on-the-fly (it'll take a few minutes). There's no login required, and conversations aren't recorded or stored anywhere, nor are they trained on. It surprised me at how fun it actually is. Give it a shot, would love to know what y'all think--

Visit product View on Hacker NewsApr 24, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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best fitHighest predicted score across all platforms for this description.

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

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

66%66% 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 · Strong signals: users · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

12%12% 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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