An AI social networking app that treats hallucinations as a feature

Hacker News

I’ve been seeing a lot of ideas around LLM agents acting on your behalf in networking or dating apps. I wondered: what if this were much simpler—almost NGL-simple? In this app, you create a private profile that only your own agent can read. Other people never see it directly. Instead, you find a friend’s name card, let your agent chat with their agent, and have the agents evaluate your compatibility. Because profiles are only visible to each user’s agent, hallucination ends up playing an interesting role. It becomes a way to express things you might want to share, but feel too awkward to put on a public profile. The agent conveys that information indirectly and ambiguously. I built this mostly as an experiment. If you try it, I’m curious to see what kinds of funny or unexpected results come out of it.

Visit product View on Hacker NewsDec 31, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, apps · Missing: mac, macos, cursor

89%89% 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.

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

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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