AI agents that validate your product idea by talking to real users

Hacker News

I built a tool to solve a problem I kept running into: I was making product decisions based on guessing instead of real users. I kept building stuff nobody wanted as I was usually wrong. So, I built HolyShift: AI agents that validate product ideas by talking to real people on Reddit, HN, X, and LinkedIn … then generate a detailed GTM and “Should we build this?” report. No synthetic data (ChatGPT). No predictions. Only real conversations from real people. What it does • Posts platform-native questions (where allowed) • Collects real reactions, objections, pricing signals • Clusters feedback into themes (pain, demand, adoption, pricing …) • Runs a monitoring agent for sentiment analysis • Produces a short validation report (PRD + GTM) All actions are rate limited and reviewed by a human for compliance. How it works (technicals) • Multi-agent pipeline (intake → landscape → engagement → monitoring → synthesis → report) • Platform specific prompting (HN vs Reddit vs LinkedIn …) • Real-time sentiment + clustering via embeddings Link https://www.holyshift.ai (Early beta) What I’m looking for • What should stay human vs automated? Should we automate this 100%? • How do you do your product validation? Do you talk to your potential users (and who?) before you build? Happy to answer anything.

Visit product View on Hacker NewsNov 30, 2025

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

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best fitHighest predicted score across all platforms for this description.

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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, pipe, io · Missing: https docs, excited, just released

30%30% 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

29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, real people · 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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