I built a app to find your first users by listening to Reddit community

Hacker News

Hey Hacker News, We all know Reddit marketing can be a minefield. There's a fine line between being helpful and being a spammer. We've seen founders find their first users by genuinely engaging, but finding the right moment to add value manually is nearly impossible to scale. I built Reddit Genius AI to fix this. It’s a "listening tool" designed to help you do outreach the right way. It monitors relevant subreddits for keywords—pain points, competitor mentions, requests for help—and flags conversations where you can make a real contribution. The AI assistant then helps you draft a thoughtful comment, turning a cold outreach into a warm, helpful interaction. The goal is to automate the tedious 90% of searching so you can focus your energy on the critical 10%: actually helping people. I’m trying to build tools that keep us on the right side of the spam line. Is this a fool's errand, or is there a real need for this? I'd love your perspective.

Visit product View on Hacker NewsAug 19, 2025

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

92%92% 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: user, new · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, 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

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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