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IndieRadar – AI finds build-worthy problems on Reddit

Hacker News

IndieRadar – AI finds build-worthy problems on Reddit

Hey HN! I’m building IndieRadar, an AI-powered scout that trawls technical subreddits and turns raw posts into actionable “opportunity cards.” Each card includes: • Demand score (0-10) – based on intent phrases, failed attempts, and impact • Type (billing | workflow | missing-feature…) • Urgency & pay-signal (0-3) • A compliant follow-up draft so you don’t break subreddit rules Why? • I wasted hours combing r/IndieHackers & r/webdev, only to realise 90 % is noise. • Generic “trending keywords” tools ignore real purchase intent. • Cold-DM is spammy; I want data-backed pull signals instead of push outreach. How it works 1. Pick subreddits / topics 2. Keyword & rule-based filter 3. LLM labels high-potential posts (we only run AI on likely hits to keep costs low) 4. Alerts in real-time or a 3-minute daily digest (Email/Slack/Webhook) Ask • Would this save you research time? • Would you use? • Do you have any good suggestions? • Which community should we support next (HN, PH, GitHub Issues are on the roadmap)? Try the private beta: drop your email at indieradar.dev (7-day trial, no CC). Promise: no auto-posting, no data-selling. Thanks! Happy to answer anything about scoring heuristics, Reddit API woes, or launching without spamming.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: slack, email · Missing: mac, agents, macos
94%94% 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.
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.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% 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
21%21% 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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