AI Compass:Daily AI Search Signals and Trends

Hacker News

Hi Hacker Friends, I’m sharing AI Compass, a daily AI signal brief built for builders who want facts over hype. AI moves fast. We track real-time signals across Google Trends and web news, then use AI clustering, denoising, and source attribution to surface what actually matters: model launches, company moves, and emerging terms. You get a structured daily brief with traceable sources and clear takeaways, so you can understand the landscape in minutes. Our priorities： 1. High signal-to-noise: only accurate, relevant items. No hype. 2. Objective and transparent: conclusions backed by traceable evidence. 3. Fast to consume: built for developers, PMs, and indie builders. This is a new launch. Feedback, bug reports, and feature ideas are welcome.

Visit product View on Hacker NewsJan 26, 2026

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

84%84% 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: model, google, new · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% 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

22%22% 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: ide, io · Missing: https docs, excited, just released

18%18% 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 · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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