I built an AI system where content compounds like interest

Hacker News

I've been building AIGM (AI Generated Media) – a self-evolving media architecture where AI doesn't just create content, it learns from how that content performs and gets smarter over time. The core loop: 1. AIKnowledgeCMS collects & structures knowledge into keyword nodes 2. AIMediaPost turns those nodes into articles, publishes to Blogger, X, etc. 3. Search traffic flows back into AIKnowledgeCMS 4. The system expands its own knowledge base automatically Every post makes the next one better. Live demos: - AIKnowledgeCMS: https://aiknowledgecms.exbridge.jp/ - AIMediaPost: https://aimediapost.exbridge.jp/ - AIRadio: https://airadio.exbridge.jp/ Happy to answer any technical questions.

Visit product View on Hacker NewsFeb 28, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

90%90% 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

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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