Ag

Agent Brain Trust, customisable expert panels for AI agents

Hacker News

Agent Brain Trust, customisable expert panels for AI agents

Agent Brain Trust lets you summon a panel of real, named experts to critique your architecture, review your writing, pressure your product strategy, or debate your design patterns. 10 built-in trusts, an extensible roster, and a working turn-taking protocol that ensures nothing useful gets skipped. Guest experts are drafted via an MCP server that maps topics to real persona cards so the panel can reach into niche and novel territory without inventing expertise it does not have. Wrote up the full thinking here: https://tinyurl.com/agent-brain-trust

Visit product View on Hacker NewsApr 21, 2026

Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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

78%78% 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.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

34%34% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

14%14% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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