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An autopoietic agent forge for emergent, consensus-based AI ecosystems

Hacker News

An autopoietic agent forge for emergent, consensus-based AI ecosystems

After developing the Protogen engine, I realized that intelligence shouldn't just be individual—it should be collective. I’ve built the Agent Forge V5.0, an orchestrator that births a swarm of autonomous engines (Theoretical Physics, Ethics, Marine Biology, etc.) and lets them 'swarm' the same data simultaneously.Key Features:•Collaborative Swarm Ingestion: Multiple agents interpret the same document through different axiomatic lenses at once.•Emergent Global Consensus: A 'Global Logic Map' that only accepts patterns validated by multiple independent agents.•Hardware-Aware: Automatically scales the swarm based on your CPU/RAM and AMD GPU (via DirectML).•Zero LLM Dependency: This is pure, self-derived logic and autopoiesis.I’m interested in the community's thoughts on Sympoiesis (making-together) and how consensus-based logic maps could provide a more stable foundation for AGI than current black-box models.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
87%87% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, 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
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% 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
26%26% 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.

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