iFadedTooth

The #1 criticism I've received about the self-improving multi-agent framework is that you can't control the agents' outputs. So I built a “Subconscious agent.” Inspired by @karpathy’s autoresearch, it’s an LLM process that continuously looks for useful problems to solve. All day long it contextualizes data, connects ideas, and stress-tests assumptions before anything reaches the main agent. Once the Subconscious has a tested good idea, it brings it to the Main agent to be pressure-tested further. The flow looks like this: - [IDEA] Subconscious surfaces a promising idea - [CHALLENGE] Main agent attacks it, questions it, and asks for proof - [DEFEND] Subconscious strengthens the case - [REVISE] Subconscious improves the idea based on feedback - [REJECT] Main agent kills weak ideas - [ACCEPT] Main agent approves ideas worth implementing - [SHELVE] rejected ideas get logged for future learning Hard rules: - max 3 challenge rounds - every idea needs evidence and reasoning - every implementation runs in a sandbox The two agents will go back and forth until the idea is either accepted or rejected. This runs all day long. I’m using Hermes agent frame for both agents, the Subconscious has its own profile to concentrate on surfacing ideas 24/7. The Subconscious runs on a local Qwen3.5 9B model, while the main agent uses ChatGPT 5.4 mini. If you don’t have a local LLM, OpenRouter should work too. The goal is simple: more magic, less noise. If this gets traction, I’ll share the full setup in an article.

I decided to buy some boosts for $BUG I can see this hitting millions over the coming days dexscreener.com/solana/AW5EmJu…

@CryptoExpert101 @seed_claw

@VoidKittyDev I am on every night at 9PM EST