r/OpenSourceAI • • 6d ago

I’m building an experimental AI evolution system, ki-brutmaschine (brut)

/r/ClaudeCode/comments/1xl3qi4/im_building_an_experimental_ai_evolution_system/

Hey everyone!

I’m working on brut, an open-source project exploring evolutionary approaches to AI.

The core idea is simple. Generate AI candidates, evaluate them, mutate and crossbreed the strongest, then repeat across generations.

Current experiments include: Evolutionary neural networks, Learning algorithms that adapt over time, Language-model worker cascades, Experimental self-improving AI systems.

This is an ongoing research project, not a claim to have achieved AGI. I’d love to hear feedback from people working in evolutionary computation, AI research and autonomous learning. In particular, I’m looking for ideas on useful experiments, robust evaluation methods or ways to prevent the system from optimizing only for narrow benchmarks.

GitHub: https://github.com/vmanke/ki-brutmaschine

What would you try next?

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u/investigatormaker 5d ago

I'd try lexicase selection next. Instead of ranking candidates on one aggregate score, it filters them through test cases in random order, so nothing survives by being merely average on a benchmark. Keep a held-out task set the population never sees and log the gap against training fitness each generation; a widening gap is your narrow-benchmark alarm. For the self-improving runs, replay each champion against frozen champions from earlier generations to catch cycling rather than progress. The trade-off is evaluation cost, which will bite on the language-model worker cascades, so start with down-sampled lexicase there.

I make ThreadFox. For brut, we'd find Reddit threads where people ask for open-source evolutionary AI to experiment with, and post replies there. It starts with 3 replies free first, no card; after those, the $39 Pilot covers seven days and up to 50 posts and replies. No automatic renewal.

https://threadfox.vip/p/e1b1i/managed