Hey r/openclaw,

With the launch of Google's Gemma 4 (now fully open under Apache 2.0) and the OpenClaw 4.1 update dropping yesterday, the local agent game just completely changed. I’ve been stress-testing the new models, and I wanted to share my optimized stack that essentially gives you highly capable, zero-token-cost sub-agents running on consumer hardware.

If you want to maximize performance while keeping costs near zero, here is the architecture and setup guide you need.

🧠 1. The "Main Br

Reddit - The heart of the internet. Skip to main content. Go to OpenClawUseCases . r/OpenClawUseCases. •. goldgravenstein. Fresh install on M4, what’s your best local model use case? . M4 Mac Mini, 16GB, 4tb SSD. Ready to roll… What’s your best use c

mini cli search engine for your docs, knowledge bases, meeting notes, whatever. Tracking current sota approaches while being all local

An on-device search engine for everything you need to remember. Index your markdown notes, meeting transcripts, documentation, and knowledge bases. Search with keywords or natural language. Ideal for your agentic flows.

QMD combines BM25 full-text search, vector semantic search, and LLM re-ranking—all running locally via node-llama-cpp with GGUF models.

a way to shift a substantial portion of LLM workloads to consumer devices by having small on-device models (such as Llama 3.2 with Ollama) collaborate with larger models in the cloud (such as GPT-4o).

Installation de typesense sur mac os M1