What I’m Building
I’m developing a fully offline, memory-retaining autonomous AI engineer. It’s designed to take user intent, retain task history, generate/refactor code, and evolve independently — no API calls, no cloud dependencies.
This isn’t a co-pilot — it’s an engineer that thinks back.
What’s Built So Far
- Local LLM inference (Mistral-based, fast + cheap)
- Full command interface
- Memory layer (session + indexed context)
- Output interpreter
- Plugin scaffold (Phase 2 now live)
- Improvement loop UI (task queue, log summarization, retries)
Why This is Different
- Fully modular + explainable
- Memory is a real system, not context stuffing
- Architecture-first, not prompt-first
- Soon expanding into hybrid (local + cloud-enhanced modes)
Screenshot
Full article with diagrams:
Feedback I’m Looking For:
- Offline vector memory strategies
- Best practice for task evaluators + retry loops
- Anyone doing similar agentic orchestration locally?
Tags:
offline-llm, memory-layer, agent-architecture, open-source-llm, mistral, dev-tools