A local-first control plane that brings multi-agent orchestration directly into your development environment — with humans firmly in the loop.

• Coordinate specialized coding agents (Claude Code, Antigravity, …) on complex engineering tasks through adapters and an internal event bus
• Local-first by design: code and data stay on your machine; external resources connect through a dedicated cloud relay
• Human approvals gate autonomous actions — nothing executes without your say-so
• Real-time dashboard to monitor agents, read logs, and intervene at any moment

TypeScript · React · Fastify · pnpm + Turborepo monorepo · Docker · GitHub Actions

Not "a chatbot with shell access" — a small orchestrator that understands intent, routes work to the cheapest competent executor, and reports back, while every side effect passes through a policy layer that can refuse.

you ─▶ ORACLE ─▶ intent ─▶ route ─┬─▶ local tools        (fast, free, guarded)
                                  ├─▶ project knowledge  (hybrid RAG)
                                  └─▶ delegated agents   (deep code reasoning)

• A deliberately small local router model (0.8B via Ollama) — 93% intent accuracy on the eval set; heavy reasoning is delegated to agents that are good at it
• Three processes, three trust levels: the model emits tool requests, a policy engine decides, an isolated low-privilege tool host executes
• Hybrid RAG over code and docs: syntax-aware chunking with tree-sitter (18 grammars), ONNX embeddings on CPU, sqlite-vec search, watch-based incremental indexing, PDF extraction
• Event-sourced runtime — any session can be replayed, audited, and tested deterministically
• 370 Python + 77 TypeScript tests, mypy --strict, and a security suite that acts as a merge gate
• Built against real constraints: designed to run next to a 4 GB VRAM GPU

Python 3.12 · FastAPI · Tauri · SQLite · Ollama · ONNX Runtime · tree-sitter

A zero-runtime-dependency Node.js pipeline runner that coordinates three agents — orchestrator → coder → tester — using repository files as the communication protocol.

• A state machine with human gates: phase completions, failures, and "done" claims without an actual commit all stop the loop for review
• Distributed mode: the PC orchestrates while a laptop executes over LAN — heartbeats, at-least-once delivery, idempotent dispatch, git as the sync layer
• Live dashboard streaming structured agent output (tool calls, turns, cost) in real time
• Safety first: never force-pushes, never runs destructive git commands, never executes anything found in repository markdown

Node.js · zero dependencies · state machine · file-based protocol

I keep a structured AI/ML curriculum (an Obsidian vault + Jupyter notebooks) and move through it stage by stage — while the projects above let me build ahead of it.

• Right now: foundations done properly — linear algebra, NumPy, statistics — worked through in notebooks, not skimmed
• Daily practice through building: transformers & LLMs — attention, tokenization, embeddings, RAG & vector search, AI agents, prompt engineering, LLM evaluation, fine-tuning & RLHF
• Next up: PyTorch, from-scratch checkpoints (linear & logistic regression → a neural network → self-attention → a small GPT → a RAG app), then MLOps

Languages & runtimes

Building with

AI / ML

Design-first — architecture docs and ADRs lead the code · humans stay in the loop · untrusted content is data, never instructions · tests and security suites are merge gates, not suggestions