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Projects

The systems behind the delivery.

Two projects I designed and built end to end, then the rest of the work underneath them.

Hardened distribution of opencode, the open-source TypeScript coding agent

  • Built the model-routing layer upstream lacks. Opencode resolves a model you already named; mine decides per request on cost, latency and capability, so tuning a deployment is configuration rather than a code change.
  • Added vLLM as a first-class provider and the retrieval that makes it worth using: the agent runs on self-hosted open-weight models inside the customer's network, grounded in RAG over their own corpus.
  • Rebuilt the MCP tool-calling path so the agent's tool surface comes from the servers attached to a deployment rather than from what shipped in the binary.
  • Built the evaluation harness that decides whether a change improved the agent, with objective per-case verification, no model grading its own work.
  • Turns captured agent transcripts into training data with supervised fine-tuning and reinforcement-learning paths on top.

Built with

  • TypeScript
  • Bun
  • vLLM
  • MCP
  • RAG

14-service AI career platform, Python and TypeScript

  • Designed the platform architecture as a reusable substrate, then specialized it into a vertical product without a rewrite.
  • Agentic workflows behind a four-gate pipeline: permission check on every tool call, verified completion against the tool ledger, deterministic grounding against the corpus, fail-closed guardian.
  • Multi-agent task division: splits a job across agents working in parallel rather than driving one agent through it serially.
  • Full-stack: front-end React and TypeScript, FastAPI and Python behind it, PostgreSQL and pgvector underneath serving RAG, every service containerized with Docker.
  • Cloud architecture on AWS: a provider-agnostic model proxy fronting Bedrock, and a GitHub MCP server held read-only with the token outside the agent.
  • 750+ test functions, constant-time key comparison on every service boundary, append-only audit schema.

Built with

  • Python
  • TypeScript
  • React
  • FastAPI
  • PostgreSQL
  • pgvector
  • AWS
  • Docker

Other work

What the two above are built on.

  • openagent-code

    Public, Apache-2.0

    A self-hosted autonomous coding agent with a plan, act, verify loop, subagents, cross-session memory, and a permission engine with fail-closed approval review.

    View the repository
  • OpenAgent

    Public, Apache-2.0

    A multi-service agent platform: identity gateway, LLM-powered APIs, RAG memory on pgvector, and HMAC-signed append-only capture enforced by database role privileges.

    View the repository
  • BoeNet

    Private, available on request

    From-scratch conditional-compute research in PyTorch, implementing Mixture-of-Depths token routing with top-1 Mixture-of-Experts and Switch load balancing.

  • DatasetForge

    Public, Apache-2.0

    A validation-first pipeline that streams and samples Hugging Face corpora into resumable, checksummed pretraining mixes.

    View the repository

If you’re putting an LLM system in front of real users, I’d like to hear about it.

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