Most of my work sits at the point where a language model stops being a chat window and starts being a process: an isolated sandbox, a real tool surface, retrieval over an actual repository, and a pull request at the end of it. Lately I've been taking the same interest upstream, into the libraries that make that possible.

Plain text that machines can read — without guessing.

Markdown makes an agent infer structure from prose. XTXT lets the document carry it: images, tables, charts, and typed records in a file that still opens in any editor and diffs cleanly in git. A written spec, a reference parser, and seven implementations.

Give it a repo URL and a task. It opens the pull request.

Clones into an isolated Docker sandbox, chunks the codebase with Tree-sitter, retrieves the relevant functions by embedding similarity, edits, runs the tests, and pushes a real PR. Every capability the agent has is exposed through an MCP server — so the tool surface is inspectable, not buried in a prompt.

A debugging pipeline that remembers.

Five specialized agents in sequence: log parsing, root-cause analysis, fix generation, validation against the test suite, then the PR. Past incidents land in a FAISS vector store, so the system gets sharper on failure modes it has already seen. Works across OpenAI, Claude, and Gemini.

Distributed training where the data never leaves the client.

Clients train locally and publish only model updates; the server aggregates them into a shared model. Kubernetes handles orchestration and scaling across the fleet.

Contributing back to the infrastructure I build on.

ResponseCachingMiddleware cached every result it saw, including ones a tool returned with is_error=True. A transient 503 or a rate-limit got pinned for the full hour-long TTL, so every later call replayed the stale failure and the tool never ran again. Added the guard alongside the passthroughs that already encode the same idea — some results describe a moment, not an answer — plus tests covering the retry path.

Two API implementations annotated a return type as the string "IndexingStatus" without importing the name. Because the annotation was a string, the missing import stayed invisible at import time and only surfaced once something resolved type hints. Traced it to both call sites, fixed, and added a regression test that needs no cluster to run.

Two more up at fastmcp: #4706 adds RFC 6570 explode support for list-typed query parameters, and #4758 serializes a read-modify-write race in the event store's stream list. Also reading the tracker at pydantic.

Also building SecurePass, a password vault that encrypts client-side so the server never sees a credential, and BankStatement_Extraction — template-free statement parsing with OCR fallback, balance-reconciliation checks, and a per-transaction confidence score.