Autonomous multi-agent platform
601 commits56,500 linesPython, Rust, TypeScript, Bashbuilt solo in five months
Orchestrates AI runner CLIs against markdown task specs, with success-criteria review loops, retries, git-worktree isolation and an orchestrator-managed pull-request flow for code tasks. Native desktop app, CI with lint and tests. Production record: 333 completed autonomous tasks across about twenty projects, around 800 generated reports, and 122 consecutive automated daily briefs. Since generalised into the team-wide knowledge base, running on our own GPUs.
Embeddable graph database in Rust
231 commits31,000 linesCypherC FFI and Python APIs
Library-first, broad Cypher support, graph algorithms, and bindings for C and Python. Started as a personal project, graduated into a team project, and is now the embedded graph store inside a knowledge-graph engine for game-narrative consistency that the lab has in development.
On-device language-model platform
originated itfine-tuning MVP in ten monthspatent pending
Shipped the automated fine-tuning MVP and built the evaluation stack: BLEU, ROUGE and METEOR, LLM-as-judge scoring, JSON-validity checks and prompt-version tracking. A 500 MB CPU-only model beat a frontier API model on latency at matched quality, which became the joint demo case in a technology partnership with a global semiconductor leader. The core method is patent pending.
Android app for on-device model evaluation
llama.cppfull benchmark suitei8mm kernel paths
Runs and benchmarks the small language models we train, with a full UI, in-app model download and on-device execution. Roughly doubled the upstream open-source codebase it started from: llama.cpp integration, templated chat and a complete on-device benchmark suite, later extended with CPU kernel optimisations.