Research discipline
The aim is not to make an interesting result sound stronger. It is to make the result easier to inspect. Each project therefore tries to preserve the machine state, configuration, measurements, faults, corrective changes and claim boundary that produced the result.
Publication rule: an interesting result is not made stronger by removing its limitations. The limitation is part of the result.
Typical evidence path
Question
Define a falsifiable engineering question and an external success boundary.
Build
Construct the smallest route that can answer it without redefining success.
Measure
Capture results, failures, hashes, environment and relevant telemetry.
Freeze
Separate demonstrated claims from limitations and future work, then preserve the evidence.
Current research areas
- Local and on-premise large-model execution on constrained consumer hardware.
- User-controlled AI agents, Model Context Protocol infrastructure and safe local tool authority.
- Engineering automation, autonomous research tooling and evidence-driven optimisation.
- Manufacturing, quality systems and practical engineering workflows.