A pytest decorator and verification protocol that mechanically proves a test catches the specific bug it claims to cover. Uses git worktrees to run today's test against yesterday's code.
AI agents produce code at 10–100× human velocity. Traditional quality processes — code review, manual testing, gradual refactoring — don't scale. Here's a toolkit of mechanical verification methods that do.
TDD tells you how to add features safely. DDD tells you how to remove them safely. Write passing tests for dead code, mark them xfail, delete the code, verify the xfail. The annotations become governance proof.
LLMs have training cutoffs. APIs change daily. Context Hub is a curated knowledge layer that gives AI agents access to operational truth — the undocumented behaviors, edge cases, and hard-won lessons that exist nowhere in their training data. Plus: the full toolkit with @fixed_by and git-mcp.
A novel neural network output architecture co-invented by a human and an AI instance. Uses unit circle geometry to enforce complementary outputs by mathematical construction, not learned correlation. The radius encodes confidence. Neither inventor would have found it alone.
Every page on our site carries structured semantic data invisible to human readers but parseable by AI agents. The data-dim attribute encodes facts, quantities, and relationships in a nested dimensional notation.
Programs have boundaries: configuration, events, resources, injected dependencies. The .bnd specification language declares what crosses each boundary, then generates correct implementations in Python, Rust, or Zig from a single spec.
Time-dependent code is hard to test. We inject clocks as constructor parameters — each component gets its own reference frame. In production, they read wall time. In tests, time only moves when you say so. Test suite runtime dropped 27%.