Aegis
A risk-scaled SDLC workflow for AI-assisted software development, with bounded work, testable increments, decision evidence, and human approvals.
Focus 01 · AI engineering
Build with AI.Decide with confidence.
I use AI to accelerate software and systems work, then keep the consequential decisions grounded in tests, evidence, and human judgment.
01 / What I bring
Useful AI needs boundaries.
The interesting part is not asking a model to produce more output. It is designing a workflow that makes the output testable, the decisions visible, and responsibility clear.
Risk-scaled deliveryBound work before implementation and match review depth to consequence.
Grounded memoryExplore local, auditable context with provenance and rebuildable summaries.
Human ownershipUse AI for leverage without asking it to own the outcome.
02 / Working model
Move quickly without losing the trail.
This is the pattern I keep returning to when AI enters a serious engineering workflow.
01
Bound the work
02
Build and test
03
Verify the result
04
Keep the decision human
03 / Selected work
Projects where the method is visible.
These projects explore governance, reflection, durable context, and author-led creative tools.
GitHub ↗
Mind’s Eye
An ongoing exploration of durable AI memory using event-first design, provenance, local storage, and hybrid retrieval.
Review Assistant
A human-centered work review system that turns decisions, commitments, open loops, and patterns into useful reflection.