AI does the heavy lifting. Experience does the judgment.
I work with AI agents to build, review, and ship software faster than a solo developer has any right to. The agents handle volume. I handle everything that requires knowing when to stop and push back.
The Method
Every project runs through a two-layer process. The first layer is Hermes — a local AI agent with persistent memory, reusable skills, and access to the full codebase across sessions. It is not a one-shot prompt. It remembers what we decided last week, what went wrong, and what we learned from it.
When something breaks, the lesson gets saved — as a memory entry or a skill update — so the same mistake doesn't happen twice. The agent gets better over time, not because the model changes, but because the context does.
Hermes handles: implementation, test generation, code review passes, refactoring, documentation, and first-pass architecture proposals. It does this fast, and it does it consistently. It doesn't forget what we decided last week.
I handle: the gut check. Does this feel right? Is the security posture sensible for what this system actually does? Where are the performance trade-offs, and do they matter here? How does this fit into the broader integration strategy? And — critically — when is the agent confidently wrong, and what do I say to redirect it?
That last one matters more than it sounds. AI agents fail quietly. They produce well-structured, grammatically correct, thoroughly commented code that does the wrong thing. Catching that is a judgment call, not a checklist. That judgment is what fifteen years in the industry buys you.
The workflow is not proprietary. It is teachable. Getting your team building this way — and avoiding the failure modes — is exactly what the AI Workflow Audit is for.
When Experience Matters
Here is a concrete example. A client site was getting strong Lighthouse mobile scores in CI. Playwright was happy. Eight separate AI review passes across two tools did not flag anything. The score looked fine.
On a real phone, the layout was broken. Text was unreadably small, elements were overflowing, the experience was unusable.
The cause was a missing viewport meta tag. One line. The kind of thing that every browser simulation gets wrong because it is not simulating — it is approximating. Lighthouse runs in a controlled Chromium instance with a forced viewport. A Samsung Galaxy in someone's hand does not.
No agent caught it. I caught it because I tested on a real device, which is a habit, not a workflow step.
That is the gap. Tools simulate. Humans verify. The value of experience is not that it replaces tooling — it is that it knows where tooling lies to you.
What I Can Do For You
AI Workflow Audit
I assess your team's development workflow and integrate AI tooling where it creates real leverage. Not a generic AI adoption talk — a hands-on review of how your team actually works, followed by a concrete implementation plan.
Fractional Tech Lead
An experienced tech lead for your product on a part-time basis. Architecture decisions, code review, team mentoring — with AI augmentation built in from day one. You get senior oversight without the full-time hire.
AI Tooling Setup
Custom AI agents, self-hosted workflows, and development tooling built around your team's actual process. No per-seat SaaS costs, no vendor lock-in.
See It In Practice
The best way to understand this workflow is to see it running. The posts on this site are produced by the same system they describe — Hermes writes, Claude reviews, I decide what ships. The tooling, the process, and the failure modes are all documented as we go.