Khivi

Building with AI

Call it vibe coding if you like — but I treat AI like any other part of the stack: deliberately. AI writes the code; I own the system. Tests stay non-negotiable, reviews stay real, and no AI output ships on vibes alone. AI made me faster, not looser.

So I built an AI platform around it, not a habit of pasting prompts: dozens of skills that encode my AI workflows, specialized AI review and security agents, multi-agent orchestration through Cockpit, the open-source orchestrator I wrote, file-based memory that persists what matters, and a token-proxy CLI that cut my AI usage by more than half. It's the same instinct as the rest of my work — optimize the system, not the parts. More on building with AI in my Substack and the AI-era edition of Culture Seeds.

Engineering Outcomes

Every line of code is a liability. I build systems where the complexity lives in the business domain, not the technology. Simple code, clear interfaces, boring technology that your team can master — that's what ships reliably. AI is no exception — AI generates code faster than ever, so it needs guardrails and a clear return, not just output. Every AI diff answers the same question: is this smaller, and is it worth the complexity it adds? What the AI era changed isn't the bar — it's where the effort goes: less time writing code, more deciding which AI-generated code earns its place.

I design for change, not perfection. Customers invest in outcomes, not technology. I make architectural decisions that keep options open, delay irreversible choices to the last responsible moment, and keep the cost of iteration low. When your assumptions change — and they will — your system should adapt gracefully.

Execution Culture

Strategy without execution is a slide deck. I build execution cultures — where sprints actually work, PRs get real reviews, master is always deployable, and deployments are zero-touch. I've codified this into what I call The KHIVI Test: ten questions that tell you if your engineering org is actually shipping or just busy. AI changes the tempo, not the rules: AI-written code clears the same gate as everyone's — tests, automated review, AI security scans, master always green. AI brings the speed; the gate brings the trust. Our cadence evolved with AI — smaller diffs, shipped more often, each one cheap to generate, review, and revert.

I believe in small experiments over grand plans, fast feedback over perfect specs, and teams that own their quality end-to-end. Product, engineering, and business aren't separate functions — they're one team solving one problem.

Mentorship

The best engineers I've worked with weren't born great — they were mentored well. I challenge engineers to come up with their own solutions, guide them through the tradeoffs, and create environments where they can take ownership. In the AI era that means teaching them to interrogate AI — review AI-written code like any other, ask "why is this better?", keep the judgment human. The craft itself evolved with AI: juniors once grew by writing the loop; now they grow by judging the code AI wrote — which demands taste sooner. I wrote Culture Seeds to capture the patterns that build great teams: how to hire, how to set standards, how to create a culture where quality is the path of least resistance.

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Building something? I can help.

Architecture reviews, hands-on coding, team building, testing strategy, production debugging, or figuring out what to build next — I've done it all and I'm still doing it.

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