ai - The View from the AI Frontier

An engineer seen from behind, sitting at a desk with five monitors showing structured data. Beyond the glass, a vast sprawl of servers and infrastructure glows in the darkness.

Deterministic Automation for a Probabilistic System

AI agents are probabilistic. The same prompt doesn't always produce the same output. That's not a problem to avoid. It's a problem to engineer around. Typed schemas, validated execution, deterministic workflows. The agent reasons freely. The system keeps it honest.

Aerial view of five people around a wooden table, each working on a laptop. A glowing holographic system architecture floats between them, connecting their independent work into one design.

Building with AI

High Trust Teams With Agents Are Unstoppable

High trust doesn't mean no guardrails. It means everyone understands the direction well enough to act independently within it. When you pair that with the right systems, every person on the team can advance the product every day.

Aerial view of a large architectural blueprint under multiple desk lamps, each illuminating a different section of the same design. Drafting tools and rolled plans scattered around the edges.

Building with AI

The Rearchitecture Your Team Could Never Justify

Every codebase has the rearchitecture that never gets prioritised. We split ours into six parallel workstreams and shipped it in eight days, start to finish. The hard part wasn't speed. It was coordination.

An elegant classical column buried under haphazard additions of scaffolding, wooden crates, and makeshift structures. The original form barely visible beneath the accumulated weight.

Building with AI

When Building Is Easy, the Danger Is Building Everything

When agents can build anything in an afternoon, the temptation is to build everything. The products that survive are the ones where someone had the discipline to say no.

Silhouetted figure standing at the edge of a crumbling stone cliff, facing a vast expanse of warm golden light. Fragments of ground fall away behind them into mist.

Building with AI

Take the Leap Before You Are Pushed

The fear of AI replacing your work is real. So is the shift. The teams that build structure around agents instead of resisting or rushing find the job gets better, not smaller.

Tangled multicolored light streams converging through a dark field into structured parallel lines

Building with AI

The Code Was Never the Job

Five of us build swamp. None of us write code. Agents write it, review it, test the binary. The work didn't shrink — it shifted to the decisions that were always the most valuable.

Glowing orange tokens streaming into a dark industrial furnace, burning bright on impact, scattered embers falling below

Building with AI

Your Agent Is Starving

Five people, $3,000/month, zero lines written manually. GitHub just moved to per-token billing. The era of flat-rate pricing absorbing your agent's inefficiency is ending. Your agent isn't the bottleneck. What you're feeding it is.

Building with AI

The Community Pull Request Is Dead

The PR-as-contribution model was built for a world where writing code was the bottleneck. AI agents broke that assumption. GitHub shipped a kill switch on PRs. Hashimoto built Vouch. We closed the door entirely. The community pull request is dead. Here's what replaces it.

Three nested layers (what it can do, how it writes code, how it thinks) surround an agent dot. An arrow leads to a red gate labelled "gh pr create permission prompt," then out to "public."

Building with AI

Agent Trust Is a System Design Problem

Most people hit "always allow" within the first hour. Agent trust is a system design problem, not a containment problem. Three layers of permissions, conventions, and context let the agent work autonomously where it's safe and stop it at the boundaries that matter.

PR flows through a UAT gate with three layers — CLI tests, adversarial, and benchmarks. All pass promotes to Homebrew, Docker Hub, and CDN. Any fail blocks the binary.

Building with AI

The Gate Between Our Agent Code and Our Users

Every release binary gets dispatched to a separate repo running CLI tests, adversarial security tests, and benchmarks. Three recent bugs — all with full unit test suites passing — never reached a user because the UAT caught them in minutes.

Four AI reviewers — adversarial, code review, UX, and CI security — each examining a PR from a different perspective, converging on a single auto-merge gate.

Building with AI

Anatomy of a Swamp PR

Agents open PRs in our repo. Other agents review them. That means untrusted input flowing through models with merge authority — so we built a pipeline with four independent AI reviews, scoped tools, and a security review that watches the reviewers.

$Fork in the road — left path fractures into tangled red lines (drift), right path leads to an ordered green city grid (coherence). Yellow dot at the fork: "every PR is a choice."$

Building with AI

The Vibes Don't Scale

Vibe coding works great for the first few PRs. Then you do it two hundred more times and the codebase quietly stops making sense. The fix isn't better prompts — it's building a machine that compounds.

Swamp

Execution Drivers in Swamp

Swamp model methods can run anywhere — directly in the host process, in a Docker container, or in a custom driver you write yourself. This post covers how execution drivers work, how config cascades from model to workflow to CLI, and how to build your own.

Person sitting at a desk viewed from behind, facing multiple monitors displaying code editors, terminal windows, and data dashboards in a dark room with warm ambient lighting

ai

AI: Why I'm All In

I'm an AI maximalist, and I think operations is about to get left behind: again. This blog is about closing that gap from the inside.