Tailor AITechnical deep dives from the engineering team. How we ship, scale, and solve hard problems.
I've been running out of Claude Code tokens on the $200 plan, so I decided to dive into Codex for active development. I've been running 5+ parallel coding tasks pretty much all day.

I thought I was working on the cutting edge. I wasn't. Not until our product was drivable by an agent. We built an MCP server. Looked like an integration task.

Tailor edits pages at serve time, in the visitor's browser, on sites we didn't build. Making the change is easy. Making it survive a framework that doesn't know you exist is the work.

Our push command took 14 minutes, and only half of that was work. Fixing it meant moving verification to CI, and then fighting CI for a week over races, caches, and a green PR nobody could merge.

The most useful prompt pattern we found this month is a loop with a harsh critic in it, and the critic has to be a different agent than the one that did the work.

Once you work on more than one branch a day, the bottleneck stops being code and becomes the environment. Five permanent worktree slots, each with its own ports, subdomain, test org, and JWT.

Our script runs on customer pages, so the honest test of a change is whether it works on a real page. You can't edit their HTML, so we built a bookmarklet that injects any build onto any page.

The fastest way to improve an agentic feature is to make its reasoning easy to copy: one blob you can paste back into the model that produced it.

Leaving everything on the most capable model runs you out mid-week on work that never needed it. Route per subagent, not per session.

We went from 'helpful autocomplete' to an AI development partner that ships code, runs tests, and operates inside our real workflows. Here's how we built the harness around Claude Code.