Multi-Agent Systems: When They Pay Off and How to Build the Harness Around Them

In February 2026, Nicholas Carlini, a researcher at Anthropic, described an experiment. He left sixteen copies of Claude to write a C compiler in Rust. Each copy ran in its own container, in a loop that started the next task as soon as the last one finished. The agents claimed work by committing a lock file to a shared git repository. They merged each other’s changes as they went, and no manager agent told them what to do. Over two weeks and nearly 2,000 sessions, they produced a 100,000-line compiler that builds the Linux kernel. The API bill was just under $20,000.1

  1. Carlini’s post reports 2 billion input tokens and 140 million output tokens on Opus 4.6. The compiler passes most of the test suites he tried, but he is candid that its output is less efficient than GCC with optimizations disabled. These costs and results come from one researcher’s experiment, not a representative sample of projects. ↩

Read More

Removing the Human From Code Review: How to Let Machines Own the Merge

It is Monday morning on a team that made the switch to coding agents around six months ago. While the team was asleep, the agents created a dozen pull requests. Two engineers are responsible for review this week, and each PR contains several hundred lines of code they did not write and only partially understand. By midday they have approved four, quickly looked through three, and told the others to hold. Further along the pipeline the agents remain inactive, because each cycle they complete ends with a person, and that person is occupied. This team produces more code than at any previous time. It deploys only slightly quicker than it did twelve months ago. And the two engineers handling reviews are currently the most discontented engineers in the building. If any of those details feel familiar, this post is for you.

Read More

Career Progression for People Who Build Software

Nobody is coming to plan your career for you. The industry will happily let you drift: take the next role that opens up, learn whatever tool lands in front of you, stay in the city you happened to grow up in, and find out at the annual review whether any of it moved you forward. Drift can look like progress for years, because the job keeps getting renamed around you, analyst, then data scientist, then machine learning engineer, now AI engineer, and a new title feels like movement. It is not. Looking at the people who have genuinely accelerated, in data, ML, AI, or traditional software, the acceleration never came from the titles. It came from a handful of deliberate choices that most people never consciously make, and that is what this post is about.

Read More