For companies tired of developers who can only work within a perfectly described ticket. We gather go-getters who take an idea, figure out the architecture, build it, and ship it (and actually want to work with AI, being very good at it), owning the outcome, not just the Jira ticket.
Let's talkThere's a reason to review one piece of work at a time, but no reason to run only one agent at a time. Most “AI ships slop” takes are right about the slop and wrong about the cause: unsupervised AI ships slop, same as a junior nobody reviews.
Anti-AI is just anti-delegation with better PR. “If it can't one-shot it, I'll do it myself” is the same flinch a newly-promoted lead feels right before they learn to let go.
The best don't flinch. Running multiple agents at once and reviewing the output is what a staff engineer does having a team — shaping the code by shaping what's behind it, not by typing every line. You can generate in parallel, but you can only judge one thing at a time — and that single-threaded judgment is the whole job now. It's what we hire for, and it's who we place.
We place developers who actually want to work with AI — and are very good with it. Here's why:
There's a reason to review the work of just one agent — you can't divide your focus. But the same way an experienced staff engineer delegates to more than one person, our devs delegate different things to a handful of agents — one refactors, one writes tests, one hunts the bug — and let them each work for an hour.
With AI, even a non-technical person can nail the product requirements. But the tech decisions? Those take them ages, or just never land. Agents are optimised to pave the happy path — and the trade-off is… trade-offs. We know what works and what backfires, which is why we say “implement a search based on Postgres's full-text search,” not “implement the search.”
AI ships slop only for the people who don't give it feedback. Left alone it drifts — a confident intern with nobody checking its work. “If it can't one-shot it, what's the point, I'd do it better myself” misses the job: you shape the code by shaping what's behind it, not with your own hands anymore. Our people review, correct, and steer — so what ships holds up.
Everyone posts Kimi K3 benchmark scores; the benchmark itself is harder to find. So we built one vs Claude Fable 5 and Opus 4.8, read every line all three wrote, and open-sourced it.
Claude Opus 5 is out at half the price of Fable 5, with thinking on by default — and one change that breaks existing code. The facts, from the docs.
An MCP server lets an AI model call your own tools. Here's how to build one in Python with the official SDK and connect it to Claude Code — tested end to end.
