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I’ve been running Kimi K3 alongside Claude on my normal coding work, and for all practical purposes I can’t tell them apart. Same tasks, same quality of output, and near identical token counts to get there. I expected an open model to be sloppier or to grind through more tokens on the way to the same answer, and neither turned out to be true.
There are three ways to do AI coding at home without spending like a company, and which one fits depends mostly on how much you trust the next year of hardware and model releases. The first is to self host. You buy the machine, run open source models locally, and pay nothing per token after that. The upfront cost is steep and the models you can actually run at home are weaker than what the…
The tools are already good at the work in front of them. Give an agent a clear task and enough context and it will write something reasonable. The harder questions show up on large projects with large teams, where the code is one part of a much bigger system of decisions, and two pieces are still missing.
Another Round follows teachers who experiment with drinking to see if it makes them better at their jobs, chasing a kind of Balmer Peak effect. It works for a bit. Then it slides into dependency and one of them dies.
Plastic won. People complain about it, but it unlocked access to things that used to be out of reach. It made products cheaper, more available, and more disposable. It spread everywhere because it was good enough.
The recent paper “Professional Software Developers Don’t Vibe, They Control” is a solid read. The authors studied experienced developers using coding agents and found what most of us already know in practice: pros don’t hand over the wheel. They plan, they steer, they verify.
I keep seeing teams lean on AI personas as if they are a substitute for sound engineering practice. The pattern is familiar: line up a “Product Manager,” “Senior Engineer,” and “QA” persona and let them talk it out. As much as I hate to admit it, I get the appeal. It feels like you are recreating a product team in a chat window. In practice, it rarely leads to better work.