AI-assisted development is a multi-pronged thought process. It is a prompting optimization game as well as a model personality/tendency and a big rework problem.
The model based on its tendency could decide if it wants to take the prompt too literally or maybe just an outline of what you want and still ignore some pieces that it doesn’t want to care about. GPT 5.5 and Opus 4.7 are prime examples for the divergence in behaviour. Opus is more exploratory in nature while GPT 5.5 takes its instructions pretty seriously (as of today). Once you understand how different models behave, you may unlock the power to work with them as a partner by using the subtle persuasion techniques. I have been dealing with these subtleties of different models for the past 8-10 months of my journey evolving my workflow with them (not the exact same versions, but you catch the drift).
Claude Code runs in my terminal all day. Although I have started dabbling with Codex, Opencode and others but Claude Code is still the primary thing I’m looking at for hours at a stretch for now. All of those harnesses run on a dedicated VM, not my main machine. Daily backups, isolated environment, nothing else lives there. At some point I realized my terminal config mattered a lot more than it used to.
Most Kafka sizing advice is hand-wavy. “Start with 3 brokers.” “Scale when you hit a bottleneck.” “Talk to your vendor.” I have spent years on both ends of that conversation, as the engineer asking and the person being asked, and the answer is almost always some version of “it depends, let’s just see how it goes.” That isn’t sizing. Its the easiest way to say “I don’t know.”.
“Which topic is costing us?” is the question every platform team gets, and nobody can answer it cleanly. SaaS providers bill you at the cluster level. Your finance team wants a line item per team. Your product managers want to know if their pipeline is the expensive one. You have a Kafka cluster, a bill, and no bridge between them.
I’ve spent 17 years building streaming platforms, integration systems, and open-source tooling. A lot of what I’ve learned lives in conference slides, GitHub repos, and conversations that are hard to find again. This blog is where I put it all in one place.