muhammadraza.me

Every few months I have the same conversation. A small team, three to eight engineers, is containerizing their app, and someone says “we should use Kubernetes, that’s the industry standard.” Six months later they’re maintaining a small distributed systems platform on the side, and the app they were supposed to ship is still competing for attention with CNI upgrades. I’ve written before about the ECS decisions that waste six weeks. This post is the prequel: what ECS actually is, how it maps onto…

Every few weeks someone downloads the GGUF build and the MLX build of the same model, runs both, screenshots the tokens-per-second counter, and posts it as proof that one format wins. The replies split down the middle. Half the thread says MLX is obviously faster, the other half says the test was rigged. They are both right, which is the problem. The number on the screen is real and it is also not the number you actually wait for. And the format you should pick was never really about that…

Every coding agent session starts from zero. The agent doesn’t know how your code is organized, which files matter, how the pieces connect. It has to rediscover the architecture from scratch. Grep around, read some files, build a mental model, start working. That mental model disappears the moment the session ends. I kept watching this happen. Ten minutes of exploration before any real work, every single time. If you work across multiple repos or come back to a proj...

I have too many issues and not enough time. Same as everyone. The usual loop is: pick an issue, context switch into it, write the code, open a PR, pick the next one. Do that until the sprint ends or you lose the will. Coding agents help with this. I can point Claude Code at an issue and let it work while I do something else. But that’s still one agent, one issue, one terminal. If I have 10 issues labeled “agent-ready,” I’m not babysitting 10 terminal tabs. I wanted...

I’ve written before about how AI agents are just CI pipelines with an LLM plugged in. That post mapped agent concepts to infrastructure patterns you already know. But there’s a discipline forming around the infrastructure side of agents that deserves its own name. Harness engineering. It’s the practice of building everything around the LLM — the execution environment, tool definitions, safety boundaries, observability, and lifecycle management. The stuff that turns ...