Engineer working on AI systems, inference and the infrastructure underneath them. I write things down so the next person does not have to rediscover them.
I'm Yogesh Sen. I build and operate systems that run machine learning models in production, and I write about what I find while doing it.
Most of that work sits at the boundary between a model and everything around it: the serving layer, the memory budget, the evaluation harness, the part of the pipeline that quietly decides what is possible.
Day to day, that means inference performance, agent architectures, and the infrastructure underneath both. I spend a lot of time with profilers, a fair amount with papers, and more than I would like with GPU memory allocators.
Two reasons.
The first is selfish: writing an explanation is the fastest way I know to find out whether I actually understand something. Most of these pieces started as a note to myself that turned out to be wrong somewhere in the middle.
The second is that the field moves quickly and explains itself badly. There is an enormous amount written about what AI systems do and comparatively little about how — the mechanics, the trade-offs, the numbers. That gap is where I try to write.
Articles are written in Markdown and stored as Markdown — no proprietary format, no lock-in. Every claim that comes from somewhere else has a reference at the bottom of the piece. Measurements include the hardware they were taken on.
There are no trackers, no advertising, and no third-party scripts. Analytics here means a daily-rotating hash that counts a view once and cannot be linked back to a person. Likes work the same way: a random id your browser keeps, which the server only ever sees as a salted hash.
The fastest way to reach me is LinkedIn — I read everything that comes in, and corrections are genuinely welcome, particularly on the benchmark numbers.
If something here was useful, or wrong, I would like to know which.
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