MLOps Shenanigans: Wrapping Up 2025

As 2025 is coming to an end, I thought it made sense to close the year with a short note.

First, thanks to everyone who took the time to read. Especially those who engaged with the posts. The original goal of this newsletter was fairly simple: to use it as a learning vehicle for myself and as a way to better crystallize my thoughts around different topics. Even so, having people respond adds another layer to this. It brings in different viewpoints and useful feedback, and to be frank, it’s motivating.

On a personal level, 2025 was a busy year. I got married and changed jobs. While I could say that this was the reason I didn’t write as much, I think the reality is that I could have just written more, as simple as that. I feel I missed out a bit on that, but hopefully next year will be better.

Professionally, the year was both exciting and a bit frustrating. There were genuinely interesting advances in areas like post-training, test-time compute, and accelerator hardware. The scale and growth of some projects and products were impressive. At the same time, it often felt like anything labeled “AI” sucked all the oxygen out of the room. Endless benchmark comparisons that rarely translated to real-world performance, constant prompt and instruction guides, and a steady stream of new “agentic” tools that you were supposedly missing out on unless you were using the latest version. As someone interested in software engineering and computer science more broadly, it became hard at times to find genuinely novel work, given all that noise.

Anecdotally, I saw something similar reflected in this newsletter itself. The posts that sparked the most interest this year were, in order:

• Advanced, Overlooked Python Typing

• Tensor and Fully Sharded Data Parallelism

• Zero Temperature Randomness in LLMs

And just like in 2024, articles focused on Python topics, whether language features, specifics, or tooling, saw significantly more engagement than pieces about large-scale model training, inference, or MCP. It makes me wonder whether, despite ongoing interest in deep learning and production ML, people are increasingly looking for engaging software engineering and CS work because those topics feel less consistently covered. But as said before, it may be purely anecdotal.

To wrap this up: I hope you had a great 2025 and that 2026 treats you even better. Thanks again for reading. Below I’m adding a short poll on software engineering topics. If you have a minute to fill it out, it will give me a better sense of what you find interesting and what feels missing from the technical landscape these days. Either way, best wishes for the year ahead!