Dima's Blog

While I no longer work for an AI robotics company (after more than five years at Waymo, Cruise, and 1X), edge inference and embedded compute are still very close to my heart, and from time to time I play with them in my hobby projects.

For decades, the AI world struggled to define "agent."

In the previous post, I mainly focused on software and data engineering lessons from working on the Caltrain bot.

In the previous post, I described what I learned about setting up a Python project and how I plan to set up all my Python projects, including Caltrain, going forward using uv, ruff, and ty.

Theory-practice Gap & Praxis I often remind myself and my peers that the gap between theory and practice is real and very common.

Approximately 85% of my career as an engineer has been spent writing Python.

When I was trying to learn more about LLMs, I found terms like pre-training, post-training, base model, instruction following, and reasoning really confusing.

You've probably heard about embeddings in the context of LLMs.

It might feel like Machine Learning (ML) has been around forever, but ML with Neural Networks (NNs) only became mainstream in the 2010s.

As a software engineer with a background in large-scale distributed systems, I've consistently struggled to create user-friendly interfaces for my projects.