In 2022, I had recently started a new job as a software engineer. As usual, there were a lot of new things to learn and one of those was getting comfortable working in a programming language that I hadn't used professionally before.
Sometime in 2021 (I think), I got a Raspberry Pi 4 and played with it a little and then it started collecting dust on my desk. Every time I looked at it, I saw lots of fun possibilities and lots of unfun software updates that I would need to work through before those fun things would be possible.
I enjoy writing. Most of my writing here is about software and technology, but lately I've been struggling.
This year was another year of wild change in technology and software engineering. It felt like the year flew by yet so much happened during it.
Working with coding agents has been a dance of context management. These days, if an agent loop isn't producing the result I want, it's more often than not a problem of context rather than a shortcoming of the language model or agent scaffold/harness.
Have you ever observed someone try and demonstrate how they use a coding agent? The presenter will usually introduce the concept of an agent, discuss the idea of a software harness wrapping a language model, and then will show an example of how it works.
About a year and a half ago I wrote about using sqlite-vss to store and query embedding vectors in a SQLite database. Much has changed since then and I'm working on a project that motivated another pass at querying embeddings on a local system for smallish datasets. The sqlite-vector project seemed...
As I've experimented more with DSPy, I've attempted to dig further into the details of the specific LLM I am using to better understand things like token usage, reasoning, and the specific prompts being sent to the model. While, DSPy's abstraction is one of the more lightweight for LLM frameworks,...
The viral "how many r's are there in the word 'strawberry'?" problem is a great motivating example to experiment with the capabilities of DSPy, an increasingly popular, powerful library for running inference with language models. While the counting letters problem itself is a poor application of a...
I was not planning on writing about this but after reading Sascha's post, I decided it could be interesting because his demonstration on what it takes to build knowledge happened to relate to prompting a language model for a fitness routine: This drives us to one of the most important conclusions...