Since agents became good enough to write low-stakes software without you needing to validate the code they write yourself, I've been experimenting with several different ways to build tools for myself on demand. If I'm working on a PC, the overhead required to scaffold a CLI, webapp, or even native...
One of the most limiting factors of LLM agents is they have limited cross-session memory. When working on software projects, this limitation isn't much of an issue because the agent can search the project and read files to understand the current state and what it needs to do to make the changes...
What is it to build software with coding agents? It's something distinctly different than writing code by hand.
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...