After two years of false starts with the Bristlemouth platform and permit headaches, I’m revisiting my whale song project with a completely different approach: semi-autonomous floating drones. The regulatory landscape has forced a rethink, and honestly, it might lead to something better.
Looking back at 2025, one theme dominates: AI agentic programming went from novelty to necessity. This year transformed how I think about software development, career advice, and even my personal hardware projects. Here’s what I wrote about, what I learned, and what I expect in 2026.
Five factors now drive language choice: developer adoption, job market demand, LLM code generation quality, concurrency handling, and deployment complexity. Here’s how they intersect.
Python dominates robotics today. C++ is the serious choice. But I’m starting to think we’ve been sleeping on Go. Here’s why I’m actively exploring it.
In my previous post about Claude Code, I talked about using VS Code devcontainers to safely run Claude in “dangerous mode.” That was great for a typical software project. But what about something harder? What about porting a complex hardware-interfacing library from Python to Go, where you need to deeply understand USB protocols, radio registers, and firmware internals?
That’s exactly what I did with gocat – a Go library for controlling the YardStick One sub-GHz RF transceiver. And Claude Code was instrumental in making it happen.
So I’ve been working on this project called client-w-mcp – it’s a leanring project to truly understand how an AI agent works with MCP servers. And I’m exploring Agentic development - with Claude Code.
The first time I used it, Claude just… flowed. It seems to do a lot more by itself to figure things out. I especially like the Task(
I decided to learn how to write gRPC code in golang - and used ChatGPT as an accelerator! Jump to the writeup.