The AI train has left the station. Some of us are on board, generating entire modules from specs. Others are still on the platform debating whether the train is even real. Four smart people wrote about this shift this past week. They all agree on what it means. And I agree with them.
If software changes something in the real world, it’s a robot.
Software development is stratifying into three distinct tiers. Where you land will determine whether you make more money, the same, or far less than today.
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.
“We are nearly at coder-equivalency for economically useful coding. A sufficiently experienced software engineer can now write >90% of production-ready code purely through prompting.” Yes, exactly.
I let Claude Code do whatever it wants without asking permission.
Before you spit out your coffee: I keep it in a jail. A container jail.
I don’t like the term “vibe coding.” I strongly prefer “AI-assisted software engineering.” But regardless of what you call it, Gene Kim and Steve Yegge have written a book about it - and their journey from skeptics to true believers is the most interesting part.
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(
There’s a lot of folks who are in outright denial about AI agentic programming. They are wrong. AND… they are right. My thoughts.
There’s no way to really understand something unless you dive into first principles. This is especially true for AI coding agents. What is the editor doing under the hood? I try to peel the onion a bit.
I’ve been exploring the intersection of AI and content creation lately, and I wanted to share my experience using Anthropic’s Claude 3.5 AI assistant within the Zed editor to streamline my blogging workflow.
Reading time: 10 minutes
It’s brutally hard to find a job as a new college grad today. And AI is changing things even for seasoned Engineers. Here’s my advice.
The evolution of AI-assisted programming has reached a pivotal moment, transforming from simple code completion to truly collaborative AI agents. This shift represents not just an improvement in tooling, but a fundamental change in how software is developed.
I bought the eBook AI at the Edge. But you can download a free copy from LinkedIn. In short? Don’t waste your money. But for free?
Let’s talk about AI pair programming - not the hypothetical future version, but what we have right now in 2024. I’ve spent the last year working extensively with AI coding assistants, and I have some thoughts to share.