Stark Raving Finkle

Two posts back, I used Kent Beck’s 3X framework to argue that agentic engineering gets read at different phases of maturity depending on how far you sit from the code.

In my last post using Kent Beck’s 3X Framework, I argued that agentic engineering is being perceived at different phases of maturity depending on where you sit.

Agentic engineering is happening at a breakneck pace, and many are wondering if they are making the “right” decisions and using the “right” approaches.

We’ve been building a more agentic engineering organization at League over the last ten months.

Most teams measure AI impact on software delivery the same way: before and after comparisons on tasks that were already in the plan.

There’s a growing belief in engineering circles that because coding agents can generate code so cheaply and quickly, we no longer need to invest in platform frameworks, services, and shared systems.

League (my day job) recently enabled mandatory GitHub Copilot code reviews on all pull requests.

For years, we’ve been told that software is eating the world.

I moved my 20+ year old WordPress blog off Bluehost and onto a modern static site setup with Hugo and Cloudflare Workers.

A few months ago, I wrote about vibe coding and the challenge of shipping quickly-created prototypes.

AI-assisted coding tools and agents have unlocked an unprecedented level of functional prototyping.

I’ve been using Copilot in VS Code to help build several different projects.

I had a lot of fun working through my “Exploring LLMs as Agent” series.

Up to this point, I have been experimenting with my own home-grown agent framework, based on Simon Willison’s great LLM project to handle wrapping the different LLMs.

I finally got around to exploring local models, which is surprisingly simple to set up.

I will likely run out of topics to cover in the “LLMs as Agents” series, eventually.

Search Here you can search for indexable pages, like posts and alike.

My fifth post on LLMs as Agents.

I spent some time refactoring the Tool Agent code, added some additional mock tools and even some basic benchmarking.

My exploration of how to use LLMs as Agents continues.

I’m still exploring how to use LLMs to build agents.

Large Language Models (LLMs) are powerful tools for generating text, answering questions, and coding.

With engineering focus on reducing incidents and improving operational reliability, I frequently come back to the realization that humans are fallible and we should be learning ways to nudge people toward success rather than failure.

I’ve had cause to looked into research and ideas about the ways information flows within organizations.

I have been wanting to play around with an ESP32-based micro for a while.

Looking back at one of the first real projects I attempted which combined 3D printing and microprocessors.

I’ve been playing around with some WS2818 / NeoPixel LED 8x8 grids and CircuitPython.

“Developers should only need Github Issues and Pull Requests to do their job” — Why should anyone need more than that to track work?

Practice makes perfect — Anonymous Parent A theme that keeps popping up in my world is the idea of how often an action is done being correlated to how well the action is done.

I often wonder if I’m being effective at my job.