Peter Forret
because eloquence is, like, whatever
Peter Forretbecause eloquence is, like, whatever
If you work in software, you’ve probably had the same weird couple of years I’ve had. You ask an LLM to fix a bug, it fixes the bug. You ask your coding agent to improve the architecture and performance of a complete app, and it does just that. Somewhere in the back of your head a voice asks: okay, but where does this actually go? At some point I got tired of getting my answer to that question from tweet threads and podcast hot takes, so I went to the source material.
If you’ve raised kids, you know: they don’t develop in a smooth upward line. For weeks nothing seems to change — and then overnight they’re walking, or talking in full sentences, or suddenly grasping abstract concepts. Developmental psychologists call these growth spurts: periods where the brain reorganizes itself and a whole new level of capability clicks into place. The in-between stretches aren’t wasted: that’s when the groundwork is laid, but the visible breakthroughs come in bursts.
One week ago I started TropicClaw: an analysis of the gaps between Claude Code and the new paradigm of OpenClaw.
I wanted to understand what the secret sauce was, the combination of capabilities that made so many people go wild about it.
TL;DR: I’ve created a daily updated AI terminal coding agent leaderboard (i.e. Claude Code and its competition), ranking popularity based on monthly downloads. It lives on https://terminalai.forret.com/.
TL;DR: “Adding an autonomous AI agent to a late software project makes it later.”
In the preparation of my PromptParlour meetings, I wanted to get an idea of who in Belgian government is responsible for
understanding and forecasting the (exponential) progress of Artificial Intelligence, and its impact on Belgian business, innovation and society.
Who do Belgian/Flemish ministers call when they want an expert opinion on legislation or government intervention?
TL;DR: I compared three AI coding agents (Claude Code, Codex CLI, Gemini CLI) for transcoding an existing audio-processing C program to Golang.
The clear winner in this case was OpenAI’s Codex. See the results on github.com/pforret/leqm-nrt.
TL;DR: I created a Jetbrains plugin in Kotlin, a language I don’t master, with Jetbrains’ own Junie AI Agent.
It went quite easy, and now I have my first published plugin in the Jetbrains marketplace.
Source code: toolstud-io/LlmBrains
