Ai

A few days ago, I heard about Pilish for the first time. As Wikipedia puts it, Pilish is a style of constrained writing in which the lengths of consecutive words or sentences match the digits of the number π (pi). My second thought (right after “who would enjoy doing that”) was, this sounds easy for LLMs! So I set out to make a minimal proof of concept. Long story short, the trick is to constrain the output to follow the digits of Pi—the same way that structured output (JSON, etc.) works. With Hugging Face Transformers, it’s easy to do with a custom logits_processor. Here, we just constrain the tokens to those matching the desired number of characters at each step. I shared the code on GitHub. ...

Lot of things going on this summer, but yesterday’s big news was the release of ChatGPT 5. I don’t have an opinion on it yet, but I guess it’s nice to have a single model (at least exposed, even if this is routed under the hood) instead of switching between 4o and o4-mini-high, etc. Recently I’ve been on the market for a “CLI Agent” (synchronous, like Claude Code, not async like Devin and co.). I’ve been using Gemini CLI with good results — as long as I’m within the free PRO requests. As soon as it switches to the “flash” model it becomes useless, can’t edit a file properly, and just loops on itself. I wouldn’t pay for it. Claude Code seems too expensive; I might give OpenAI Codex a try with GPT-5, but I haven’t heard much about it. ...

So I started reading a paper about diffusion (the original DDPM paper) and I was quickly out of my depth. I needed a refresher about probabilities, and actually even more basic stuff like exponential and integration. And I thought why not share the notes here! So this blog post is the content of a python notebook about exponential and the normal distribution, exported to markdown with jupyter nbconvert. It’s not deep or anything, just a nice refresher for myself. Kind of a pain to write math formulas and get them displayed in my Jekyll blog but got it working with Mathjax. ...

Just a quick update to say I am still making progress on the Password Game Agent project I mentioned last post. I have now reached up to step 16 where we need to solve a chess position… Seems like a suitable job for a reasoning model ! The main changes that enabled going from step 11 (Wordle answer) to 16 were adding a “search tool” based on OpenAI web search tool and changing the reasoning effort from “medium” to “high”. The current version of the code is here : https://github.com/goverture/password-game-agent/blob/master/manually.py I’m still making regular changes and trying new ideas to make progress. I’ve noticed we often seem to get stuck for various reason (for instance a badly recognized Capcha), so I want to add a new step to ensure that we make progress consistently, or backtrack, in order to not get stuck.

There is this javascript game called The Password Game where you have to chose a password, following increasingly ludicrous requirements. Agentic coding/research is all the rage today and I got the idea of making an “agent” that would solve it. Conceptually it’s just about calling a LLM in a loop, giving it the proper tools and context until it solved the task at hand. Here’s where I got so far, using ChatGPT 4o and Playwright MCP (only gave it the navigate and type tools for now). It’s solving correctly the first few steps, but gets stuck at the “sponsor” rule because it cannot view the image. The next step is to give it access to the playwright’s screenshot tool. Let’s see how far it can go ! ...