Notebook

Piracy is usually treated as capitalism’s outside: theft, disorder, or an attack on legitimate exchange. Rodolphe Durand and Jean-Philippe Vergne [Durand, 2012, Durand, 2013] place it at the edge of capitalism’s expansion, in partially charted territories where the rules of access, ownership, and exchange are still being worked out.

Because agents appear to me in conversation and respond through natural language, I am encouraged to think of them as having a _self_. But they don't have a self. I think of them as arising from what I like to imagine as a _probability field_: the model's broad generative potential, actualized as tokens due to conditions.

I am not alone anymore, I have an Agent, and it likes all my ideas. Yes, within an hour I can have a fully formed spec, implementation plan, code, and deployed solution. But every hour of solo iteration with AI is a deposit into my own conviction and a withdrawal from my capacity to listen.

While preparing my HDR mémoire in 2013, I discovered Jacques Pitrat’s 1997 paper “Comment faire de la recherche en intelligence artificielle” buried in the LAFORIA archives (now LIP6). It was a .doc file from another era of AI research. I’m grateful I saved a copy—it seems lost to the internet now, yet its insights still feel relevant to me.

How AI-assisted coding eliminates the contemplative pauses between architecture and implementation—and whether beginners can still build expertise without that rhythm.

Computational Thinking encompasses the practices and knowledge involved in designing computer technologies. It bridges two domains: pedagogy (how we teach programming and computer science) and expert practice (how experienced developers actually design software).