The harder a piece of code is to parse, the more you will tend to rely on LLM-based analysis and generation to maintain and build from it. Illegibility to humans is vendor lock-in. That's the business model.

The enclosure of the FOSS commons may seem like a programmer-specific problem, but it really affects everyone. Privacy-preserving apps like Signal, for example, serve a purpose precisely because they're open and can be audited. Take away that ability to verify the developer's claims by parsing the code, and all guarantees are lost. The more that AI vendors succeed in locking in the FOSS commons, the less transparency we'll have into what our software actually does.

It is 0% coincidence that these technologies are being pushed by some of the least transparent companies on the planet.

TL;DR the take is argumented and ends with:

So don't get too worked up about an individual using LLMs, that's not what's undermining the very foundations of FOSS. It's a drivel-spewing idiot who's happily planning to sink another 100 billion $ on a probabilistic text generator. We're talking about people using money that could have solved several world-spanning problems - even profitted from doing so! - and still didn't. That's who we're talking about.

Trouve les dépendances américaines à partir d'une URL. Le site retourne un score de santé sur la souveraineté des technologies utilisées.

The whole content is copied here:

You've probably heard about how (micro)plastics are poisoning everything, from our land and water, to our bodies, and of the absolute malfeasance of the oil industry.

Thus, as any reasonable person, you may be actively looking to eliminate plastic from your life.

After hours and hours of research, my conclusion is that wool (merino & alpaca) and titanium are the best ways to achieve this goal.

Wool is the best material for clothing thanks to its thermal and anti-odor properties (and maybe cotton/denim for pants, because 100% wool is rather fragile). Linen, Tencel/Lyocell and cotton are also fine but have inferior technical properties.

Titanium for cooking utensils. It's my understanding that titanium is not only extremely durable (buy it for life), but also the safest metal for our bodies.

Of course, before optimizing the everything else, you may first want to adjust your lifestyle toward more durable and reusable things instead of disposable/single-use everything.

Pas encore confirmée, mais la piste semble sérieuse selon l'article.

D'après les éléments publiés, les informations potentiellement accessibles incluraient notamment :
👉 noms d'affichage ;
👉 adresses de messagerie professionnelles gouvernementales ;
👉 identifiants de comptes ;
👉 organismes et ministères de rattachement ;
👉 historiques de conversations ;
👉 messages échangés dans les salons ;
👉 contenus de groupes collaboratifs ;
👉 métadonnées associées aux comptes ;
👉 identifiants techniques d'équipements ;
👉 fichiers et documents partagés ;
👉 images et médias publiés sur la plateforme ;
👉 liens de visioconférences Zoom ;
👉 liens de réunions Webex ;
👉 URL de ressources internes ;
👉 informations relatives à certains salons publics ou collaboratifs ;
👉 listes de participants aux espaces collaboratifs ;
👉 dates et horaires des échanges ;
👉 informations techniques relatives aux terminaux enregistrés.

Donc la seule solution reste une architecture zero-knowledge

Un article à charges montrant que la Silicon Valley a perdu son rôle d'innovation suite à la concentration du pouvoir et de la richesse

Coût total du salaire par l'employeur, salaire brut puis les salaire net et salaire net après impôts.

Si l’autorité française a bien adapté ses systèmes pour répondre à la demande, « l’âge moyen et le stock de plaintes non traitées ont augmenté sensiblement » : de 256 jours en 2017 à 309 jours en 2024.

Alors que le budget global de la CNIL a augmenté de 72 % pour passer de 16 millions d’euros en 2017 à 28 millions en 2024, la Cour des comptes l’estime globalement « correctement réalisé » : l’autorité a même réalisé quelques économies de fonctionnement sur la période. En revanche, la masse salariale, elle, a crû (de 194 personnes à 298, soit + 54 %) au point de compter pour plus de 80 % de son budget, sachant que la CNIL fonctionne sur une logique de « recours systématiques de contractuels à durée indéterminée ». 



Le rapport: https://www.ccomptes.fr/sites/default/files/2026-06/20260604-CNIL.pdf
La synthèse: https://www.ccomptes.fr/sites/default/files/2026-06/20260604-synthese-CNIL.pdf

following the post "5 years trying to add recursion to lychee" https://shaarli.lyokolux.space/shaare/STTHtQ

The key takeaway is: they didn’t find a clever trick we missed. They were built as crawlers from the very first commit, and I initially built lychee as a stream.

Why it's still hard and not solved

• the other projects started as crawlers; lychee started as a stream
• the frontier and the rate-limiter must be different objects
• single-threaded runtimes get dedup for free.

it’s a harder problem than just “copy what they do,” because most link checkers didn’t start with uncompromising performance as their top goal.

So the other projects "made it a part of the architecture from the beginning, and they leaned on a runtime (providing conveniences like a WaitGroup, a joinable queue, an idle promise) that solved termination without solving “distributed termination detection.”

instead of letting the call stack implicitly control what happens next (as recursion does), store the pending work in an explicit data structure such as a stack, queue, or heap. This turns control flow into ordinary data that can be inspected, paused, modified, or resumed.

Explicit stacks makes interruptions, limits, cancellation or interleaving work easier to work with. It's more adaptable to real-world constraints and test.

A stack (LIFO) produces depth-first search behavior.
A queue (FIFO) produces breadth-first search behavior.

Mais justement, est-ce possible de gagner en précision en générant des échantillons synthétiques avec l’IA générative en toute « rigueur scientifique » ? Est-il possible, à partir de 116 interviews, de générer 464 autres de manière synthétique, pour obtenir un total de 580 interviews d’enseignants de collège et de lycée, ce qui nous amènerait à gagner en précision ?

« Je vais le dire clairement : si on part d’un échantillon de 116 individus, on aura la précision associée à un échantillon de 116 individus. On ne peut pas créer de l’information nouvelle à partir de rien. »

All TODOs for a "good" website: HTML requirements, well known URIs, etc...

Stop complaining, build as much as possible while you can do it for (almost) free, and enjoy the assets for the rest of your life. Code that you build today will still be usable and valuable in 10 years, and if you build stuff that let you reduce your expenses, you could retire early.

The rich text editor that gets out of your way. Zero framework lock-in, full control over every feature, and an architecture built for the real world.