Hugh

Level 43 +++++

[Hugh @hourdays]

Gravity is responsible for people falling in Love after all ;-) https://t.co/dQKhlvzzlb #weightless by @jbchandelier

[Jason Burne @Monty_Brogan69]

M'Bappé quand il va revenir auprès d'Ester après avoir passé 3 semaines aux Etats-Unis :

[Spencer Baggins @bigaiguy]

A French engineer who lives quietly in Paris has spent 30 years writing software that the entire internet now runs on without knowing his name.

He wrote the code that streams every YouTube video, every Netflix show, every TikTok clip. He wrote the code that runs the virtual servers underneath AWS, Google Cloud, and Microsoft Azure. He calculated more digits of pi than anyone in history. He has no Twitter. He has no marketing. He just keeps shipping.

His name is Fabrice Bellard.

Here is the story, because almost nobody outside the systems programming world knows what one man has built.

Fabrice was born in 1972 in Grenoble, France. He studied at École Polytechnique, the top French engineering school. He never went to Silicon Valley. He never built a startup empire. He just wrote code.

In 2000 he started a project called FFmpeg, an open-source multimedia framework for encoding, decoding, and streaming video. He was 28. The project did one thing nobody else had done well. It handled every video and audio format that existed, in one library, on every operating system. He led it himself for years.

Today FFmpeg is the invisible engine of the internet. YouTube uses it. Netflix uses it. VLC uses it. Chrome and Firefox use parts of it. Every Android phone, every iPhone, every smart TV, every video editing tool you have ever touched runs FFmpeg somewhere underneath. If you have watched a video on a screen in the last 20 years, Fabrice's code processed it.

He was not done.

In 2003 he started QEMU, a machine emulator and virtualizer. He wrote it solo until version 0.7.1 in 2005. QEMU lets you run any operating system on any other operating system. It became the foundation of modern virtualization. KVM, the Linux kernel hypervisor, runs on top of QEMU. Every major cloud provider, AWS, Google Cloud, Microsoft Azure, IBM Cloud, runs virtual machines on infrastructure built around it. The Quick Emulator is the most cited piece of cloud infrastructure code on Earth.

He kept going.

In 2001 he won the International Obfuscated C Code Contest with a small C compiler that grew into TCC, the Tiny C Compiler. TCC can compile and boot a Linux kernel from source in under 15 seconds. In 2004 he calculated the most digits of pi ever computed at the time, using a personal desktop computer and an algorithm he derived himself called Bellard's formula. In 2011 he wrote a complete PC emulator in pure JavaScript that runs Linux in your browser, a project called JSLinux that engineers still cannot believe is real.

In 2019 he released QuickJS, a small but complete JavaScript engine that fits where V8 cannot. In 2021 he released NNCP, a neural network based lossless data compressor that immediately took the lead on the Large Text Compression Benchmark.

Then he turned his attention to large language models. He built TextSynth Server, a web server with a REST API for running LLMs locally. He released ts_zip and ts_sms, compression utilities that use language models to compress text and short messages at ratios traditional algorithms cannot reach. He released TSAC, a very low bitrate audio compression system. In December 2025 he released Micro QuickJS, a new JavaScript engine for microcontrollers, separate from QuickJS, designed for environments with almost no memory.

Fabrice co-founded a telecom company called Amarisoft in 2012, where he serves as CTO. Amarisoft builds 4G and 5G base station software used by carriers and labs around the world. He has been running it for over a decade while continuing to ship personal projects from his own home page at bellard dot org

He has no Twitter. He has no Instagram. He gives almost no interviews. His personal website is a flat list of projects with no styling, no fonts, no marketing copy. Just titles and links.

A quiet French engineer who never moved to Silicon Valley wrote the code that quietly runs the internet.

He is still shipping.

[Hugh @hourdays]

@jackccrawford @perrymetzger Indeed

[O’Shea Jackson Jr @OsheaJacksonJr]

I saw it. Now you have to see it. His name is Samuel. And he…is a king.

[Dalen Xara @DalenXara]

@AKBrews Yup 😂

[Hugh @hourdays]

@Eurostar internet connexion sucks so so so bad in your trains it hurts, really hurts, such a crazy loss of time, crazy

[Mario Nawfal @MarioNawfal]

The CEO of Take-Two, the company behind GTA, just said something the entire AI industry doesn't want to hear.

And he said it without being anti-AI.

Strauss Zelnick's argument is precise. AI is built on datasets. Datasets are backward-looking. Creativity is forward-looking. A model trained on everything that already exists cannot, by definition, produce something genuinely unexpected. And all hits, by their very nature, are unexpected.

Asset creation and hit creation are not the same thing. AI is getting very good at the first one. The second one is what actually makes money, builds franchises, and changes culture. Nobody has shown AI can do that yet.

The derivative property problem is real. You can clone GTA with existing technology. You could do it before AI. It would take 3 years and look identical. It still wouldn't sell. Because it isn't GTA. It's a clone of GTA.

And consumers, despite what the industry occasionally pretends, can feel the difference between something genuinely new and something assembled from the residue of things that already worked.

Thousands of mobile games ship every year. 0 to 5 hits get made. The same studios make them every time. The technology to make more games has been commoditized for years. It didn't democratize hit creation. It just flooded the market with more forgettable product.

The Silicon Valley thesis that AI unlocks game creation for everyone is true in the same way that cheap cameras unlocked filmmaking for everyone. They did. And the same 5 studios still make the movies everyone watches.

What Zelnick is saying, without quite saying it, is that the thing AI cannot replicate is taste. The instinct for what hasn't been done yet. The cultural antenna that detects the gap in the market before the data can see it.

Data tells you what people wanted. Hits tell people what they want next.

Those are different jobs.

[Hugh @hourdays]

@Fabien_Mikol Cf. https://t.co/14vyIB2GHO

[Mo @atmoio]

The Unethical Guide to Surviving AI Layoffs

[Hugh @hourdays]

@elonmusk Yes but to compensate we use the Force

[Dr Thomas NΞNNINGΞR @T_Nenninger]

Grand merci à .@romainsimon pour son set de skills pour l’administration française.
J’ai tweeké le skill pour l’adapter à ma situation. Mais bordel, ça fonctionne😅👍

Le mur des cerfas imbitables (à dessein) de l’administration française s’est effondré face à l’ingeniosité d’un français monté sur son fidèle destrier IA.

[Brian Roemmele @BrianRoemmele]

The two people you must make proud of you in your lifetime.

[Genesis AI @gs_ai_]

We are back. After one year of quiet building.

Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability.

For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans.

Solving it means rethinking the whole stack from the ground up:

- A robotics-native foundation model.
- A 1:1 human-like robotic hand.
- A noninvasive data collection glove for motion, force, and touch.
- A simulator that turns weeks of experiments into minutes.

GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm.

Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on)

We are approaching the endgame for robotics.

And this is just a beginning.

[Hugh @hourdays]

@Raclure03 je me demande ce que ça donne x5

[shouko @shoukointech]

GitHub's former CEO gave his AI eyes.
What happened next is equal parts hilarious and haunting

[Hugh @hourdays]

@CaptainHaHaa @runwayml Love this

[Hugh @hourdays]

The language shapes the build.

[Souveraine Tech @SouveraineTech]

L’Index Thomisticus : comment un jésuite italien a inventé le traitement automatique du langage

En 1949, le père Roberto Busa, jésuite italien passionné de saint Thomas d’Aquin, réalise un pari apparemment impossible. Il convainc Thomas J. Watson Sr., fondateur d’IBM, de financer la numérisation intégrale de l’œuvre complète du Docteur angélique : plus de 11 millions de mots en latin.

À l’époque, les ordinateurs ne servent qu’à calculer des chiffres. Busa, lui, veut créer une "concordance lemmatisée" exhaustive : non pas simplement scanner les textes, mais les analyser linguistiquement, repérer chaque occurrence d’un mot sous toutes ses formes (lemme), et permettre des recherches contextuelles ultra-précises. Watson hésite. Un rapport interne d’IBM juge le projet irréalisable. Busa rétorque avec le célèbre slogan de la compagnie : « The difficult we do right away ; the impossible takes a little longer. » (« Ce qui est difficile, on s'y attaque tout de suite ; ce qui est impossible prend un peu plus de temps. » )

Watson accepte. Le projet "Index Thomisticus" est lancé.

Pendant trente ans, des équipes de religieuses, d’universitaires et d’ingénieurs IBM perforent des millions de cartes, puis passent aux bandes magnétiques. En 1974 paraissent les premiers volumes imprimés ; en 1980, le projet est achevé sous forme de 56 volumes et, surtout, d’une base de données électronique révolutionnaire.

Ce qui paraissait une simple indexation philologique devient bien plus : la naissance de la "linguistique computationnelle". Pour la première fois, une machine traite du langage naturel à grande échelle. Busa pose les bases techniques et conceptuelles du traitement automatique du langage naturel (TALN/NLP) que nous utilisons aujourd’hui.

Sans lui, pas de moteurs de recherche sémantiques, pas d’analyse de corpus massive, et, par filiation directe, pas de grands modèles de langage comme ceux qui alimentent Mistral, Claude, ChatGPT ou Grok.

Ce projet, qui dura trente ans, inventa de toutes pièces la linguistique computationnelle — ancêtre direct du traitement automatique du langage naturel et, par filiation, des grands modèles de langage actuels.

Le père Busa, mort en 2011 à 97 ans, est aujourd’hui reconnu comme l’un des pionniers des "digital humanities". Son œuvre montre que les humanités classiques et l’informatique ne s’opposent pas : elles se fécondent mutuellement.

L’Index Thomisticus reste un monument discret mais fondateur de notre ère numérique. Un jésuite et un milliardaire américain ont, ensemble, écrit la première page de l’histoire des IA linguistiques.

https://t.co/aCCB3V50C9

[Hugh @hourdays]

@UiSavior Interest = lead? > lead = feedback? feedback = training? Training = predictability?

[Hugh @hourdays]

@UiSavior Both? Flip to reveal?

[Hugh @hourdays]

@UiSavior action = interest?