Latest Articles from the world of Artificial Intelligence
13 May 2026
Alexa? No, Gina! My Self-Built, Local Voice Assistant
It all started almost banally, with that kind of intellectual itch that pushes you to take objects apart to understand their mechanism. For years, we have lived with commercial voice assistants: Alexa on the nightstand, Google Assistant on the phone, a few Siris scattered in between. Honestly, I don't use them, but observing others, there has always been a nagging feeling difficult to ignore—the sense that every conversation ends up somewhere far away, on unknown servers, managed by opaque companies.
11 May 2026
European Tech Map: The Guide to European Digital Sovereignty
Early explorers venturing into unknown territories often relied on rough maps, sometimes completely invented. Not because the land beneath their feet was unreal, but because no one had ever truly mapped it. It's a metaphor that fits the European tech ecosystem of 2025 with surprising precision: the infrastructure is there, the companies exist, the products work. The problem is that no one knows where they are.
08 May 2026
Autonomous Agents: 9 Seconds to Delete Everything, What the PocketOS Disaster Teaches Us
It was about nine o'clock on Saturday morning when PocketOS customers discovered that their reservations no longer existed. Not in the sense that the system was slow, or that there was a temporary error: the data was gone. Reservations, payments, vehicle tracking—everything a small car rental startup builds in months of work, deleted in nine seconds by a single GraphQL API call to the infrastructure provider Railway.
06 May 2026
100 Million Tokens: Memory or Mirage?
Let's try to reason through images. One hundred million tokens is not a number that the human brain can spontaneously visualize, so we need an anchor. A token, in the language of language models, corresponds approximately to three-quarters of an English word. Ten thousand tokens are roughly seventy-five pages of text. One million tokens represents the entire *Recherche by Proust, that seven-volume work that almost everyone pretends to have read. Ten million tokens cover the entire works of Shakespeare plus the Bible plus a few medium-sized encyclopedias. One hundred million tokens are something in the order of a small university library, say sixty thousand average-length novels loaded into a single active context, simultaneously available for a language model to answer questions.*
04 May 2026
Inside Claude Code: It’s the System That Counts, Not the Model
There is a precise moment when an assistant stops answering and starts acting. It’s not just a matter of intelligence, or at least not only: it’s a matter of architecture. Classic chatbots function like sophisticated jukeboxes: they receive a request and return an output. Coding agents like Claude Code do something fundamentally different: they open files, execute commands, read the output, fix errors, and repeat—all on their own, until the task is finished or someone stops them. This leap from auto-completion to autonomy is not cosmetic. It requires an infrastructure that chatbots never needed to build.
01 May 2026
More Agents, Less Intelligence? Stanford Questions Multi-Agent Architecture
There's a cult scene in "Primer," Shane Carruth's low-budget sci-fi film, where two engineers build a time machine in their home garage, convinced that the more components they add, the better it will work. Then they discover, in the most painful way possible, that complexity is not synonymous with power: it's just complexity. The artificial intelligence industry is currently going through a similar philosophical crisis, albeit decidedly less temporal, regarding multi-agent systems. And a paper, published by two Stanford researchers in April 2026, has the merit of putting its finger exactly on the wound.
29 April 2026
Stanford AI Index Report 2026: AI Accelerates, Governance Slows Down
Fifty-three percent global adoption in three years, faster than the internet and the personal computer. Eighty-eight percent of organizations claiming to use AI. A programming benchmark, SWE-bench Verified, jumping from 60% to nearly 100% in twelve months. Private investment in the US at $285.9 billion, twenty-three times that of China. A fifty-percentage-point gap between what experts expect from AI and what the public thinks. These five numbers frame the perimeter of the 2026 AI Index Report from Stanford HAI: the ninth edition of a document that serves as a ruthless mirror of a sector accelerating much faster than anyone can measure, regulate, or socially absorb it.
27 April 2026
Caveman: Why AI Talking Like a Caveman is Worth It
Yabba-dabba-doo! Remember Fred Flintstone? He lived in Bedrock, drove a car powered by bare feet, had a dinosaur as a crane, and a pterodactyl as a record player. Yet, looking closely, that Stone Age civilization already possessed everything needed for a comfortable modern life: functioning gadgets, efficient technology, practical solutions. It just lacked the shiny veneer of progress. Perhaps we should have realized then that the essentials are enough, and that adding complexity doesn't necessarily mean adding value.
24 April 2026
Learning memory: Karpathy challenges RAG with an evolutionary knowledge base
There is a moment that anyone who has worked intensely with a language model knows well: the reset. You are building something complex, perhaps an elaborate software architecture or research weaving together dozens of sources, and the model has understood everything, keeps the thread, responds with surgical precision. Then the session ends, or you reach the context limit, and the AI forgets everything. It starts from zero. You have to explain again who you are, what you are doing, what decisions you made together. It’s like Christopher Nolan's film "Memento," where the protagonist must tattoo information on his body because short-term memory doesn't work: brutal, redundant, and deeply frustrating.
20 April 2026
The Code That Isn't Written: CodeSpeak and the Specification Revolution
There are names in the programming world that carry a specific weight. Andrey Breslav is one of them. If millions of Android developers write code in Kotlin today instead of Java, it is largely thanks to him: Breslav is the lead designer of the language that JetBrains launched in 2011 and that Google officially adopted as the preferred language for Android in 2017, during the Google I/O that changed the mobile ecosystem forever. He is not an academic theorizing from a chair: he is one of those who built tools used every day by hundreds of thousands of people in the real world, with all the compromises, production bugs, and pressures that this entails.
20 April 2026
Gemma 4 locally: 26 billion on my PC
There is a particular satisfaction in running something that it would be recommended not to download. Not the satisfaction of the hacker who forks a system, that's different stuff, but that quieter and more artisanal one of those who tighten the screws a bit beyond the recommended torque and discover that the structure holds anyway. It's the kind of satisfaction I found this week, while Gemma 4 26B ran on my consumer PC with a fluidity I didn't expect.
15 April 2026
10 rules for using AI in business
Let's start with a fact that serves as a mirror. According to the McKinsey State of AI report of November 2025, 88% of organizations already use AI in at least one business function. Yet, in the same period, the World Economic Forum and Accenture estimated that less than 1% of these have fully operationalized a responsible AI approach, while 81% remain in the most embryonic stages of governance maturity. The paradox is served: almost everyone uses AI, almost no one really governs it.
15 April 2026
Project Glasswing: Claude Mythos and the mysterious model
Anthropic presents a security initiative to defend critical software in the era of artificial intelligence. At the center is Claude Mythos Preview, the most powerful model ever developed by the company, capable of finding vulnerabilities that humans haven't found in thirty years. The paradox is that you won't be able to use it.
13 April 2026
TurboQuant: One bit to redefine the limits of artificial intelligence
At the end of April 2025, four researchers from Google Research and New York University published a paper on arXiv with a sober title: *TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate. For months, almost no one talked about it outside of academic circles. Then, in March 2026, Google published a post on the official blog announcing TurboQuant as a breakthrough in the efficiency of language models, with acceptance at ICLR 2026, and within forty-eight hours the paper appeared on every tech feed. Announcements of compressions over five times higher without loss of quality, enthusiastic headlines everywhere. A one-year delay, a wave of hype.*
10 April 2026
MIT and “Humble” AI: How to teach models to say “I don't know”
There is a thought experiment that MIT researchers use to explain the problem at the heart of their research. Imagine an intensive care physician at three in the morning, after a twelve-hour shift. An AI-generated diagnosis appears on the monitor: bacterial pneumonia, 94% probability. The doctor has a doubt, a gut feeling that something isn't right. But the number is there, precise, authoritative. And the doctor gives in.