The 100 who are shaping AI in Europe
Ines is featured among the top 100 individuals who are shaping Artificial Intelligence in Europe, compiled by French newspaper l’Opinion.
Les 100 qui font l'IA en Europe
Avec le cabinet Oliver Wyman, l'Opinion liste les 100 personnalités incontournables qui font l'intelligence artificielle sur le continent européen. Une sélection d'inventivité, de dynamisme et d'ambition. Tout est encore possible !
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research-llm-apis 2026-04-04
Release: research-llm-apis 2026-04-04 I'm working on a major change to my LLM Python library and CLI tool. LLM provides an abstraction layer over hundreds of different LLMs from dozens of different vendors thanks to its plugin system, and some of those vendors have grown new features over the past year which LLM's abstraction layer can't handle, such as server-side tool execution. To help design that new abstraction layer I had Claude Code read through the Python client libraries for Anthropic, OpenAI, Gemini and Mistral and use those to help craft curl commands to access the raw JSON for both streaming and non-streaming modes across a range of different scenarios. Both the scripts and the captured outputs now live in this new repo. Tags: llm , apis , json , llms
Harvard Proved Emotions Don't Make AI Smarter — That's Exactly Why You Need Soul Spec
The Myth Dies Hard "I'll tip you $200 if you get this right." "This is really important to my career." "I'm so frustrated — please help me." If you've spent any time on AI Twitter, you've seen people swear that emotional prompting makes LLMs perform better. A few anecdotal successes became gospel. The technique spread. Now Harvard has the data. It doesn't work. What the Research Actually Shows A team from Harvard and Bryn Mawr ( arXiv:2604.02236 , April 2026) ran a systematic study across 6 benchmarks, 6 emotions, 3 models (Qwen3-14B, Llama 3.3-70B, DeepSeek-V3.2), and multiple intensity levels. Finding 1: Fixed emotional prefixes have negligible effect. Adding "I'm angry about this" or "This makes me so happy" before your prompt? Across GSM8K, BIG-Bench Hard, MedQA, BoolQ, OpenBookQA, and
How AI Is Changing the Way We Build Online Businesses
Not long ago, building an online business meant: months of development hiring developers large upfront costs Today? AI has completely changed the game. Now, one person can go from idea → to revenue faster than ever before. And this shift is just getting started. ⚠️ The Old Way vs The New Way Before AI: Build everything from scratch Spend weeks on infrastructure Launch slowly Iterate even slower With AI: Build faster Automate key tasks Launch quickly Iterate in real time The difference is massive. 🧠 AI Is Reducing the Cost of Building One of the biggest changes: 👉 Building is no longer the bottleneck AI helps with: generating content writing code automating workflows handling repetitive tasks What used to take weeks… 👉 now takes days ⚙️ Infrastructure Is No Longer the Hard Part Another s
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Modern food systems may look stable on the surface, but they are increasingly dependent on digital systems that can quietly become a major point of failure. Today, food must be “recognized” by databases and automated platforms to be transported, sold, or even released, meaning that if systems go down, food can effectively become unusable—even when it’s physically available.
I Put an LLM Inside the Linux Kernel Scheduler. Here's What Happened.
A few weeks ago, I did something that probably shouldn't work. I replaced the CPU scheduling algorithm in my Linux kernel with calls to an AI model. As on-device LLM inference capabilities grow, I am curious about its potential as a CPU scheduler. Maybe in the future, tweaking a laptop's performance is a matter of adjusting the system prompt 🤷♂️ What Is a CPU Scheduler? CPU Scheduler is an operating system component that decides which task or process gets to use the CPU at a particular time. Linux's default scheduler is called CFS (Completely Fair Scheduler). It's an algorithm that tries to give every process a fair share of CPU time, weighted by priority. It makes decisions in microseconds, fully algorithmic. The Idea Two things that made this feel worth trying. First, sched_ext landed
I Turned My MacBook's Notch Into a Control Center for AI Coding Agents
Every developer using Claude Code knows the pain: you have 5+ terminal sessions running, Claude is asking for permission in one tab, waiting for input in another, and you're buried in a third. You Alt-Tab frantically, lose context, and waste time. So I built CodeIsland — a free, open-source macOS app that turns your MacBook's notch (Dynamic Island) into a real-time dashboard for all your AI coding agents. The Problem When you're running multiple Claude Code sessions across different projects, there's no way to see everything at a glance. You're constantly switching between terminals to: Check which session finished Approve permission requests Answer Claude's questions Monitor usage limits Multiple Claude Code sessions in cmux, with CodeIsland monitoring everything from the notch The Soluti
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