Your pyramid is a lie: Here is the Capitalist Pyramid published in 1911 by the Industrial Workers of the World. It illustrates how the working class props up capitalist society. A century of propaganda has conditioned Americans to love the capitalist system that enslaves them and hate the socialist system that liberates us all.

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

📜 Le programme de cette audition publique : ➡️ Introduction par Raja Chatila, membre du Conseil scientifique de l’Office, professeur émérite ➡️ Progrès récents et pistes de développement de l’IA • Patrick Pérez, directeur général de Kyutai • Jean Ponce, professeur au Département d'informatique de l'ENS PSL et au Courant Institute de New York University, directeur associé pour la recherche de l'IA du Cluster PR[AI]RIE et président de la startup ENHANCE LAB • Jean-Frédéric Gerbeau, directeur général délégué à la science, INRIA • Laurent Simon, professeur des universités, titulaire de la chaire de la Fondation Bordeaux Université sur l’IA Digne de Confiance et membre du collège Représentation et Raisonnement de l’Association française pour l’intelligence artificielle ➡️ La diffusion des innovations dans les usages de l’IA •Alexis Bacot, directeur de projets IA, Direction générale des entreprises •Selma Souihel, adjointe au directeur du programme IA d’INRIA •Françoise Soulié, administratrice et membre du bureau du Hub France IA •Erwan de la Porte du Theil, président fondateur de l’Association nationale de l’intelligence artificielle •Arthur Grimonpont, responsable du plaidoyer, Centre pour la sécurité de l’IA ➡️ Carte blanche à Daniel Andler, membre du Conseil scientifique de l’Office, professeur émérite #DirectAN

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.