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Welcome to my blog! I'm a seasoned Software Engineer with over 20 years of experience in the tech industry. My passion lies in writing about the latest advancements in technology and sharing my insights through public speaking. I envision a world where technology is accessible to everyone and understood as deeply as by those who create it. Join me on this journey as we explore the fascinating world of technology together.
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Top LLM Cost Optimization Techniques Every Engineer Should Know
Building an AI feature has never been easier. A working prototype takes an afternoon: pick a frontier model, write a prompt, wire up a few tools, ship it. Then the invoice arrives. The prototype that cost a few dollars a day in testing now serves thousands of users. Every request carries a 6,000-token system prompt.…
Chatbot ou agent IA : où se situe vraiment votre système sur l’échelle d’autonomie
Chatbot ou agent IA Si vous avez assisté cette année à une démo d’un éditeur IA dans une banque, vous avez sans doute vu la slide : « Découvrez votre premier agent IA ». Puis la démo commence. L’« agent » répond à trois questions sur les frais de tenue de compte à partir d’un…
Chatbot vs AI Agent: Where Your System Really Sits on the Autonomy Spectrum
Chatbot vs AI Agent If you have sat through an AI vendor demo in a bank this year, you have probably seen the slide. “Meet your first AI agent.” Then the demo starts, and the agent answers three questions about account fees from a product PDF and offers a link to reset a password. It’s…
Arrêtez de récupérer, commencez à compiler : le pattern LLM Wiki
Le RAG est un interpréteur, le LLM Wiki est un compilateur Le RAG nous a habitués à une drôle de pratique : chaque question repaie intégralement le prix de la compréhension du corpus. Le pattern LLM Wiki refuse de payer ce prix au moment de la requête. Il déplace la synthèse vers l’ingestion : un…
Stop retrieving, start compiling: the LLM Wiki pattern
RAG is an interpreter. The LLM Wiki is a compiler RAG trained us to accept a strange habit: every question pays the full price of understanding the corpus again. The LLM Wiki pattern refuses to pay that price at query time. It moves synthesis to ingestion, where an agent reads each source once, writes what…