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Ça fait un moment que je construis des agents avec LangGraph, et je retombe toujours sur le même problème : chaque message envoyé au LLM peut contenir des données sensibles, et selon le fournisseur que vous utilisez, ce qu'il advient de ces données change complètement. En simplifiant, il y a trois f
The best thing about self-hosted LLMs is that you can choose from hundreds of models
Why We Built a Statically Typed Functional Language for LLMs to Write ll-lang is a language for one narrow, practical job: helping LLMs generate correct code faster by spending fewer tokens on syntax and getting compile-time feedback instead of runtime surprises. The problem with "AI coding" is not
A freelance gaming journalist's offers tips on ditching Chrome, Office, Gmail, Photoshop, and other AI-infested tools in favor of alternatives that just do the normal stuff. Read More
There’s a popular misconception that local LLMs are not useful for anything beyond passing “trust me, bro” benchmarks. In reality, they can be surprisingly effective when used for the right tasks with the right setup. I’ve been using them for a while to supervise my agents in TaskSquad, and they’ve
The LLM:Probabilistic Predictor The process: It views your query as a sequence of tokens, converts them into vectors, and uses Self-Attention to weigh the importance of those tokens. The outcome: It is always calculating probability. When it produces 2 as the answer to 1 + 1=, it isn't "adding"; it
Over the past few days, I've realized that there are a lot of folks out there using LLMs that haven't had an opportunity to dig, even a little, into the basics of how LLMs really work. And I guess that makes sense; for the most part, the average person doesn't have a lot of reason to know this. But
In the Agentic AI course Andrew recommends using Docker or another sandbox type software when running Agentic code. I am curious to know the following: Are there other applications that have been developed recently What type of security guardrails these applications use to prevent an AI Agent from r
LLM Planning, AI Arguments, and Building Persistent Worlds LLM planning is gaining focus, while new tools are emerging to address agent identity and trust. The conversation around AI capabilities is shifting towards more practical, modular approaches, and the potential for AI to be integrated deeply
## Most early LLM apps start the same way: “Let’s just put everything into one prompt and let the model handle it.” So we write a prompt that tries to: validate input transform data generate output summarize add reasoning handle edge cases …and somehow do it all in one call. It works—until it doesn’
LLM agents fail in four predictable, mechanism-level ways. Attention decay, reasoning decay, sycophantic collapse, hallucination drift. The current stack (prompting, fine-tuning, RAG, agent loops) cannot close them because each layer operates inside the same decaying chain. The fix is an external la
The stochastic challenge Traditional software is predictable: Input A plus function B always equals output C. This determinism allows engineers to develop robust tests. On the other hand, generative AI is stochastic and unpredictable. The exact same prompt often yields different results on Monday ve
The provider dashboards show you one number — your total bill. That's like getting an electricity bill with no breakdown. You just see the total and hope nobody left the AC on. Tbh, if you look closely at your API logs, you are probably wasting around 43% of your budget. I spent the last few weeks a
Steve Ballmer wrote a fiery letter in the sentencing of disgraced founder Joseph Sanberg documenting all the harm that's befalling him as an investor.
Beyond Brute-Force Aesthetics | Full Launch Validation of the Minimal MVP for Modular Brain-Inspired Decoupled Large Language Models Preface Current all-in-one large models centered on the Transformer architecture have long fallen into a vicious cycle of mindless parameter stacking. Trillion-scale p
RTX 4090 Cooling, LLM KV Cache Quantization, & Deepseek V4 Flash Models Today's Highlights Today's highlights include a deep dive into optimal GPU cooling solutions for the RTX 4090, alongside advanced VRAM optimization techniques for LLMs through KV cache quantization. Additionally, new Deepseek V4
Agentic AI & LLM-Powered Workflows Transform Development Today's Highlights This week, we explore how AI is revolutionizing development, from enabling rapid game creation to serving as a daily coding assistant for engineers. We also dive into the rising trend of agentic AI and its impact on automati
If you've tried building anything serious on top of large language models (LLMs) recently, you've probably run into this: "Thinking" is supposed to make models better. In practice, it makes your infrastructure worse. This isn't a model problem—it's an infrastructure and abstraction problem. And it's
Why token counting isn't a solved problem when building across providers Building AI products that span multiple LLM providers involves a challenge most developers don't anticipate until they hit it: context windows are not interoperable. On the surface, managing context in a multi-LLM system seems
LLMs generate text by predicting the next best token. Pass the same prompt twice and you might get two different outputs. Sometimes it's a clean JSON object. Sometimes it's the same data wrapped in markdown fences and a paragraph of explanation you didn't ask for. Often, you don't need the full resp
