François Chollet

📣 Introducing RRE-1: A New Model for Retrieval Evaluation & Reranking for RAG based applications. Retrieval-Augmented Generation (RAG) has massively helped transform LLM-powered applications, but let’s face it—retrieval quality is still the weakest link in many pipelines. Poor retrieval is one of the major contributors of lower accuracy of your LLM applications. 🧐 That’s why we at AIMon built RRE-1, a powerful retrieval evaluation and reranker model designed to optimize ranking quality at scale. 🎯 👉 Why does this matter? Traditional retrieval methods like vector search are powerful, but they have limitations in effectiveness, especially when handling complex queries and domain specific knowledge. RRE-1 addresses this by offering: ✅ Offline evaluation of retrieval effectiveness, helping teams benchmark and refine their pipelines. ✅ Real-time low-latency reranking to ensure the most relevant context reaches the LLM. 👉 Who is this for? If you’re working on LLM agents, enterprise RAG systems, or AI copilots, this is for you. Our model ensures your retrieval pipeline is as optimized for your generative model—because great responses start with great retrieval. 🔎 Want to see it in action? Read more about AIMon RRE-1 in our blog post (link in comments). If you’re tackling retrieval challenges in your AI stack, we would love to chat! #AI #RAG #Retrieval #LLMs #MachineLearning

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Nuanced is a structured context layer for AI coding tools: call graphs, symbol maps, and static analysis that make LLMs reliable. We just launched our explainer video: 📽️ https://lnkd.in/eA8wmYeA If you’ve ever banged your head against Claude Code or Cursor, you know the pain: LLMs can write code, but they don’t understand it. They hallucinate helpers, invent imports, and break prod, because they can’t trace real structure across your repo. Nuanced gives them that structure. No more guesswork. Just deterministic edits, grounded in your codebase.

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🚀 Excited to announce: Portkey now integrates with Pydantic AI! We're integrated with many other agent platforms, but Pydantic is one that's getting a lot of love from our customers in the Python ecosystem. Pydantic AI is interesting because of 3 key things: 🛠️ It uses Python's native control flow instead of imposing new abstractions – no DSLs, just vanilla Python 🧩 Unique dependency injection system for passing context throughout agent workflows 🔄 Multi-agent delegation with full usage tracking across different LLMs If you've followed the drama on Twitter, you know this is a framework to evaluate. Our new Pydantic-AI integration transforms experimental agents into robust production systems — bringing the same ergonomic design and developer experience to GenAI that FastAPI brought to web development. 📊 Complete observability of every agent step, tool use, and interaction 🛡️ Built-in reliability with fallbacks, retries, and load balancing 💰 Track and optimize your AI spend across all models 🔌 Access 200+ LLMs through a single, unified interface 📝 Version-controlled prompts for consistent agent performance The magic happens when Python's type safety meets enterprise-grade AI infrastructure. Your agents stay strongly typed and validated while gaining production superpowers. I've seen teams transform their AI prototypes into scalable systems within days instead of months using this integration. The observability alone has helped identify bottlenecks we never knew existed. Check out the docs to get started: https://lnkd.in/gAH9eG_C Who's building with AI agents? I'd love to hear how you're solving the production challenges! #AIEngineering #MachineLearning #PydanticAI #AIInfrastructure #GenerativeAI

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"When AI models are open source, it encourages innovation. Developers, amateur hobbyists, and academic researchers can all access cutting-edge algorithms, models and tools without prohibitive costs." Read Yaron Inger's latest Techzine piece to understand why we at Lightricks decided to release LTX Video as an open source model:

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LLMs weren't built to think in APIs, and APIs weren't built for LLMs to use. This mismatch is why 90% of "AI agents" are still just chatbots. Here's the problem: When you give an LLM a datetime parameter, what's the cardinality? Infinite. When you ask it to call Salesforce's API directly, "get deals" doesn't exist—you need 4+ API calls just to get what a human thinks of as one action. The solution? Abstraction layers that reduce cardinality. Instead of infinite datetime options, give the LLM "tomorrow at 3pm PST." Instead of 4 Salesforce API calls, give it one "get_deals" function. At Arcade.dev, we've spent years building these abstractions. Our tools work because we give LLMs what they need: lower cardinality, clearer intent mapping, and error handling designed for probabilistic systems. Stop forcing square pegs into round holes. Build for how LLMs actually work.

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