RT Logan Kilpatrick (@OfficialLoganK)Introducing the File Search Tool in the Gemini API, our hosted RAG solution with free storage and free query time embeddings 💾We are super excited about this new approach and think it will dramatically simplify the path to context aware AI systems, more details in 🧵 ...

RT Avi Chawla (@_avichawla)6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/SvYt7PiJQxAvi Chawla (@_avichawla):6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

1️⃣ Sim AIA drag-and-drop UI to build AI agent workflows!Sim AI is a lightweight, user-friendly platform that makes creating AI agent workflows accessible to everyone.Supports all major LLMs, MCP servers, vectorDBs, etc.100% open-source with 18k starsThe video below depicts a finance assistance app created with Sim and connected to Telegram in minutes.The Finance Agent uses Firecrawl for web searches and accesses stock data via Alpha Vantage's API through MCP servers.🔗 https://t.co/z4LPCV4SwH2️⃣ Transformer ...

6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

2025年6月，Shopify CEO Tobi Lütke 和 AI 大神 Andrej Karpathy 在 X 上提出了一个新概念——上下文工程。Karpathy 将其定义为"一门微 妙的艺术与科学，旨在填入恰到好处的信息，为下一步推理做准备。" 本文将以这篇论文为基础，系统性地回答三个核心问题：上下文工程到底是什么？它的基础构件是什么？未来会如何发展？ 01 上下文工程是什么？一门关于熵减的古老学科 要理解上下文工程，必须先回答：为什么人与机器的交流如此困难？ 然而，这个新概念与提示词工程有什么不同？为什么它会和 RAG、MCP 等技术扯上关系？过往的回答大多从技术角度出发，试图拆解 上下文都包括什么，如何让它能够发挥最好的效果。 10月30日，上海交通大学和 GAIR 实验室发表了论文《上下文工程 2.0：上下文工程的上下文》，用一种更全面的视角定义了这个新兴学 科。它不再把人机交互视为技巧，而是回归到了交流动力学的基础逻辑。 论文认为，这是因为人类与机器之间，存在一道认知鸿沟。 人类的交流是高熵的，他们的表达无序、混乱、充满隐含信息。当我对同事说"帮我搞定那个报告"，他需要记忆中的"那个报告"指什 ...

Microsoft.Google.AWS.Everyone's solving the same problem for Agents:How to build a real-time context layer for Agents across dozens of data sources?Airweave is an open-source context retrieval layer that solves this!Learn how this layer differs from RAG below: https://t.co/hqycgs5eoqAvi Chawla (@_avichawla):You are in an AI engineer interview at Google.The interviewer asks:"Our data is spread across several sources (Gmail, Drive, etc.)How would you build a unified query engine over it?"You: "I'll embed ever ...

You are in an AI engineer interview at Google.The interviewer asks:"Our data is spread across several sources (Gmail, Drive, etc.)How would you build a unified query engine over it?"You: "I'll embed everything in a vector DB and do RAG."Interview over!Here's what you missed:Devs treat context retrieval like a weekend project.Their mental model is simple: "Just embed the data, store them in vector DB, and call it a day."This works beautifully for static sources.But the problem is that no real-world workflow ...

RT Avi Chawla (@_avichawla)You can now deploy any ML model, RAG, or Agent as an MCP server.And it takes just 10 lines of code.Here's a breakdown, with code (100% private): ...

Connecting AI models to different apps usually means writing custom code for each one.For instance, if you want to use a model in a Slack bot or in a dashboard, you'd typically need to write separate integration code for each app.Let's learn how to simplify this via MCPs.We’ll use @LightningAI's LitServe, a popular open-source serving engine for AI models built on FastAPI.It integrates MCP via a dedicated /mcp endpoint.This means that any AI model, RAG, or agent can be deployed as an MCP server, accessible ...