AI Agent Infrastructure - The industry is focused on building a real-time context layer for AI Agents across numerous data sources [2] - Airweave offers an open-source context retrieval layer as a solution [2] Technical Challenges & Solutions - Companies face the challenge of querying data spread across multiple sources like Gmail and Drive [2] - A common but potentially insufficient solution is embedding everything in a vector database and using RAG (Retrieval-Augmented Generation) [2]

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 ...

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): ...

AI Engineering Projects - The industry highlights 9 real-world MCP (presumably Machine Comprehension and Planning) projects for AI engineers [1] - These projects are accessible via a GitHub repository [1] Project Types - The projects cover areas like RAG (Retrieval-Augmented Generation), Memory, MCP client, Voice Agent, and Agentic RAG [1] - The "and much more!" suggests the repository contains additional project types beyond those explicitly listed [1]

9 real-world MCP projects for AI engineers covering:- RAG- Memory- MCP client- Voice Agent- Agentic RAG- and much more!Find them in the GitHub repo below. https://t.co/oXp4PmxvYB ...

AI Engineering Fundamentals - Industry emphasizes the importance of coding fundamentals, including Python, Bash, Git, and testing as a starting point for AI engineers [4] - Focus on understanding and utilizing LLM APIs for structured outputs, caching, and system prompts [4] - Industry highlights the necessity of augmenting LLMs with additional information through fine-tuning, RAG (Retrieval-Augmented Generation), and prompt/context engineering [4] Retrieval and RAG Techniques - Industry stresses the significance of retrieval techniques, including vector databases, hybrid retrieval, and indexing strategies, for providing context to LLMs [4] - Industry focuses on building retrieval and generation pipelines, reranking, and multi-step retrieval using orchestration frameworks [2] - After solid retrieval, industry moves into RAG (Retrieval-Augmented Generation) [4] AI Agents and Production Deployment - Industry explores AI Agents, focusing on memory, multi-agent systems, human-in-the-loop design, and agentic patterns [4] - Industry emphasizes shipping AI systems in production with infrastructure, including CI/CD, containers, model routing, Kubernetes, and deployment at scale [4] - Industry prioritizes observability, evaluation, and security, including guardrails, sandboxing, prompt injection defenses, and ethical guidelines [3][4] Advanced AI Workflows - Industry explores advanced workflows, including voice & vision agents, CLI agents, robotics, agent swarms, and self-refining AI systems [4]

Core Insights - The article discusses the decision-making process for individuals in the large model field regarding whether to pursue a PhD or engage in entrepreneurial ventures related to agents [1][2] Group 1: Importance of Foundation in Large Models - A solid foundation in large models is crucial, as the field encompasses various directions such as generative models, multi-modal models, fine-tuning, and reinforcement learning [1] - Many mentors lack sufficient expertise in large models, leading to a misconception among students about their readiness for related positions [1] Group 2: Role of a Pioneer in Research - The suitability of an individual to take on the role of a "pioneer" in research is essential, especially in a field with many unexplored directions [2] - The ability to independently explore and endure failures is emphasized as a key trait for those aiming to innovate from scratch [2] Group 3: Community and Learning Resources - The "Large Model Heart Tech Knowledge Planet" community offers a comprehensive platform for beginners and advanced learners, featuring videos, articles, learning paths, and Q&A sections [2] - The community aims to provide a space for technical exchange and collaboration among peers in the large model domain [4] Group 4: Learning Pathways - The community has compiled detailed learning pathways for various aspects of large models, including RAG, AI Agents, and multi-modal training [4][9] - Each learning pathway includes clear technical summaries, making it suitable for systematic learning [4] Group 5: Benefits of Joining the Community - Members gain access to the latest academic advancements and industrial applications related to large models [7] - The community facilitates networking with industry leaders and provides job recommendations in the large model sector [7][68] Group 6: Future Plans and Engagement - The community plans to host live sessions with industry experts, allowing for repeated viewing of valuable content [65] - A focus on building a professional exchange community with contributions from over 40 experts from renowned institutions and companies is highlighted [66]

Core Insights - The focus of academia and industry has shifted towards VLA (Vision-Language Action) in autonomous driving, which provides human-like reasoning capabilities for vehicle decision-making [1][4] - Traditional methods in perception and lane detection have matured, leading to decreased attention in these areas, while VLA is now a critical area for development among major autonomous driving companies [4][6] Summary by Sections Introduction to VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for improving the reliability and safety of autonomous driving [1][4] Course Overview - A comprehensive course on autonomous driving VLA has been designed, covering foundational principles to practical applications, including cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6][12] Course Structure - The course consists of six chapters, starting with an introduction to VLA algorithms, followed by foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [13] - Chapter 2 focuses on the foundational knowledge of Vision, Language, and Action modules, including the deployment of large models [14] - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [15] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [16] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action generation [17][19] Learning Outcomes - The course aims to deepen understanding of VLA's current advancements, core algorithms, and applications in projects, benefiting participants in internships and job placements [24]