AI Agent Fundamentals - The report covers AI Agent fundamentals [1] - It differentiates LLM, RAG, and Agents [1] - Agentic design patterns are included [1] - Building blocks of Agents are discussed [1] AI Agent Development - The report details building custom tools via MCP (likely meaning "Minimum Complete Product" or similar) [1] - It provides 12 hands-on projects for AI Engineers [1]

AI Agent Fundamentals - The document covers agent fundamentals, providing foundational knowledge for understanding AI agents [1] - It differentiates LLM, RAG, and Agents, clarifying their roles and relationships in AI systems [1] - Agentic design patterns are explored, offering insights into structuring and organizing AI agents [1] - Building blocks of agents are outlined, detailing the essential components for constructing AI agents [1] Practical Applications - The document includes 12 hands-on projects for AI Engineers, providing practical experience in building AI agents [1] - It covers building custom tools via MCP (likely referring to a specific methodology or platform), enabling customization and extension of AI agent capabilities [1] Resource Availability - A PDF containing all AI Agents posts is available for download, offering a consolidated resource for learning about AI agents [1]

It's FREE, access it here:https://t.co/JwCdUAC7yY ...

AI Agent Fundamentals - The document covers AI Agent fundamentals [1] - It compares LLM, RAG, and Agents [1] - It discusses Agentic design patterns [1] - It outlines the Building Blocks of Agents [1] AI Agent Development - The document details building custom tools via MCP [1] - It includes 12 hands-on projects for AI Engineers [1]

技术趋势 - OpenAI gpt-oss (100% locally) 的微调是行业关注点 [1]

General Overview - The document is a wrap-up message encouraging readers to reshare the content if they found it insightful [1] - It promotes tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) [1] Call to Action - The author, Avi Chawla (@_avichawla), invites readers to find him for more content [1] Specific Topic - The document mentions fine-tuning OpenAI gpt-oss (100% locally) [1]

Model Fine-tuning - Fine-tuning enables the LLM to generate reasoning tokens in French before the final English response [1] - The video demonstrates the LLM's behavior before and after fine-tuning [1]

Technology & AI - The document discusses fine-tuning OpenAI gpt-oss (100% locally) [1]

RT Avi Chawla (@_avichawla)Build human-like memory for your Agents (open-source)!Every agentic and RAG system struggles with real-time knowledge updates and fast data retrieval.Zep solves these issues with its continuously evolving and temporally-aware Knowledge Graph.Like humans, Zep organizes an Agent's memories into episodes, extracts entities and their relationships from these episodes, and stores them in a knowledge graph:(refer to the image below as you read)1) Episode Subgraph: Captures raw data with ...

Agentic System Challenges - Agentic 和 RAG 系统在实时知识更新和快速数据检索方面面临挑战 [1] Zep's Solution - Zep 通过其不断发展和时间感知的知识图谱来解决这些问题 [1] - Zep 像人类一样组织信息 [1]