AI-Ready API Importance - The industry emphasizes the growing need for "Agent-ready" APIs as every website transitions to this paradigm [1][2] - APIs must be well-documented for Agents to easily understand endpoints, parameters, data expectations, and error recovery [3] - Agents should not have to guess how an API works to avoid friction, inconsistency, and potential errors [5] AI Adoption and API Design - 89% of developers use AI daily, but only 24% design APIs with AI agents in mind, highlighting a significant gap [2] - Addressing this gap is crucial because Agents cannot navigate poorly documented or broken endpoints like human developers can [2] Postman's Solution - Postman's Agent Mode automatically generates detailed, contextual documentation for APIs based on request/response examples and usage patterns [4] - Postman's AI-powered platform allows users to explore, debug, and build APIs using natural language [4] - Postman facilitates the design, documentation, publishing, and monitoring of APIs that are reliably usable by both humans and agents [5] Future Implications - Agents will increasingly handle tasks such as making purchases, finding the best options, and filling out job applications [6] - Predictable structures, machine-readable metadata, and standardized behavior are essential for AI-ready APIs [7]

Overview - The document is Postman's Playbook [1]

Industry Trend - The industry is moving towards an era where every website must be "Agent-ready" [1] - Agents, not humans, will be making purchases in the future [1] - Agents will be responsible for finding the best options [1] Technological Advancement - Postman's latest Guidebook on building AI-ready APIs is highlighted as a crucial document for developers [1]

General Overview - The document is Postman's Playbook [1]

AI and API Development - The industry is entering an era where every website and API must be "Agent-ready" [1][2] - 89% of developers use AI daily, but only 24% design APIs with AI agents in mind [2] - APIs must be well-documented for Agents to easily learn about them, including endpoints, parameters, data, and error recovery [3] Postman's Solution - Postman's AI-ready APIs Playbook emphasizes the importance of machine-readable documentation that stays in sync with the schema [2] - Postman's Agent Mode automatically generates detailed, contextual documentation for APIs based on request/response examples and usage patterns [4] - Postman's platform allows for designing, documenting, publishing, and monitoring APIs that both humans and agents can use reliably [5] Future Implications - Agents will increasingly make purchases, find the best options, and fill out job applications, replacing human roles [6] - Predictable structures, machine-readable metadata, and standardized behavior are crucial for AI-ready APIs [7] - Agents using APIs should not be guessing; everything should be documented for both humans and machines [5]

LLM Engineering Skills - LLM Engineering 需要掌握 8 项关键技能 [1] - 行业提供免费/开源资源以掌握这些技能 [1]

That's a wrap!Over to you: What other LLM development skills would you add?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.Avi Chawla (@_avichawla):8 key skills to master LLM Engineering:(free/open-source resources below) https://t.co/kG8qQ6asAB ...

AI Engineering Skill - Context engineering is rapidly becoming a crucial skill for AI engineers [1] - It's about the systematic orchestration of context, not just clever prompting [1] Context Engineering Workflow - The demo provides more information about what context engineering actually means [1] - Let's build a context engineering workflow, step by step [1]

8 key skills to master LLM Engineering:(free/open-source resources below) https://t.co/kG8qQ6asAB ...

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]