AI agents' autonomous and probabilistic nature forces stricter security and authorization models, breaking traditional microservice assumptions.
Explores building AI Agents as streaming SQL queries using platforms like Apache Flink for improved consistency, scalability, and developer experience.
Explores building AI Agents as streaming SQL queries using platforms like Apache Flink for improved consistency, scalability, and developer experience.
Explores the challenges of delegating authority to AI agents due to fragmented user identities and ungoverned authorization systems in enterprises.
llm.codes converts JavaScript-heavy Apple and other developer docs into clean Markdown that AI agents can read, solving a key problem for AI-assisted coding.
An overview of Generative AI and an introduction to building AI agents using Python and the LangGraph library.
Introduces Peekaboo MCP, a macOS tool that enables AI agents to capture screenshots and perform visual question answering using local or cloud vision models.
Introducing hinbox, an AI-powered tool for extracting and organizing entities from historical documents to build structured research databases.
Explores common design patterns for building AI agents and workflows, discussing when to use them and how to implement core concepts.
Explains how Sampling and Prompts in the Model Context Protocol (MCP) enable smarter, safer, and more controlled AI agent workflows.
Explains how Tools in the Model Context Protocol (MCP) enable LLMs to execute actions like running commands or calling APIs, moving beyond just reading data.
Explains how the Model Context Protocol (MCP) uses 'Resources' to securely serve structured data from systems like files and databases to LLMs.
Explains the Model Context Protocol (MCP), an open standard for connecting AI agents and LLMs to external data sources and tools, enabling interoperability.
Explores AI agent frameworks, their benefits, limitations, and introduces the Model Context Protocol (MCP) for more modular AI systems.
Explores AI agents, their core components, differences from LLMs, and real-world applications, positioning them as the future of autonomous AI systems.
An explanation of the Model Context Protocol (MCP), an open standard for connecting LLMs to data and tools, and why it's important for AI development.
A tutorial on building a ReAct AI agent from scratch using Google's Gemini 2.5 Pro/Flash and the LangGraph framework for complex reasoning and tool use.
Explains the difference between Pass@k and Pass^k metrics for evaluating AI agent reliability, highlighting why consistency matters in production.
Explains the LLMs.txt file, a new standard for providing context and metadata to Large Language Models to improve accuracy and reduce hallucinations.
A developer shares talks on building safe AI agents for high-stakes industries using Go and durable execution, and announces an upcoming meetup.
