A developer's guide to building an 'AI in a Box' solution using Azure Foundry, Azure Functions, and Static Web Apps, grounded in a personal project.
A guide to the Model Context Protocol (MCP) for AI agents, explaining its core capabilities using a story-driven example.
Introducing Code Sandbox MCP, a Model Context Protocol server for safely executing Python and JavaScript code in containers via AI agents.
A guide to automating the fine-tuning of AI agent workflows using the Qodo CLI, based on analyzing execution logs to improve instructions.
Analyzes key enterprise challenges in adopting MCP servers for AI agents, focusing on security, governance, and authorization complexities.
A technical guide on building a contextual fitness AI agent using LangChain.js, covering architecture, challenges, and implementation details.
Argues that reading raw AI input/output data is essential for developing true intuition about system behavior, beyond just metrics.
A guide to using Langchainjs for coordinating AI agent tool and function calls with chain-of-thought reasoning, including a practical code example.
A developer reflects on the first month of VibeTunnel, a terminal app for running AI agents, detailing rapid growth, key technical milestones, and lessons learned.
Introduces Graphiti, an open-source framework for building bi-temporal knowledge graphs to give AI agents long-term memory and real-time data understanding.
Explores how developers must now design tools for both human users and AI agents, discussing the rise of AI experience (AIEx) alongside developer experience (DevEx).
Explores the critical but underdeveloped components of Agent-to-Agent (A2A) protocols: dynamic discovery, naming, and resolution for scalable AI agent ecosystems.
A developer documents how AI agents built a complete shopping cart feature for an e-commerce app, from requirements to code, without manual programming.
A tutorial on building a multi-agent AI system with specialized agents using IBM's Watsonx Orchestrate platform and Docker.
Peekaboo 2.0 is a fast macOS screenshot tool for AI agents, now available as a CLI to avoid MCP context bloat and enable on-demand use.
A guide to adding long-term memory to a Gemini 2.5 chatbot using the Mem0 library and vector databases for personalized AI interactions.
A tutorial on building an AI agent with Watsonx.ai and integrating it using the Model Context Protocol (MCP) Gateway for seamless tool communication.
Explains why Context Engineering, not just prompt crafting, is the key skill for building effective AI agents and systems.
A guide to building configurable, collaborative AI agents using the OpenAI Agents SDK, covering agent factories, tool registries, and collaboration patterns.
Explores the trade-offs between single-agent and multi-agent AI systems, discussing their characteristics, pros, and cons for different tasks.
