A developer's personal reflection on the addictive nature of AI agentic engineering, the loss of work-life balance, and the industry-wide trend of extreme work hours in the AI space.
Explores how zero-trust environments like defense and finance can securely adopt AI using local-first agents and semi-autonomous workflows.
Part 2 of a guide on using Docker Compose to enhance the reliability and portability of AI agents, focusing on Dockerfile and compose.yaml.
A tutorial on using Docker Compose to create reproducible, containerized runtime environments for AI agents, focusing on a weather query example.
Explores key traits of AI-native products designed for efficient consumption by AI agents, focusing on context optimization and tool execution.
Explores implementing a 'human-in-the-loop' tool for AI agents using Python async and DSPy, enabling agents to ask users clarifying questions.
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 developer details building a modular, agentic Personal AI Infrastructure (PAI) system named Kai, focusing on the 'why' behind AI development.
A developer details building a modular, agentic Personal AI Infrastructure (PAI) named Kai, focusing on the 'why' behind AI tools and preparing for a post-work future.
A developer builds an AI-powered novel-writing assistant using the Snowflake method, dspy, and Gemini 2.5 Flash-Lite, sharing the project on GitHub.
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).
