Law firms lag behind AI’s rapid technical advances because they treat tools like add-ons instead of rebuilding workflows around them. The real opportunity lies in “absorption”—writing firm-specific practices into models, training lawyers on prompt anatomy, and embedding new procedures at the practice level. Those who tear up the old billable-hour floor and automate production will secure a lasting edge.

This article introduces Agentic Experience (AX) Design, a new field focused on mapping and automating business workflows for autonomous AI agents rather than humans. It outlines the AX designer’s role—investigating real processes, structuring machine-readable systems, and defining guardrails—to ensure reliable, scalable automation.

This guide breaks down 30 fundamental ideas behind AI agents—from the basic think-act-observe loop and state management to multi-agent patterns, guardrails, and observability. It shows how to configure, extend, and safely run agents in any framework by focusing on underlying principles rather than tools.

This article argues that AI tools speed up code delivery but raise cognitive strain, erode satisfaction, and drive developers into a cycle of nonstop, draining work. It breaks down how skipping hands-on coding reduces ownership and fulfillment, then offers steps to restore enjoyment, pride, and sustainable workflows.

A developer ignored tmux for a decade, rebuilt their terminal workspace every morning, and lost over two hours daily. After learning six basic tmux commands, they cut setup and context-recovery time to two minutes, reclaiming hours for real work.

This article examines how AGENTS.md files impact AI coding agents, showing that well-structured agent docs can boost code quality by up to 15% while poorly designed ones can hurt performance. It outlines seven patterns that work—like progressive disclosure, step-by-step workflows, decision tables, real-code examples, and pairing “don’ts” with “dos”—and warns against overexploration from excessive context or warnings.

Prompt Opinion lets healthcare organizations plug in interoperable AI agents, tools, and standards into real workflows. It uses MCP, A2A, and FHIR to connect EHRs, policies, and data, turning standalone agents into production-ready tasks like prior authorizations, trial matching, and population health analyses.

This article discusses a live session with Alejandro Aboy, a Senior Data and AI Engineer, about the evolving role of data engineers in the context of AI. Alejandro emphasizes that strong data engineering skills, particularly data modeling and orchestration, are crucial for successful AI work and shares practical steps for integrating AI into existing data workflows.

This article discusses the need for new workflows in product development as traditional methods like Agile and PRDs become obsolete. It highlights the shift in how teams work, emphasizing the importance of tools that adapt to modern, nonlinear processes. The author argues for a new structure that aligns with current realities rather than outdated practices.

The Compounding Engineering plugin enhances development workflows by systematically improving the planning, execution, and review stages of coding. It leverages AI to create comprehensive issues, manage isolated tasks, and conduct thorough code reviews, ensuring that each unit of engineering work makes future tasks easier. By documenting processes and refining quality, this tool aims to build a more efficient development system over time.

Durable Execution (DE) engines allow for the persistence of multi-step workflows, enabling them to resume from the last successful step after interruptions. This article explores the development of a simple DE engine called Persistasaurus in Java, which utilizes SQLite for logging execution state and highlights key implementation choices.