The author reflects on automating reading with AI, distinguishing information gathering from thinking exercises, and proposes a three‑question framework to decide when automation adds value versus when personal reading is essential, ultimately choosing to limit AI to pre‑ and post‑reading tasks.
The article explains how multi‑agent architectures solve three structural bottlenecks of single‑agent AI—context overload, diluted expertise, and hidden failure points—by showing a concrete contract‑review use case and outlining four essential product‑design decisions for AI PMs.
The article examines the conflicting narratives around Vibe Coding, clarifies its true purpose for product managers, and presents a decision framework that distinguishes when rapid prototyping adds value versus when traditional engineering is required, emphasizing the need for deeper business understanding alongside AI tools.
This article analyzes the trade‑offs between Model Context Protocol (MCP) and Skills for Claude‑based AI workflows, offering a three‑question test, real‑world examples, and architectural diagrams to help engineers choose the most efficient approach for their tasks.
This article provides a detailed technical comparison between Model Context Protocol (MCP) and traditional Command Line Interface (CLI) for AI agents, outlining their design goals, performance trade‑offs, security implications, benchmark results, and a practical decision framework with concrete signals for choosing the right approach.
This article presents a practical framework for CTOs to evaluate three leading AI agents—Claude Code, Microsoft Copilot, and OpenClaw—across data sovereignty, ecosystem integration, cost structure, and team capability, and demonstrates the approach with a realistic fintech case study.
The article outlines the challenges of distributed architecture governance and presents a layered decision framework, ADR usage, technology radar, committee structures, tooling, and continuous improvement practices to achieve consistent yet innovative architectural standards.
The article examines why AI product managers repeatedly fall into three traps—over‑relying on prompt engineering, blindly adopting Retrieval‑Augmented Generation, or costly fine‑tuning—by presenting real‑world failures, debunking myths, and offering a five‑layer decision framework with cost, data, resource, and risk analysis to choose the right solution.
Choosing the right technology involves evaluating functional fit, non‑functional quality attributes, and team considerations, and this article presents a hierarchical decision framework that helps structure the selection process, adapt dimensions to specific scenarios, and foster consensus within development teams.
This article shares an Amazon Cloud R&D manager’s perspective on product management, covering how customers, engineers, sales and marketing view the role, the five essential customer‑centric questions, priority‑setting logic with the RICE model, innovation culture, and practical advice for transitioning product managers.
