The article explains how product managers often waste resources chasing 100% functional coverage, and shows that mature PMs use a 60%‑for‑80% approach—freezing low‑frequency, high‑cost scenarios, applying ROI formulas, scoring cards, and concrete ERP examples—to deliver core value efficiently.
The article debunks the buzzword “Product Sense” and argues that a competent AI product manager must master three core disciplines—computer fundamentals, psychology, and management—to make logical, user‑focused decisions and earn technical teams’ respect.
After two months of AI Agent projects, I distilled five counter‑intuitive iron rules—decision‑makers avoid exhibition booths, trust demands deep conversations, my strength lies with hour‑long clients, technical details can be outsourced while business insight stays personal, and acquisition must move from breadth to depth.
The article explains how AI product managers can avoid costly product failures by designing a comprehensive, multi‑dimensional evaluation framework that guides development, quantifies progress, and builds trust with users and stakeholders.
After a project finishes, teams often keep visible artifacts like screenshots and acceptance documents, but the truly valuable knowledge lies in the judgments, constraints, and reusable conditions behind those artifacts; this article explains how product managers can identify, evaluate, and correctly allocate that value to make future projects lighter, not heavier.
The article outlines how future software will serve AI agents instead of humans, describes three essential cognitive shifts for AI product managers, poses four critical questions, presents a detailed capability map covering business understanding, technical principles, data handling, evaluation, prompt design, product design, and ethics, and concludes with actionable advice for thriving in the fast‑moving AI product landscape.
OpenAI replaces its confusing model labels with the Sol‑Terra‑Luna hierarchy, merges Codex into the ChatGPT interface, and shifts focus from raw performance metrics to end‑to‑end task execution, illustrating how product‑level clarity, resource consolidation, and cost‑effective benchmarks reshape AI competition.
This article presents a detailed, step‑by‑step design of a universal restaurant POS and ordering system covering four‑tier modular architecture, complete business flow, functional breakdown for front‑end ordering, kitchen production, back‑office management, and core infrastructure, plus implementation steps and solutions to common deployment issues.
This article presents a detailed, step‑by‑step design of a universal contract management platform that replaces Excel and paper processes, covering lifecycle automation, RBAC permissions, template handling, electronic signing, risk alerts, integration with finance, OA and CRM systems, and comprehensive module breakdown across nine functional areas.
In an interview, Instagram head Adam Mosseri explains how AI is reshaping team structures, talent requirements, platform strategy, algorithm design, and content governance, emphasizing the continued importance of human judgment, taste, and transparent sourcing in a rapidly automated social ecosystem.
The article introduces Archify, an open‑source drawing skill that lets developers generate architecture, workflow, and sequence diagrams instantly via AI agents like Claude Code, eliminating the tedious manual work of tools such as draw.io.
The article reviews the PM Skills Marketplace, a GitHub project with over 22,000 stars that packages the entire product‑development workflow into Claude‑driven plug‑ins, detailing its nine skill groups, command usage, real‑world examples, benefits, limitations, and how to install it.
This article examines the distinction between project and product managers, outlines the high‑standard qualities required of product managers, describes the ideal career pathways and ability models, and details a resource‑pool framework for systematically developing product‑manager talent within an IPD‑driven R&D organization.
The article outlines the distinct qualifications of project and product managers, compares their required abilities, and provides a step‑by‑step guide—including mindset shifts and systematic product knowledge learning—for project managers aiming to become product managers.
The article analyzes Boris Cherny’s five talent prototypes for the AI era, explains how he flips the product‑lifecycle axis to replace traditional front‑end/back‑end/PM roles, presents supporting data from Anthropic, critiques the framework, and shows how companies and individuals can apply the model in practice.
The article defines projects and products, explains project management and product management processes, compares the roles, responsibilities, and career paths of project managers and product managers, and highlights why a project manager can successfully transition into product management.
Airbnb explains its three‑part Listing Lifetime Value framework—baseline, incremental, and marketing‑induced LTV—detailing the machine‑learning models, data collection, and the practical challenges of accurate estimation, incrementality, and uncertainty during market shocks.
The article details JD Health’s OPC team’s adoption of Anthropic’s open‑source product‑management Skills to create a repeatable, evidence‑driven workflow that guides problem validation, requirement definition, roadmap planning, sprint execution, and post‑launch review without a dedicated product manager.
The author recounts building the Design.md Token Exporter Chrome extension using ChatGPT, Codex, and Gemini, showing how AI lowers coding barriers but forces product teams to confront costly decisions about feature scope, privacy, permissions, and trust, emphasizing disciplined trade‑offs over unchecked ambition.
The article explains how product managers must move beyond writing better prompts to building repeatable, evidence‑driven loops that continuously improve long‑term assets such as PRD review rules, interview summarizers, and release checklists, outlining the five loop components, practical examples, and common pitfalls.
