Focuses on AI-powered enterprise digitalization, sharing product and technology practices. Covers AI use cases, technical architecture, product design examples, and industry trends. Aimed at developers, product managers, and digital transformation professionals, providing practical solutions and insights. Uses technology to drive digitization and AI to enable business innovation.
The article breaks down the 2026 mid‑year AI model landscape, compares tiered pricing across major providers, and offers concrete selection rules, caching tricks, tool‑chain setups, and habit‑based practices that together let teams minimize spend while maintaining high‑quality output.
By adapting the engineering practices of AI teams—goal definition, incremental execution, verification, and knowledge persistence—this article shows how anyone can transform ad‑hoc AI tool usage into a personal, loop‑engineered workflow, using Fable 5 principles, context engineering, and reusable SOPs for sustained collaboration.
The article applies Jevons Paradox to AI coding, arguing that as software becomes as cheap as water and electricity, previously unaffordable enterprises will become customers, unleashing a long‑tail of fragmented, on‑site software demand that rewards programmers with deep industry knowledge.
The article outlines how AI programming tools can shift coding from a scarce skill to a shared capability, detailing the creation of clear, maintainable engineering standards—Skills, hooks, templates, and checks—that transform individual productivity gains into coordinated team-wide processes.
The article analyzes how enterprise AI rollout transforms middle managers from traditional experience‑based overseers into architects of reusable organizational capabilities such as Skills and Agents, reshaping responsibilities, decision‑making and the overall structure of the company.
The article explains how AI product managers should first dissect business pain points into clearly defined, high‑frequency tasks, evaluate their suitability as AI Skills, and use the TCRT framework to design, scope, and validate these Skills before building reliable, feedback‑driven AI solutions.
The article analyzes how AI‑native products reshape traditional software by processing unstructured data with LLMs, adding a semantic layer that understands, calls, outputs, and learns from business context, thereby turning rapid business changes into traceable, reusable system capabilities.
The article outlines a systematic reverse‑engineering process for legacy systems that extracts factual system knowledge, organizes it into AI‑consumable context, and integrates the workflow into a continuous delivery loop to improve AI draft accuracy and team cognition.
The article reviews a year of production‑grade AI agent deployments, revealing that engineering challenges—data handling, rule governance, workflow integration, context quality, and clear boundaries—are far more critical than model performance for successful real‑world adoption.
The article explains how AI‑first teams can turn AI coding from a hit‑or‑miss practice into a controllable, measurable engineering process by focusing on context quality, a six‑stage delivery loop, Skill‑as‑Code assets, and continuous verification, ultimately stabilizing first‑draft accuracy.
