Rethinking the Unit of Work in Software Development

2025-09-23

This article explores best practices for defining the 'unit of work' in software development. The author argues that a good unit of work should be decomposable, verifiable, independent, and prioritizable, similar to a user story but with a stronger emphasis on its role throughout the entire software lifecycle. Clearly defining the unit of work, the author claims, increases team efficiency, reduces unnecessary complexity, and ultimately delivers more customer value. The article also critiques the practice of solely measuring AI-assisted development efficiency by code generation volume, advocating instead for a customer-value-oriented assessment of the unit of work's actual impact.

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Development unit of work

AI-Assisted Coding: Mastering the Unit of Work

2025-09-18

Effective AI-assisted coding isn't just about intelligent models; it's about meticulously managing units of work. The author argues that breaking down tasks into appropriately sized units is crucial. Too small, and efficiency suffers; too large, and context loss leads to error accumulation. The ideal unit should possess clear business value, like user stories, enabling human review and error correction, minimizing AI error compounding. The StoryMachine project aims to define more effective units of work to enhance AI-assisted coding efficiency and accuracy, ultimately making AI development less of a gamble and more effortless.

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Development context management

Engineering Leadership: A Monthly/Quarterly System Health Check

2025-09-13

This post proposes a 2-4 hour system health check for engineering teams to conduct monthly or quarterly. The check assesses system quality across dimensions like reliability, performance, cost, delivery, security, simplicity, and organizational structure. Thought-provoking questions guide reflection on the system's current state, moving beyond mere metrics to identify and address underlying issues. The goal is to improve overall system quality and team effectiveness.

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Development system health check

AI: A Recursive Paradigm Shift

2025-08-13

This article explores the revolutionary impact of Artificial Intelligence (AI) as a new General Purpose Technology (GPT). AI is not only changing how we access knowledge but also how we think, even triggering a recursive paradigm shift: software uses AI, AI uses software, AI builds software, and AI itself is software. The author argues that the rapid development of AI brings immense opportunities and challenges, requiring us to adapt and participate actively, exploring future AI applications and redefining our roles in technological transformation.

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MCP: Simplifying AI Integration with a New Protocol

2025-05-22

The Model Context Protocol (MCP) is an emerging protocol designed to simplify the integration of AI applications with various data sources and tools. It reduces integration friction by transforming the M × N integration problem into an M + N problem. MCP servers connect to data sources and expose tools, while MCP clients (typically part of AI applications) can connect to any MCP server. The author demonstrates how to easily integrate an AI application with CKAN data using a CKAN open data access MCP server and utilizes the Claude desktop application for data analysis. While MCP isn't a silver bullet, it offers a more convenient and flexible way for AI application development, especially in scenarios that require integration with multiple external systems.

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AI