Explores whether code quality remains important when LLMs generate code, questioning if human-centric quality standards apply in an AI-driven future.
Explores five software architecture mistakes that make systems hard to change, emphasizing understanding the domain before choosing tools.
Explores how AI-assisted programming affects coordination in large software projects, using the Tower of Babel as a metaphor.
AWS Transform custom uses agentic AI for large-scale software modernization, offering five new managed definitions to reduce tech debt.
Charity Majors argues that AI-generated code requires more engineering discipline, not less, drawing parallels to the shift from server pets to immutable infrastructure.
Explores intent debt as a hidden cost in software engineering, distinct from technical and cognitive debt, and why AI agents can't resolve it.
Explores the concept of typed, budgeted, and auditable memory for AI agents, moving beyond flat retrieval to structured context management.
Analysis of the growing divide between AI enthusiasts and skeptics in tech, exploring their conflicting views on AI's impact on software development.
A Rails performance audit report for the Organization for Transformative Works and Archive of Our Own, focusing on optimization and technical debt reduction.
Explains why tech leaders should adopt proven solutions instead of building custom systems, focusing on cost, maintenance, and competitive advantage.
Analysis of how prioritizing speed over communication in tech projects leads to collaboration breakdowns, technical debt, and system fragmentation.
Explores the decision between rebuilding or refactoring legacy software, covering challenges and business considerations.
Explores cognitive, technical, and intent debt in software systems, plus LLMs as System 3 thinking.
A Debt-Aware ADR Model to manage technical debt and speed up architectural decision-making by treating choices as financial transactions.
A reflection on the value of time, patience, and friction in software development and company building.
![[journal] Noticing, and Solving, Technical Problems in a Codebase](https://crawljobs-blogs-media.s3.us-east-1.amazonaws.com/alldevblogs/thumbs/article-7c77fb3c4207-full-c98199c5.webp)
A guide for developers on identifying and addressing recurring technical friction in codebases to improve long-term maintainability.
Explores 'comprehension debt,' the hidden cognitive cost of over-relying on AI-generated code, which erodes team understanding and long-term maintainability.
Argues that AI coding agents should be used to improve code quality and reduce technical debt, not just speed up development.
Argues that AI coding agents can help developers produce higher quality code and reduce technical debt by automating tedious refactoring tasks.
Argues that Windows patching is an organizational governance and risk management issue, not a technical one, and defines clear operational roles.
