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The author explains the reverse Jevons paradox in software engineering: when the cost of making changes rises significantly due to red tape, the total volume of changes can drop to zero, leading to a fundamental halt in incremental improvements rather than a mere slowdown.
A software engineer discusses when feature flags make sense, such as for A/B testing and complex deployments, and cautions against overusing them when teams have control over their deployments.
SQLite developers share their reliability experience, derived from the DO-178B avionics standard, ensuring code quality through 100% MCDC test coverage, emphasizing the philosophy: 'if it hasn't been tested, it doesn't work.'
An analysis of the 'AI productivity gap' in software engineering, arguing that AI mainly speeds up the coding portion of developers' jobs while leaving other crucial tasks like design, reviews, and meetings largely unchanged, leading to only modest overall gains. It also notes juniors benefit more than seniors, contrary to some leaders' assumptions.
The article provides a detailed breakdown of OpenAI's complete software engineer interview process, analyzing round by round the core evaluation points behind initial screening, coding, system design, take-home projects, and the final interview, and compiles them into a systematic engineer advancement checklist.
Carl Kolon, an engineering leader, shares his curated 'required reading' list of software engineering articles, covering coding practices, platform design, and frontend development, with personal notes on why each matters.
An essay arguing that software engineers repeatedly reinvent well-solved infrastructure like auth, background jobs, rate limiting, and feature flags, trading proven solutions for custom code that they must maintain and debug.
An opinion piece arguing that platform engineering and code reuse remain valuable even with AI coding tools, because tokens cost money and reuse provides leverage. Cites Martin Fowler's similar argument about refactoring.
An essay discussing the common statistical fallacy of averaging percentiles, framed with Hamlet quotes and Hacker News examples, and arguing for empathetic communication of statistical insights.
The author describes how exe.dev decouples billing from business logic using 'billable facts' — atomic events that trigger downstream reconciliation with Stripe, avoiding brittle billing code.
Valentina Servile argues that good software design still matters even as AI writes more code, because the audience for well-designed code now includes AI agents as well as humans.
A Google Testing blog post offering guidance on how to respond to code review comments, emphasizing the importance of adding context when it helps clarify decisions and rationale.
The article argues that selling AI wrappers (simple interfaces over existing models) is easier than building AI systems that actually work reliably in production, highlighting challenges in deployment.
This article presents an experiment on refactoring a large codebase written by AI agents, demonstrating that refactoring reduces token consumption for future AI-driven changes. The author details the methodology and results showing a decrease in input tokens after refactoring.
A software engineer expresses frustration with local LLMs for agentic coding, citing issues like technical debt, ignored instructions, and excessive code generation, questioning their usefulness.
Hillel Wayne analyzes the historical and practical barriers preventing widespread adoption of formal methods in software engineering, distinguishing between formal specification and verification across code and design domains.
A developer reflects on how AI coding agents can rapidly build and modify software, but developers often lose understanding of the codebase architecture and decisions, creating a new engineering challenge.
Addy Osmani recommends using SonarSource for catching bugs and security issues on every commit, emphasizing code quality and taste.
A practical book introducing logic for programmers to improve software design, verification, and reasoning, covering topics from simplifying conditionals to formal verification and constraint solving.
This paper introduces CanItDelete, a benchmark of 200 real-world deletion-only code editing tasks, and measures how LLMs avoid deleting code. It finds that frontier models frequently leave obsolete code in place, producing passable but not merge-ready patches, and that adding deletion-focused training data improves performance.