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Sean Goedecke publishes an email from a reader criticizing his pragmatic stance on AI's impact on software engineering, along with his response drawing parallels to the Industrial Revolution and machine-breaking Luddites.
A developer explains that AI coding agents keep raising the database pool max connection limit despite comments and instructions, and the only reliable guardrail has been a test that fails if the value changes.
Gergely Orosz announces that Casey Muratori will be a guest on The Pragmatic Engineer Podcast and asks followers for questions to ask.
Matt Pocock announces his AI Coding Crash Course, a workshop teaching engineers how to apply strategic programming principles and use agent skills to stay in control of AI-generated code.
A software engineer reflects on how scientists often lack software engineering skills, using an example of optimizing an astrophysics simulation postprocessing tool and advocating for a 'missing semester' for scientists.
An essay arguing that AI-generated prototypes are not real products; a real product requires real users, a real market, and solves real problems. It critiques the hype around AI demos and reminds builders that without users, what they've built is just a toy.
An essay arguing that as AI collapses the cost of software production, taste—the wordless verdict of quality—becomes the decisive skill for developers.
An article debunking eight common myths about using generative AI in software engineering, offering a realistic perspective on its capabilities and limitations.
An educational guide that breaks down each stage of a production deployment—builds, artifacts, database migrations, health checks, rolling updates, and rollbacks—and discusses when to use a PaaS versus running your own deployment infrastructure.
Casey Muratori demonstrates how following 'clean code' guidelines like polymorphism and small functions can lead to significant runtime performance costs, using a classic shape area example.
A tweet argues that LLM benchmarks should report token usage alongside accuracy, introducing VulcanBench, an open-source benchmark for realistic software engineering tasks that tracks token efficiency and cost.
An engineer got an L4 SWE offer at Anthropic without solving LeetCode problems, highlighting a shift in top AI companies toward real-world tasks over algorithm memorization.
This paper surveys self-evolving coding agents, which improve their future behavior by updating frameworks, memory, skills, tools, or models from prior coding interactions, and presents a taxonomy of what evolves, when, and what software-specific evidence drives it.
Epoch AI and METR introduce MirrorCode, a benchmark that tests AI models on reimplementing entire programs end-to-end over long horizons. Early results show Claude Opus 4.7 solving a bioinformatics toolkit in 14 hours at $251, though memorization caveats remain.
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.