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The Pragmatic Engineer surfaced this October 29 conversation with Titus Winters, a former Google engineering leader who helped run the company’s C++ infrastructure and language-readability programs before joining Adobe. The episode connects three questions that are often discussed separately: how to change enormous codebases safely, how to judge whether AI actually improves engineering, and what new programmers should learn when generating code is becoming cheap.

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The Pragmatic Engineer’s October 15 Pulse surfaced Anthropic’s original engineering account of replacing Git for its main monorepo. The striking part is not simply that Anthropic built another internal developer tool. It says AI-generated code increased the rate of change so dramatically that assumptions embedded in source control, code review, CI, and even the meaning of a working copy stopped fitting the workload.

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The Pragmatic Engineer surfaced its September 30 episode, “Distributed databases with Peter Mattis”, a conversation with the Cockroach Labs co-founder and CTO about building storage systems at Google, turning Spanner’s ideas into a commercial database, and using AI agents without abandoning engineering judgment. The most useful thread is not a prediction that databases or programmers disappear. It is that durable abstractions and fast verification let one expert explore far more designs than before.

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The Pragmatic Engineer surfaced its September 24 Pulse, “The Pulse: RoR creator sparks new ‘death of coding by hand’ debate”. Its public preview reports that 37signals now has agents generate nearly all its code and asks whether professional hand-coding is over. In his Rails World keynote, co-founder David Heinemeier Hansson says the company recently made agent-written code its normal workflow; writing code by hand is now the exception.

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The Pragmatic Engineer surfaced its September 24 report, “The Pulse: a new trend of CPU shortages”. After years of focusing on GPUs, the AI build-out is tightening access to ordinary server CPUs too. Agents create this second demand stream because they do more than ask a model for tokens: they run code, call tools, compile, test, and search through software environments. Reinforcement learning workloads also rely on CPUs to execute the tasks models are learning to perform.

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The Pragmatic Engineer surfaced the September 9 episode, an interview with Codex co-creator and OpenAI product leader Tibo Sottiaux about how the coding agent was built and how it is changing software work inside the company. The conversation’s strongest idea is that a useful coding agent is not merely a language model in a terminal. Its performance comes from a coupled system: the model, the harness around it, a reproducible execution environment, access to organizational context, and the tests and review practices that turn generated code into trusted changes.

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The Pragmatic Engineer surfaced Ramp’s internal coding system in its August 25 deep dive, “Why Ramp built its own in-house coding agent, Inspect.” The headline number is striking: Ramp says Inspect now originates 75% of the pull requests merged at the company. The more important result is architectural. Inspect is not a better foundation model. It is a model-agnostic agent wrapped in a remote developer environment, connected to Ramp’s internal systems, and required to verify its work with the same tools an engineer would use.

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The Pragmatic Engineer surfaced the Asana Engineering Team’s August 7 post, “We migrated off Enzyme in 2 weeks. It should have taken five years.” The eye-catching result is real: Asana removed its remaining use of an obsolete frontend testing library in about a week and a half of engineering time. The more useful story is why this particular job was unusually well suited to coding agents.

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The Pragmatic Engineer surfaced the piece in “Headed for the Exit: the Great Engineering Leader Career Break”, Gergely Orosz’s account of an unusual pattern: experienced CTOs, vice presidents of engineering, and heads of engineering are leaving desirable jobs without another role lined up.

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The Pragmatic Engineer surfaced the piece in “Formal methods with Hillel Wayne”, a conversation about a question made more urgent by AI-generated code: if software can be produced faster than people can understand it, what should teams trust as evidence that it is correct?