Tag: sdlc

  • programmers are managers now

    The central task becomes describing an objective precisely enough for the system to divide it, establishing the rules under which the agents operate, and judging whether their collective output belongs in the codebase. 🔗 Claude Code Turns Software Development Into a Management Job

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  • Inside OpenAI’s agentic software factory

    We’re talking about roughly a 10x increase in load on some systems. At most companies, that kind of growth might happen over two or three years. At OpenAI, we see it in about six months. 🔗 Inside OpenAI’s agentic software factory

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  • The Interfaces Are Arriving

    For an engineering organization, the immediate consequence is reuse. An MCP server for an internal ticketing system can serve every compatible IDE, chat application, and agent. Models and hosts will keep changing; the interface contract, server implementation, schemas, and authorization wiring can endure. The integration becomes an asset shared across applications. Language servers followed the…

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  • Reframing the AI-native SDLC in terms of stewardship boundaries

    To make AI agents succeed, we must inject very clear, tightly constrained context. This is where the platform patterns we popularized in Team Topologies become essential. A well-designed internal platform acts like a “vending machine,” providing self-service, reliable access to clean data, APIs, and operational capabilities. By reducing cognitive load on human teams and narrowing…

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  • Relative to your interests, Tuesday

    Relative to your interests, Tuesday

    I have been on vacation for a little while, in Finland for the third time. Finland feels different than the other parts of Europe I have been to. Perhaps it’s because I spent my time there in rural Finland. But it also feels like most of Finland is rural. In that way, it feels a…

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  • Enterprise Java apps are much more than just code, AI benchmarks don’t measure the Java SDLC enough

    There’s countless dependencies in other frameworks and libraries, plus the build pipeline and even production platform are part of the app. Enterprise Java applications frequently span hundreds or thousands of modules connected through Maven or Gradle builds, Spring dependency injection, Jakarta EE services, annotation processors, generated code, legacy XML configuration, container orchestration, proprietary build plugins,…

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  • The Death and Rebirth of Programming – The Phoenix Architecture

    AI collapses the cost of writing code to near zero while leaving the cost of understanding it intact, so software should be architected around disposability and regeneration rather than ownership and preservation. Summarized by AI. 🔗 The Death and Rebirth of Programming – The Phoenix Architecture

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  • What the Top 1% of Engineering Teams Do Differently with AI

    Data across 10k+ engineers shows a power-law gap between top performers and the rest, with winners running flatter teams, spending more on AI for greater output per dollar, and prioritizing spec review over code review. Summarized by AI. 🔗 What the Top 1% of Engineering Teams Do Differently with AI

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  • The reason AI coding isn’t working on your team

    The underlying model matters less than the ‘harness’ surrounding it – context files, scoped tests, guardrails – and requires a named owner to prevent burnout and maintain code quality. Summarized by AI. 🔗 The reason AI coding isn’t working on your team

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  • Trisha Gee: AI Amplifies What’s Already Broken

    Trisha Gee argues AI does not fix a broken pipeline, it exposes and breaks it faster, drawing on DORA metrics and the SPACE framework for what real measurement shows about AI adoption. Her assessment is considerably darker than the conference hype around it. Summarized by AI. 🔗 Trisha Gee: AI Amplifies What’s Already Broken

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