Tag: codegeneration
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short feedback loops always works
Using short feedback loops to experiment and direct software development is always a good idea, especially with AI: What survives from Agile is the feedback logic. Short cycles still matter. Thin vertical slices still matter. Customer or stakeholder review still matters. Working software is still better than progress theater because agents can generate a lot…
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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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Harness engineering is the new DevOps
Are we going to see harness engineering teams? Yes: these are already emerging. It’s too early to say, but that feels wrong in the same way that DevOps teams were a misunderstanding of DevOps-the-movement. My belief is that while there is friction in having every team create their own harness, that friction is generative and…
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training your replacement
Yes: the work is moving from writing code to specifying, verifying, and curating knowledge. But: the code is the better spec. Their point: code is the only artifact that’s unambiguous, executable, and – this is the big one – deterministic. A prose spec is fuzzy and an agent will interpret it a little differently every…
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Reducing token usage
Better Defaults (not Usage Caps) — Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage…
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How AI change your software development organization
A catch-up with Purnima Padmanabhan, GM of the Tanzu Division at Broadcom, on what her team has actually learned shipping enterprise software with AI for the last year and a half: code generation is the small part, beautiful code is the new uncanny valley, and you cannot solve the agent boundary problem from inside the…
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That means each employee’s AI spending cap is ~11% of that median compensation package. Last year, there were a few anecdotes about high growth tech companies spending $100,000/year per head on tokens. That seems like it’s coming to end.a 🔗 Uber Caps Usage of AI Tools Like Claude Code to Manage Costs
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Three reasons why a “batteries included” platform is urgently needed right now
Removing product as a bottleneck: The conversation around PaaS is urgent again, and AI is why. Code generation can speed up your development cycles, building and pushing features faster, but production delays will persist if you’re still deploying at the same speed as before. To avoid eroding the benefits of code generation, you need to deploy…
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Enterprise AI Slop
People are using AI to generate too much work because they think they know what they’re doing: A growing body of work calls this output-competence decoupling. In any previous era, the quality of a piece of work was a more or less reliable signal of the competence of the person who produced it. A novice…
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The users have plenty of feature ideas
AI allows people who aren’t software engineers to build meaningful software. Those of us who are software engineers at companies should stop building features and focus instead on building systems that allow people on the sales team, the factory shop floor, etc. etc. etc. to ship safely. 🔗 notes from o11ycon 2026

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