tag · 20 essays
All essays tagged platform-engineering.
The obvious version of AI developer experience is a chatbot in the portal. The better version diagnoses the failure, repairs what the platform owns, and reruns to green before the developer opens the log.
★A gate proves a standard ran. It does not prove anyone can operate the result at 3 a.m. Operability has to become part of the spec, and the evidence has to survive the merge.
★The golden path made the right way easier to follow. It still asks developers to learn the road. With AI, intent can become the interface while the platform keeps the standards, the controls, and the gate.
★We treat enterprise context as something developers gather before they can use AI well. Platform teams should treat trusted context as something the platform provides.
★We built internal platforms for developers. Those developers now arrive with an AI agent beside them, and how well your platform serves that second user is becoming the real maturity test.
★Complexity is inevitable in modern engineering. Drag is optional. The work of leadership is turning repeated friction into platforms, defaults, and systems that help teams move faster with trust intact.
★A year of writing, one argument, and a working theory of where software is going
★Code used to be the durable asset. In an agentic SDLC, that changes. Code becomes the regeneratable output of a system that runs on something more important: a clear, versioned, reviewable specification. That shift changes what engineering organizations invest in, how they govern delivery, who they hire, and what they actually ship.
★Jasmine Sun argues AI politics has a new meta, and the warning shots have started. Reading her piece as an engineering leader, here is what the narrative failure looks like from inside a large team, why sociopolitical alignment is our job, and what each of us owes our own career in a market this fast.
After two greenfield cloud builds in financial services, these are the decisions that aged well, the ones I would redo, and why the small choices in year one decide whether you have a platform or a pile in year five.
★The strategy posts say AI software development is a system. Here is the working loop I run inside that system: a refined specification, a layer of standards, and a coordinated set of specialists doing the work.
★After thirteen months of daily Claude Code use, I stopped treating AI coding as a prompt discipline problem and started treating it like an engineering system: configurable, layered, observable, and built to learn.
Everyone sells pipeline-first delivery as a best practice. Remove access. Route through automation. Enforce consistency. What the slide deck leaves out is the part that actually determines whether this works.
★Back in January I wrote about OpenClaw as a concept. That post was the theory. This one is the implementation. One month running a dedicated EC2 instance, a name, and an agent I genuinely rely on.
Most enterprise conversations about GenAI are arguments about assumptions nobody has questioned. Here is what stays when you strip everything else away.
★Anthropic has shipped more meaningful product features in the last few weeks than most teams ship in a quarter. A theory — and what it tells us about what AI-assisted development actually unlocks when a team uses their own product to build it.
The companies that built the modern internet went bankrupt doing it. The companies building AI infrastructure may follow the same path. That is not a warning. It is how transformative technology actually works.
A humorous confession about subscribing to nine different AI development tools in my quest to find the best tool(s).
AI-powered interfaces are evolving beyond traditional graphical UIs toward intent-based interactions, where users describe desired outcomes rather than navigating through menus and clicks.
A hands-on look at how AWS Kiro transforms AI-assisted development from chaotic vibes into structured, spec-driven engineering.