tag · 26 essays
All essays tagged engineering-leadership.
My personal delivery pipeline runs on three Claude agents, three nested loops, and two explicit human approval gates. The gates sit where judgment is cheapest and most consequential, and the real bottleneck moved upstream to the clarity of the ask.
★Work is already hard. Leadership should remove the avoidable human drag: unclear decisions, unsafe disagreement, personal friction, and cultures where telling the truth costs too much.
★I lead engineering at a Fortune 100 company, and I still ship my own software. Not because leaders need to prove they can code, but because AI is changing the work faster than secondhand models can keep up.
★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.
★AI has made generation cheap. The durable advantage now belongs to teams that can encode judgment into gates that verify whether the work should ship.
A government order took Anthropic's Fable 5 and Mythos 5 offline. The lesson for enterprise AI is architecture: portability gets you out, verification gets you out safely.
★Build, WWDC, two confidential S-1 filings, and a tiered frontier model launch hit in the same two weeks. The agent became the interface just as the invoice arrived.
★Enterprise AI is entering its accounting phase. The winners will measure verified outcomes, not just tokens, subscriptions, or generated output.
★Most AI reorgs open with a headcount model. The better sequence redesigns workflows, decision rights, ownership, and evaluation loops first, then lets the org chart follow the work it is meant to describe.
AI's free-lunch phase is ending, and that makes AI-driven layoff math harder to defend. Two repricings, one ledger.
Agent loops let the model pick the next step. Workflows invert that. Code owns the control flow; the model owns the judgment inside each step. Here is the TypeScript file I am running today, type-checked against the live SDK, and the honest answer to whether you should build this now or wait for the official tool.
★My thesis is that as agents get better at execution, the primary constraint for organizations shifts from technical production to the human-led framing of problems.
★Viral prompt threads borrow the language of science without the rigor. Here's a four-question code review for any prompt, plus a worked example that shows the gap between sounding authoritative and being right.
A field report on building Inkwell, a pure-CSS design system that turns taste into repeatable constraints for people, teams, and coding agents.
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.
★A weekend home-networking project became a practical lesson in AI-assisted reasoning, documentation, guardrails, and engineering judgment.
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.
Anthropic's new design tool does not threaten senior designers. It threatens the apprenticeship that made them senior.
Every tool in your product development life cycle is now an AI agent trying to do everything. Here is how to stop the chaos, draw the right boundaries, and build an orchestrated pipeline that actually works.
★Most engineers treat AI-generated code like work from a junior developer they don't trust. Simon Willison gave me a better mental model: the dark factory. Here is what it means, why experience is the raw material, and how to build a system that runs.
Google dropped Gemma 4, and I had it running locally the same night. What open weights actually mean, the hardware reality, and why the most interesting AI architectures are about to go hybrid — on-device and in the cloud.
After thousands of sessions with Claude Code, Codex, Kiro, and every other LLM-based CLI and IDE, I distilled what I learned into a reusable Claude Skill. Here's how those lessons became the guardrails that let me move faster and actually trust the output.
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