Chronological archive of 15 posts
How I built an automated system that generates social images for blog posts using multiple AI providers, with Claude analyzing results and a PR comment interface for human-in-the-loop selection. A case study in agent-orchestrated workflows.
For the past decade, AI models trained on the open web. That era is ending. The next frontier of training data isn't on websites—it's in our conversations with agents. If we don't build infrastructure to capture and share this data, the future of AI will be fragmented, siloed, and closed. Here's why we need an Agent Data Commons, and what it would look like.
When I hire an engineer, I look for their blog posts, open source contributions, and public work. Why should hiring an AI agent be any different? Exploring the rationale behind distributed, discoverable agents with public personas—and why this matters for the future of autonomous systems.
When you're cooking, there will be shells. When you're vibe coding, there will be markdown files scattered everywhere. AI-generated artifacts aren't bugs in the process—they're receipts of creation. Here's why fighting the mess is futile, and why hiring a cleanup crew (of agents) is the answer.
From mainframes to modern Rust CLIs—exploring how compile-time embeddings and offline-first architecture embody the principles of self-contained autonomous agents. A deep dive into building a blog distribution CLI that needs nothing but itself to run.
AI agents that generate code need the same thing humans do: fast, objective feedback. From plan-driven execution to self-verification systems, the future of AI-assisted development is built on the same loops we've always used—just at unprecedented scale and speed.
Over the past two years, a split has formed in software engineering. Some see AI as a new programming language—a compiler for natural language. Others see it as a powerful but fallible tool. Both perspectives miss something crucial: the ghost in the machine, and why fundamentals matter more than ever.
Frontend developers intuitively understand feedback loops. From instant visual validation to component isolation tools like Storybook, UI development is the art of seeing change instantly. Here's why it works—and how modern complexity almost broke it.
TDD isn't just about testing—it's about creating fast, objective feedback loops that make software observable. Understanding this foundation is critical for modern development, from frontend tooling to AI agents.
TDD, hot reload, Storybook, AI agents—they all solve the same problem. Understanding this pattern is the key to thriving in AI-first development.
Software engineering is fundamentally the craft of forming expectations and verifying them. From test-driven development to AI agents, the same core mechanic governs progress: tight, objective, observable feedback loops.
Progress doesn't ask for permission. As AI transforms how we build software, every engineer faces a choice: resist, observe from the sidelines, or learn to orchestrate the future. This is our Hamlet moment.
As AI agents write more code, the fundamentals matter more than ever. A reflection on relics, craftsmanship, and why less code is still better code — even when machines do the writing.
Revisiting my advice to "read more, write less code" in the age of AI. While research remains crucial, developer workflows now include a vital new step: co-creating with AI tools before coding.
We don't know what we don't know - You've got a task? Instead of going ahead and writing the code for it, try researching online.
January 2026