Found 24 posts in category Developer Philosophy
Goals break down as they grow: they outgrow the box, turn into pointers to files, and get judged by what the working agent chose to show. Desired-state development declares what must be true instead, and lets a governing agent, increasingly a personal assistant, reconcile toward it until the evidence and the owner's verdict say it holds.
Mobile-first taught us to design for the most constrained screen first. Agent-first asks a more fundamental question: what should our software be able to do when the user never opens it? This post looks at assistants that live in messaging apps, at capabilities an agent can discover and verify, and at what changes for marketing, discovery, and small open-source projects when the evaluator is an agent.
Software is no longer just its source code. Its harness — the tools, memory, skills, and rules guiding the agents that build and run it — is now first-class.
Software used to be expensive enough that we assumed every serious effort should last. AI weakens that assumption. Some software now only needs to exist long enough to solve one problem, communicate an idea, or feed the real build. Disposable software is its own category — not a lower one.
The Unix philosophy — small programs, composed through pipes, doing one thing well — turns out to be an ideal operating model for AI agents. Skills leverage this by guiding agents to use battle-tested programs rather than reinventing them. Here's why that beats building custom MCP servers for most workflows.
We've become unpaid staff for the applications we use. The future will separate operational back-office work from human-facing front-office experiences, with AI agents handling the infrastructure while humans focus on judgment and decision-making.
After more than a decade in tech, I thought I'd seen every variation of the hiring process. Then I worked with a recruiter who showed me what it looks like when someone genuinely cares about doing the job right. This is a reflection on trust, transparency, and why the best recruiting feels less like a transaction and more like a partnership.
Work is the worst place to learn how to use AI well—precisely because it's the place where results are expected. In 2026, having a personal or open source project isn't about passion. It's about having a place where you're allowed to learn slowly so you can perform quickly elsewhere.
There's a line in a song that aged better than anyone expected: your own personal Jesus. In the AI era, it stops being poetic and starts becoming literal. But this isn't about simulating Jesus—it's about building agents that embody the behavioral patterns of moral revolutionaries, governed openly, and accessible to everyone without institutional gatekeeping.
There's a strange moral superiority creeping into how people talk about AI-assisted development. We mock AI for making mistakes we'd forgive in ourselves. We demand one-shot perfection from tools while accepting iteration from humans. Maybe it's time for a little more empathy—and a little more honesty about how software has always been built.
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.
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.
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.
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.