Found 13 posts with tag #Software Engineering
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.
Documentation drift is the silent killer of codebases. Your roadmap says one thing, your website another, and your README hasn't been updated in months. Here's how I built a pluggable Claude Code skill that detects drift, shows exactly what's out of sync, and fixes it—with your approval.
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.
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.
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.
