Two colleagues who owned a critical subsystem left within a week of each other, and a leftover task in their area — one I barely knew — landed on me. The migration plan for it was freshly AI-generated, and what I didn’t fully trust was my own grasp of it. So I asked my AI to quiz me — not to grade me, just to see what I’d actually absorbed. ...
An engineer’s conjecture on distributed biological intelligence, dreams, and the uncomfortable trajectory of AI In computing, thick clients carry their own processing power, storage, and local decision-making — but they become dramatically more capable when networked. A thought lodged in my head recently and wouldn’t leave: A human brain looks remarkably like a thick client. Andy Clark and David Chalmers argued in their “Extended Mind” thesis (1998) that cognition doesn’t stop at the skull — it extends into notebooks, tools, and cultural artifacts. They didn’t use the client-server vocabulary, but the implication is the same: the brain is a node in a larger system. ...
Your AI coding assistant quotes a specific line from a specific file. The file is real. The section name is close. The quote sounds exactly right. But the line doesn’t exist — and that’s precisely what makes it dangerous. A Quote That Never Was I’ve been working with Claude Code on a large healthcare integration project — syncing an on-premise practice management system (I’ll call it “SimplePractice”) with a cloud FHIR platform. Over several weeks, we’ve built push/pull sync, environment tooling, documentation, and dozens of shell scripts. It’s been genuinely productive. ...
Gemma 4 Structured-Task Performance: Field Report from a Local-First App Benchmark data and prompt-format findings from deploying Gemma 4 E4B in a real application. Intended for LLM teams (Gemma, Ollama) and developers building structured-output pipelines on local models. Context We build Gary, a privacy-first personal assistant CLI for macOS. It runs entirely locally — an encrypted database, a daemon process, and a local LLM via ollama. The LLM handles three structured tasks: ...
Let’s be real. When the first AI coding agents dropped, we all nodded solemnly and said, “Of course, a human will always review every single change. Safety first.” We lied to ourselves. Or, more accurately, we succumbed to the seductive illusion of frictionless productivity—a 10x illusion where we feel like we’re coding faster, but we’re actually just accumulating debt we can’t afford to pay. The recent Stack Overflow post on AI as a second brain identifies the core issue: we are offloading our judgment. This isn’t a future sci-fi risk; cognitive offloading is happening now, and it’s reshaping both our codebases and our minds. ...
The 10x Illusion: If AI Codes 10x Faster, How Much Faster Do Projects Actually Ship? AI coding tools are getting shockingly good. So it’s natural to ask: if the coding part gets 10x faster, shouldn’t the whole project get 10x faster too? The answer is surprisingly counterintuitive — and backed by a growing body of data. The Speed Is Real. The Extrapolation Is Not AI coding tools deliver genuine speed on implementation tasks. GitHub Copilot studies show developers completing isolated coding tasks 55% faster. AI agents can generate entire modules in minutes. The speed is not the illusion. ...