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.