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The AI Learning Curve: Why Devs Get Slower Before They Get Faster

Experienced developers often slow down when adopting AI coding tools, not because the technology fails, but because they're learning to shift from solo creator to collaborative reviewer of AI-generated code. Drawing from data showing a 19% velocity decrease among senior engineers and insights from the [DEV]olution podcast, this piece explores why the initial friction is actually an investment. Teams willing to be slower today while building trust through verification and establishing the right infrastructure will ultimately be faster, more confident, and more productive tomorrow.

AIOct 29 2025
A blog post header image displaying a speed gauge and article title

Why Your AI Agents Need Ephemeral Environments (And Your Devs Probably Do Too)

Local development environments are inconsistent and unpredictable, filled with forgotten configs and custom workarounds. For human developers, this is frustrating—but for AI agents that can autonomously execute commands, it's genuinely dangerous. Ephemeral environments solve this by providing fresh, production-like workspaces that can be spun up instantly and deleted just as quickly, containing the blast radius when things go wrong. Beyond making AI agents safer, they eliminate the "works on my machine" problem entirely, helping human developers debug actual code instead of investigating mysterious local setups.

AIOct 23 2025
A blog post header image stating that AI agents and human developers need ephemeral environments.