GenAI – Force Multiplier

Whew — got physical today. Jogging, working out, pushing myself. NBA convention coming up, so I’m trying to keep the momentum going.

From a learning and professional perspective, I’m noticing something interesting: the challenges I run into on the “professional” engineering side are the exact same kinds of challenges I run into on the home-lab or development side. Different environments, similar problem-solving patterns. Systems are systems.

But the thing that’s really fascinating — and something I was mentioning to my colleagues — is how much of a force multiplier AI has become for me.

AI as Acceleration

When I’m building applications, AI lets me generate boilerplate code in seconds. Stuff that used to take hours — the scaffolding, the repetitive patterns, the configs — now takes a couple of minutes. The core ideas still come from me, but I get to stand on a higher platform and build faster.

Testing used to be the same story. Crafting a JSON test payload with multiple layers of parameters? That used to take 30 minutes or an hour if the spec was complicated. Now I can generate it instantly, try variations, and validate behavior way faster. It completely changes the tempo of development.

The Real Impact

It’s not just speed — it’s iteration. AI reduces the friction between idea → implementation → test → improve. Faster loops = better systems.

In fact, I’m going to expand this in a separate blog focused specifically on the problems I’ve been running into and how I solved them. The patterns are becoming clearer:

• Proxy configurations matter more than you expect
• CI jobs can silently hit rate limits
• AI can help diagnose and test faster
• System understanding compounds over time

Feeling excited about where all of this is going.