😸 Clean up crew for AI code

gm legends, happy Wednesday.

Here’s today’s lineup: Command Center helps engineers ship faster by cutting through the mess of AI-generated code; GitHub’s Mission Control gives you one dashboard to wrangle all your coding agents before they crash your repo; and Parallax links up your devices to run local AI models together, no cloud required.

P.S. Building something new? Tell us about it → editorial@producthunt.co 🫶

Command Center is an AI code review and refactoring agent built for engineers who spend more time reading AI output than writing their own. It learns your codebase, cleans up the mess, and helps you move from “does this even work?” to shipping without second-guessing every line.

🔥 Our Take: Everyone’s bragging about how fast AI writes code, but no one’s fixing the part where you still have to understand it. Command Center actually goes after that — the human bottleneck. It’s less about speed, more about peace of mind when you’re the one on call at 3 AM.

Nika started a thread about the growing split between AI adoption and the backlash against it. Big tech companies like Meta are swapping people for models, while brands like Heineken, Aerie, and Polaroid are turning “made by humans” into a marketing flex.

It’s ironic — the more powerful AI gets, the more valuable human creativity becomes. What used to be standard is suddenly a selling point.

Do you think this anti-AI wave is just a phase, or the start of a bigger shift in what we call “authentic”?

GitHub’s new Mission Control pulls all your AI coding agents into one view. You can track what they’re working on, check their progress, and stop them before they break something you’ll have to fix later.

🔥 Our Take: Developers asked for smarter tools and ended up with a small army of half-autonomous interns. This update doesn’t make them smarter, but at least now you can see what they’re up to before they crash the repo.

Parallax is an open-source framework that lets you run large language models across multiple devices, your own or shared with others. It distributes workloads so even smaller machines can handle big models together, no cloud required.

🔥 Our Take: Everyone talks about “local AI,” but it’s always just one lonely laptop gasping through a 7B model. This connects the dots, or GPUs, and makes it feel like you actually own some power again.