Container Diet

@nikuscs The diff is that you can run it locally, no need to copy/paste the whole Dockerfile content. Can analyze your local images as well, layer by layer and main focus is layers, unnecessary packages or build tools, security risks, and multi-stage build suggestions.

@chilarai Awesome! Let me know how much weight (MBs or GBs) your containers manage to lose. 📉

I'd love to hear your feedback once you've given it a spin—especially if the AI dietician gets a little too sassy with your code. Happy optimizing! 🐳

@sarrah me too

@premansh_tomar Good questions!

1. Vs. Generic AIs: You technically can, but you'd be working blind. Standard AIs only see the text you paste. Container Diet connects to your local Docker daemon to inspect the actual built layers. It feeds the AI real data—like specific file sizes, permissions, and hidden cache folders—so the advice is based on reality, not just the Dockerfile syntax.

2. Generating Images: It doesn't generate the image itself (you still run docker build). Think of it as a critic rather than a creator. It analyzes the image you already built, identifies the bloat, and gives you the code changes to shrink it. 📉

@mykyta_semenov_ Thank you! I really appreciate the support. 🙌

@alexcloudstar Haha, glad the vibe resonates! Yes! The AI specifically analyzes your build stages. If you aren't using multi-stage builds (or using them inefficiently), the Dietician will definitely call you out for carrying 'dead weight' like build tools in your final image. But I might have missed adding the caching to the Dietician focus.

⚠ WARNING: Gasp, why aren't you running a multi-stage build? Your final image is just sippin' on all those build-layer calories. 🎯

✓ SUGGESTION: Split your Dockerfile into a build stage and a runtime stage. In the runtime stage,

FROM `scratch` or `alpine`, copy only that lean, mean `myapp` binary and needed assets. You'll peel off layers faster than an onion!

Can't wait to see what it says about your chunky image—hope it survives the roast! 🍗🐳