quine-relay VS mediapipe

If there were more languages, then it'd be `console.log("System.out.println({python_source})")`, etc. The problem then becomes quoting and escaping inner quotes. I managed to avoid the problem by using both single and double quotes, and relying on Python's `repr` also giving valid JS strings, but if I had to add one more language I'd have problems.

I still think the Quine Relay is a tour de force, but for different reasons. It's not 128 quines in different languages, but an incredibly robust system for quoting and escaping strings in 128 different languages.

[1] https://github.com/mame/quine-relay/blob/master/src/code-gen...

One of the most impressive works I've seen: https://github.com/mame/quine-relay

"Quine" is a type of program, when executed, will output itself (it's actually it's source code). It's very hard to write one. And this guy wrote a loop quine. It supposed to work like this: * A program in language A output a program in language B * The program in language B output a program in language C * The program in language C output the same program in language A which we started with

Or a Quine relay.

Why use a couple of languages, when you can use 128 of them simultaneously? https://github.com/mame/quine-relay

Quine relay [1] is to this day the most "I will never understand this" brainfuck project I have ever seen.

[1] https://github.com/mame/quine-relay

Go look at a programming quine and tell me it isn't art.

mediapipe/docs/solutions/pose.md at master · google/mediapipe · GitHub

Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.

You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md

I believe Google already released transformers under an apache 2 license with a patent grant:

https://github.com/google/mediapipe/blob/master/mediapipe/mo...

I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].

For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM

[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...

[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."

[3] - https://developers.google.com/mediapipe/solutions/vision/ima...

I have used mediapipe very successfully in multiple projects and it's very easy to get running. You can choose from many different vision tasks including hand landmarks ( https://github.com/google/mediapipe/blob/master/docs/solutions/hands.md )

I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.

We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.

Medipaipe library link: https://mediapipe.dev/