mation-spec VS code-align-evals-data

Hi, I've created an application where you can follow step by step origami fold instructions, and a Creator where you can make these interactive folds.

On comparing to video instructions, you have the ability to quickly skip/rewind steps and replay a complicated step many times.

On the creation side, there have been one or two attempts at this before, but those solutions rely on mouse drags as the user interface. This greatly limited the kinds of folds possible. The foldMation Creator uses commands, keywords and values to compose a domain specific language/step and provides a (relatively speaking) easy to use user interface to compose the steps.

For those interested in using the Creator, please go through the tutorial at the top of the create page.

Btw, the DSL for foldMation uses https://github.com/mationai/mation-spec. I created it since I couldn't find anything out there that is similar, allowing me to specify a well structured data with English-like readable syntax.

Let me know what you think?

The DSL for foldMation uses https://github.com/mationai/mation-spec . I created it since I couldn't find anything out there that is similar, allowing me to specify a well structured data with English-like readable syntax.

Ohm is a wonderful tool. I used it to create mation-spec [0], a readable structured configuration and specification format to automate and run code. I look hard trying to find something like it before giving up and creating one myself with the help of Ohm. The mation-spec is the basis of an origami fold simulation language to describe and simulate origami folds. PM me if you like to see it before I post the simulator on HN.

[0] https://github.com/mationai/mation-spec

Have you thought of finding or creating something like this [0]?

I created this as the basis for my origami folding descriptive language. I tried to find something similar, requirements being both well structured and English-like but couldn't find any, so I created it.

The origami folding app will hopefully be out in 2 weeks, so you can see how it's used.

[0] https://github.com/fuzzthink/mation-spec

deduplication. We first split the files into words/tokens based on non-alphanumeric characters and remove files with fewer than 10 tokens. Next, we compute the MinHash with 256 permutations of all documents, and use Locality Sensitive Hashing to find clusters of duplicates. We further reduce these clusters by ensuring that each file in the original cluster is similar to at least one other file in the reduced cluster. We consider two files similar when their Jaccard similarity exceeds 0.85.

Near-duplicates are still difficult to measure. So we should expect duplication, and it should be proportional to the number of samples we have (even if the same variance, but I'd wager higher variance with larger duplications).

[0] https://github.com/openai/code-align-evals-data/tree/97446d9...

[1] https://arxiv.org/abs/2211.15533