mation-spec
stat4701
| mation-spec | stat4701 | |
|---|---|---|
| 4 | 1 | |
| 6 | 2 | |
| - | - | |
| 4.4 | 10.0 | |
| 4 months ago | about 9 years ago | |
| JavaScript | R | |
| Apache License 2.0 | - |
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mation-spec
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Show HN: FoldMation – An Interactive Origami Learning and Creation Application
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.
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Ohm: A library and language for building parsers, interpreters, compilers, etc.
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
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Replit's new Code LLM was trained in 1 week
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
stat4701
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Replit's new Code LLM was trained in 1 week
My favorite line from the HumanEval paper
> It is important for these tasks to be hand-written, since our models are trained on a large fraction of GitHub, which already contains solutions to problems from a variety of sources.
So to answer your question, yes, the evaluation dataset is spoiled. You can find such unique and never before seen docstrings like
> For a given list of input numbers calculate the Mean Absolute Deviation around the mean of this dataset. Mean Absolute Deviation is the absolute difference between each element and a centerpoint (mean in this case)[0]
And here's a repo I found that is 8 years old[1]. But how about a more recent one that is even closer?[2] There's plenty more examples[3] (does anyone know how actually limit the date to prior to 2021? `pushed:<2021` doesn't work nor does using the `created` keyword. Date searching doesn't seem to work well).
[0] https://github.com/openai/code-align-evals-data/blob/97446d9...
[1] https://github.com/bertomartin/stat4701/blob/ec2b64f629cbbf6...
[2] https://github.com/danielwatson6/hate-speech-project/blob/64...
[3] https://github.com/search?q=abs%28x+-+mean%29+for+language%3...
What are some alternatives?
IF
ReplitLM - Inference code and configs for the ReplitLM model family
code-align-evals-data
trax - Trax — Deep Learning with Clear Code and Speed
fauxpilot - FauxPilot - an open-source alternative to GitHub Copilot server
hate-speech-project
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.