Claudemacs looks really good -- thanks for the pointer!
My main design goal was to have the whole chat described by a text buffer, that can be converted back and forth to the Claude dialog format. I love Emacs' built-in `shell-mode` because I can jump around in the shell buffer, kill and yank, etc. I wanted that same interaction model in the agent interface. I ended up writing a tree-sitter grammar and maybe going a bit over the top...
Also, I wanted something very hackable, where the entire codebase is in elisp. Claudemacs has the benefit of building on Claude Code, so it gets all the Claude Code features for free, whereas I have to implement everything from scratch. But I think of Greger as an experiment surface for novel agent patterns.
I don't think anyone has really figured out how to get the most of out agents yet, so it's great that we're all attacking it from different angles and taking inspiration from each other.
MusicGen is an LLM on top of EnCodec tokens, instead of working directly with audio. EnCodec is neural audio compression algorithm that encodes audio as tokens from a codebook. It's a really clever trick!
There was a typo in the readme, thanks for pointing this out! I add 8 channels (4 mask + 4 masked chorales). The chorales are transformed into 4-dimensional arrays, each channel representing a part of the piece. I've added some example plots to the readme to illustrate.
I'm sure your script runs a lot faster than my model :D A well-tuned heuristic script can probably do as good harmonizing as any black box deep learning model. I was mostly just curious how diffusion models would handle symbolic music data. This model does reasonably well on short time scales but has no idea about long-term context.
Andreas, author of the Replicate model here -- though "author" feels wrong since I basically just stitched two amazing models together.
The thing that really strikes me is that open source ML is starting to behave like open source software. I was able to take a pretrained text-to-image model and combine it with a pretrained video frame interpolation model and the two actually fit together! I didn't have to re-train or fine tune or map between incompatible embedding spaces, because these models can generalize to basically any image. I could treat these models as modular building blocks.
It just makes your creative mind spin. What if you generate some speech with https://replicate.com/afiaka87/tortoise-tts, generate an image of an alien with Stable Diffusion, and then feed those two into https://replicate.com/wyhsirius/lia. Talking alien! Machine learning is starting to become really fun, even if you don't know anything about partial derivatives.
Great questions! At the moment we recommend passing dataset URIs as params to replicate.init(): https://replicate.ai/docs/guides/training-data, but of course this assumes immutable and stable URIs.
Hi, Andreas here. Yes spreadsheets are great, and better than notebooks in many cases. But I always felt like I was doing something wrong when I used spreadsheets and markdown files to manually record metrics and hyperparameters for my experiments. It's error prone and easy to forget to update the spreadsheet with new experiments.
So we're trying to automate recording this metadata, but then give you that metadata in various ways for you to inspect it. One of those ways is actually spreadsheets: https://github.com/replicate/replicate/issues/289
My main design goal was to have the whole chat described by a text buffer, that can be converted back and forth to the Claude dialog format. I love Emacs' built-in `shell-mode` because I can jump around in the shell buffer, kill and yank, etc. I wanted that same interaction model in the agent interface. I ended up writing a tree-sitter grammar and maybe going a bit over the top...
Also, I wanted something very hackable, where the entire codebase is in elisp. Claudemacs has the benefit of building on Claude Code, so it gets all the Claude Code features for free, whereas I have to implement everything from scratch. But I think of Greger as an experiment surface for novel agent patterns.
I don't think anyone has really figured out how to get the most of out agents yet, so it's great that we're all attacking it from different angles and taking inspiration from each other.