RVS_MediaServer VS agi-pack

Well, these ones aren't "half-baked," but they are no longer being maintained (archived):

[0] https://github.com/RiftValleySoftware/RVS_IPAddress

[1] https://github.com/RiftValleySoftware/RVS_ParseXMLDuration

[2] https://github.com/RiftValleySoftware/RVS_ONVIF

This project is unfinished (I just walked away from it, as it wasn't really giving me what I wanted):

[3] https://github.com/RiftValleySoftware/RVS_GTDriver

This one is "half-baked," I believe. I never really took it particularly far:

[4] https://github.com/RiftValleySoftware/RVS_MediaServer

I did something similar for Mac, a while back[0]. I never really developed it much farther, because of the latency issues. Since it was for surveillance cameras, that was a showstopper.

[0] https://github.com/RiftValleySoftware/RVS_MediaServer

Here’s a fun project I hacked together this weekend.

https://github.com/spillai/agi-pack

TL;DR `agi-pack` is a Dockerfile generator for machine learning (ML) developers that is simple, hackable and extensible. Inspired by Replicate's cog, Baseten's truss, skaffold, and docker-compose services, I wanted a simple and standalone tool that could generate docker images given a simple YAML specification (i.e. python version, system packages, conda/pip dependencies, GPU libraries etc), without any added cruft/dependencies of vendors and services.

To get started, just run `pip install agi-pack` in your virtual env.

Check out the quickstart (https://github.com/spillai/agi-pack#quickstart-) section to see what `agi-pack` can do. If you’re interested in building multiple docker targets, the advanced section (https://github.com/spillai/agi-pack#more-complex-example-) might pique your interest.

Why the name? `agi-pack` is very much tongue-in-cheek -- we are soon going to be living in a world full of quasi-AGI agents orchestrated via ML containers. At the very least, `agi-pack` hopes to provide the building blocks for us to build a more modular, re-usable, and distribution-friendly container format for "AGI". ;)

Fun fact: More than 75% of the original implementation was generated by conversations with GPT-4 + Github Co-pilot.

Let me know what you think. Feel free to reach out / DM if you’d like to build on this work, happy to chat and hear about your use-case. DMs open on Twitter (https://twitter.com/sudeeppillai).

Here’s a fun project I hacked together last weekend — https://github.com/spillai/agi-pack

  TL;DR agi-pack is a Dockerfile generator for machine learning (ML) developers that is simple, hackable and extensible.