luvdb VS agi-pack

https://luvdb.com

https://github.com/huangziwei/luvdb

A self-hosted media cataloging web app, with which one can mark the books, movies, music and games one likes, along with some basic social networking features such as microblogging, normal blogging, bookmarking and curating stuffs into lists. It's just me trying out ideas in an highly self-indulgent way.

I spent 86 days with ChatGPT-4 and made a Goodreads clone (kinda) that I always want to have: https://github.com/huangziwei/luvdb

I have near-zero web dev experience before this project. After about a month, I started to rely on it less and less. But I still use it to debug when I hit the wall.

Build your own tool if there isn't one. I used to think it's impossible as I barely know web dev, but in 2023, with the help of ChatGPT, I managed to build my own Goodreads alternative (well, it's actually more than a Goodreads alternative) in two months:

https://github.com/huangziwei/luvdb

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.