RVS_ParseXMLDuration
agi-pack
RVS_ParseXMLDuration | agi-pack | |
---|---|---|
2 | 3 | |
1 | 15 | |
- | - | |
1.9 | 8.1 | |
almost 2 years ago | 3 months ago | |
Swift | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
RVS_ParseXMLDuration
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Ask HN: Show me your half baked project
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
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Code Colocation Is King
Not completely. The way that it works for me, is that I start work on a project, and, while building, I notice that some code that I'm working on is:
1) Pretty complex, and fairly insular; and/or
2) Possibly useful, elsewhere.
If that's the case, I will then stop work on the main project, and take some time to extract and "genericize" the subproject. I'll usually set it up as a standalone open-source project; complete with tests and documentation.
This may happen before I have completed the coding in the main project, or may happen as the result of a review, after the fact.
In some cases, I very clearly need to develop a subproject before starting on the main project, or before certain milestones within that project (for example, SDKs or drivers). In that case, the timelines are completely separate.
If you look at my GH repos, you'll see a whole bunch of these projects, including some rather strange ones, like an XML duration parser[0]. These are the types of projects that I extract.
In some cases, I end up not using the extracted project in my main project (happens to some of my UI widgets). In that case, even though I am not using it, I still have an excellent project for the future. Here's an example[1]. I have ended up not using the spinner in my own work, as it was too obtrusive a widget, but it's nice to have it available for future projects.
[0] https://github.com/RiftValleySoftware/RVS_ParseXMLDuration
[1] https://github.com/RiftValleySoftware/RVS_Spinner
agi-pack
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AGI-pack: Dockerfile generator for ML developers
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).
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Ask HN: Show me your half baked project
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.
What are some alternatives?
laminarmq - A scalable, distributed message queue powered by a segmented, partitioned, replicated and immutable log.
luvdb - Your self-hosted inner space
typocide - Where Typos Meet Their Demise!
TOSIOS - The Open-Source IO Shooter is an open-source multiplayer game in the browser
ukey - Simple ukulele chord reference web app
YTBN-Graphing-Software - (Yet-to-be-named) Graphing Software
prepareprojectforllmprompt - Transform your code project into a Markdown document optimized for interaction with Language Learning Models like GPT-4, complete with dynamic file selection and token management features.
RVS_MediaServer - Translating Streaming Video Server (Work In Progress)
speech - A tool to practice English speaking
paperless-ngx - A community-supported supercharged version of paperless: scan, index and archive all your physical documents
quantraserver - Distributed QuantLib
kons-9 - Common Lisp 3D Graphics Project