RVS_ParseXMLDuration
zillion
RVS_ParseXMLDuration | zillion | |
---|---|---|
2 | 11 | |
1 | 155 | |
- | - | |
1.9 | 7.2 | |
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
zillion
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Let's Talk about Joins
I've also been frustrated when testing out tools that kinda keep you locked into one predetermined view, table, or set of tables at a time. I made a semantic data modeling library that puts together queries (and of course joins) for you as it uses a drill-across querying technique, and can also join data across different data sources in a secondary execution layer.
https://github.com/totalhack/zillion
Disclaimer: this project is currently a one man show, though I use it in production at my own company.
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Ask HN: Show me your half baked project
https://github.com/totalhack/zillion
Semantic data warehousing and analytics tool written in python. It has experimental/half-baked NLP features to query your warehouse by interacting with the semantic layer with AI, instead of the normal approach of having an LLM write SQL and needing to know your entire schema.
- So I watched a few videos about Fabric, and started to cry a little...
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Zillion - Semantic data modeling and analytics with a sprinkle of AI
Hey All, I wanted to share Zillion -- an open source Python data modeling and analytics library with experimental natural language features powered by OpenAI, LangChain, and Qdrant. Zillion acts as a semantic layer on top of your data, writes SQL so you don't have to, and easily bolts onto existing database infrastructure via SQLAlchemy Core.
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Ask HN: Most interesting tech you built for just yourself?
Built it for me, but available to all -- Zillion: a python data modeling and analytics library.
https://github.com/totalhack/zillion
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Zillion - Data modeling and analytics with a sprinkle of AI
More details/docs can be found in the GitHub repo: https://github.com/totalhack/zillion
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πΌπ¬ BabyDS: An AI powered Data Analysis pipeline
Nice work. I had considered implementing something similar in https://github.com/totalhack/zillion down the road, probably as a layer on top.
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Ask HN: Those making $0/month or less on side projects β Show and tell
Zillion: https://github.com/totalhack/zillion
A python data warehousing / modeling / analytics library that can unify multiple datasources and writes SQL for you. It's alpha level at the moment and I just slowly chip away when time allows, though I'm using it in production in another project (which does make money).
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Replacing a SQL analyst with 26 recursive GPT prompts
This seems fun, but certainly unnecessary. All of those questions could be answered in seconds using a warehouse tool like Looker or Metabase or https://github.com/totalhack/zillion (disclaimer: I'm the author and this is alpha-level stuff, though I use it regularly).
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PRQL a simple, powerful, pipelined SQL replacement
At first glance this seems more confusing, particularly the grouping/aggregation syntax, though I suppose that's something I'd just get used to. Some of the syntactic sugar is nice, but some things are also unlike SQL for no apparent reason which just makes adoption harder than necessary (join syntax for example).
IMO the main selling point would be the "database agnostic" part, but I already achieve that through SQLAlchemy Core and/or a warehouse layer like https://github.com/totalhack/zillion (disclaimer: I'm the author and this is alpha-level stuff, though I use it regularly). It seems like many newer DB technologies/services I'd want to use either speak PostgreSQL or MySQL wire protocol anyway.
The roadmap is worth a read, as it notes some limitations and expected challenges supporting the wide variety of DBMS features and syntax. That said, I can see where this might be useful in the cases where I do have to jump into direct SQL, but want the flexibility to easily switch the back end DB for that code -- that's assuming it can cover the use cases that forced me to write direct SQL in the first place though.
What are some alternatives?
laminarmq - A scalable, distributed message queue powered by a segmented, partitioned, replicated and immutable log.
sqlglot - Python SQL Parser and Transpiler
typocide - Where Typos Meet Their Demise!
endoflife.date - Informative site with EoL dates of everything
ukey - Simple ukulele chord reference web app
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
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.
objectiv-analytics - Open-source product analytics infrastructure for data teams that want full control. Built for high quality data collection and ready to use for advanced analytics & ML.
speech - A tool to practice English speaking
nature - π The Nature Programming Language, may you be able to experience the joy of programming.
quantraserver - Distributed QuantLib
Skytrax-Data-Warehouse - A full data warehouse infrastructure with ETL pipelines running inside docker on Apache Airflow for data orchestration, AWS Redshift for cloud data warehouse and Metabase to serve the needs of data visualizations such as analytical dashboards.