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I am looking into a way to replace my google reader experience that I had back in 2013 before google took it from us. I am starting by learning how to parse feeds with python, and without much previous knowledge, it proved to be much easier than anticipated thanks to the feedparser library.

This is how I used python to parse rss and setup my own custom feed.

Install the feedparser library.

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The input will be a yaml file containing a list of Items you want to stay up to date with. Inside each item will be a url, and weight.

email: max-entries: 10 recipients: - [email protected] markdown: max-entries: 100 output: - README.md json: max-entries: 1000 output: - feeds/feed.json rss: max-entries: 1000 output: - feeds/feed.xml html: max-entries: 100 output: index.html items: Waylon Walker: weight: 5 url: https://waylonwalker.com/rss.xml @_WaylonWalker: weight: 3 twitter: https://twitter.com/_waylonwalker DEV Waylon Walker: weight: 8 url: https://dev.to/waylonwalker Stack Overflow Kedro: weight: 5 url: https://stackoverflow.com/questions/tagged/kedro Kedro GitHub: weight: 4 url: https://github.com/kedro-org/kedro Kedro Pypi weight: 10 url: https://pypi.org/project/kedro/

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• rss feed (primary source)
• youtube feed
• Stack Overflow tags
• GitHub...

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I ran this post on dev.to and got a great response of great examples, check it out.

The GitHub profile feature just went live for a subset of users. Simply creating a repo named after your username, and clicking share to Profile on the sidebar will create a custom profile that shows up just above your pinned projects.

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Can someone explain how or why twitter cards render differently from device to device? I do understand that twitter cards a built from meta tags, the full list can be found in their docs

Mobile Looks fine.

On Desktop it is not picking up the image.

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I ran a discussion on dev that collected quite a list of examples in the comment section. So many great calls to action, animations, memes, and weird tricks.

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Python *args and **kwargs are super useful tools, that when used properly can make you code much simpler and easier to maintain. Large manual conversions from a dataset to function arguments can be packed and unpacked into lists or dictionaries. Beware though, this power can lead to some really unreadable/unusable code if done wrong.

I generally post these as a carousel on LinkedIn based on a full article. Let mw know what you think of it shown inside of a blog @_waylonwalker.

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While using the catalog alone will not reap all of the benefits of the framework, it does get you and your project ready for the full framework eventually. For me the full benefit of the catalog comes when you combine it with the pipeline and dont even touch read/write steps at all.

Taking a step into kedro by adopting the catalog first will give you a way to organize all of your data loads in one place, and stop manually writing read/write code, which can be different for each data and storage type. You just don’t need to think about it.

“can be dropped into kedro later” Let’s talk a bit more about that

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