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#281: ohmyzsh + ohmyposh + mcfly + pls + nerdfonts = wow

Published Thu, Apr 28, 2022, recorded Wed, Apr 27, 2022
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About the show

Sponsored: RedHat: Compiler Podcast

Special guest: Anna Astori

Michael #1: Take Your Github Repository To The Next Level 🚀️

  • Step 0. Make Your Project More Discoverable
  • Step 1. Choose A Name That Sticks
  • Step 2. Display A Beautiful Cover Image
  • Step 3. Add Badges To Convey More Information
  • Step 4. Write A Convincing Description
  • Step 5. Record Visuals To Attract Users 👀
  • Step 6. Create A Detailed Installation Guide (if needed)
  • Step 7. Create A Practical Usage Guide 🏁
  • Step 8. Answer Common Questions
  • Step 9. Build A Supportive Community
  • Step 10. Create Contribution Guidelines
  • Step 11. Choose The Right License
  • Step 12. Plan Your Future Roadmap
  • Step 13. Create Github Releases (know release drafter)
  • Step 14. Customize Your Social Media Preview
  • Step 15. Launch A Website

Brian #2: Fastero

  • “Python timeit CLI for the 21st century.”
  • Arian Mollik Wasi, @wasi_master
  • Colorful and very usable benchmarking/comparison tool
  • Time or Compare one ore more
    • code snippet
    • python file
    • mix and match, even
  • Allows setup code before snippets run
  • Multiple output export formats: markdown, html, csv, json, images, …
  • Lots of customization possible
  • Takeaway
    • especially for comparing two+ options, this is super handy

Anna #3:

  • langid vs langdetect


  • This library is a direct port of Google's language-detection library from Java to Python
  • langdetect supports 55 languages out of the box (ISO 639-1 codes):
  • Basic usage: detect() and detect_langs()
  • great to work with noisy data like social media and web blogs
  • being statistical, works better on larger pieces of text vs short posts


  • hasn't been updated for a few years
  • 97 languages
  • can use Python's built-in wsgiref.simple_server (or fapws3 if available) to provide language identification as a web service. To do this, launch python -s, and access http://localhost:9008/detect . The web service supports GET, POST and PUT.
  • the actual calculations are implemented in the log-probability space but can also have a "confidence" score for the probability prediction between 0 and 1: > from langid.langid import LanguageIdentifier, model > identifier = LanguageIdentifier.from_modelstring(model, norm_probs=True) > identifier.classify("This is a test") > ('en', 0.9999999909903544) - minimal dependencies - relatively fast - NB algo, can train on user data.

Michael #4: Watchfiles

  • by Samual Colvin (of Pydantic fame)
  • Simple, modern and high performance file watching and code reload in python.
  • Underlying file system notifications are handled by the Notify rust library.
  • Supports sync watching but also async watching
  • CLI example
  • Running and restarting a command¶
    • Let's say you want to re-run failing tests whenever files change. You could do this with watchfiles using
    • Running a command: watchfiles 'pytest --lf``'

Brian #5: Slipcover: Near Zero-Overhead Python Code Coverage

  • From twitter account, which I’m pretty sure is Ned Bachelder
  • coverage numbers with “3% or less overhead”
  • Early stages of the project.
  • It does seem pretty zippy though.
  • Mixed results when trying it out with a couple different projects
    • flask:
      • just pytest: 2.70s
      • with slipcover: 2.88s
      • with 4.36s
    • flask with xdist n=4
      • pytest: 2.11 s
      • coverage: 2.60s
      • slipcover: doesn’t run (seems to load pytest plugins)
  • Again, still worth looking at and watching. It’s good to see some innovation in the coverage space aside from Ned’s work.

Anna #6:

  • scrapy vs robox


  • shell to try out things: fetch url, view response object, response.text
  • extract using css selectors or xpath
  • lets you navigate between levels e.g. the parent of an element with id X
  • crawler to crawl websites and spider to extract data
  • startproject for project structure and templates like settings and pipelines
  • some advanced features like specifying user-agents etc for large scale scraping.
  • various options to export and store the data
  • nice features like LinkExtractor to determine specific links to extract, already deduped.
  • FormRequest class


  • layer on top of httpx and beautifulsoup4
  • allows to interact with forms on pages: check, choose, submit




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