snakemake VS tldr

Last year, my pull requests were on my own projects: two to for my Apple Watch telemetry recording app, one was for a workout Watch app, and the third was for the research project I was working on at the moment (this paper has since been published, "The origins and genetic interactions of KRAS mutations are allele- and tissue-specific" and the code is open source). This year, in order to enhance my learning, I challenged myself to contribute to others' projects. In my opinion, I have been quite successful with contributions to Fig, tldr, snakemake, and pymc3-examples. In addition, I have also taken up the challenge of learning about Appwrite, one of the sponsors of this year's Hacktoberfest, and producing educational content about the service. (With some encouragement by the offer of free stickers 🙃) I found this a great opportunity to learn about an essential world of programming that I had yet to deal with: backend services.

If you’re into data science, have you taken a look at Snakemake? I came across this quite recently, and now, I’m wondering why I hadn’t used it before (it really would’ve come in handy to automate a few data-wrangling processes).

Maybe this already helps: https://github.com/tldr-pages/tldr

A bit of an aside, but I really like "guides to things we otherwise take for granted". So few man pages are built around example use cases, but those are often what make the case for a tool!

A similar spirit to projects like https://github.com/tldr-pages/tldr/ , but this has a lot more useful detail.

The ripgrep author has a blog post on performance and benchmarking that is an interesting read in itself: https://blog.burntsushi.net/ripgrep/

Looks like bro pages is archived and they recommend https://github.com/tldr-pages/tldr or https://github.com/cheat/cheat

a very excellent tool to grab is TLDR https://tldr.sh/

And that's why tldr is such a powerful tool! You can easily install it with sudo apt install tldr or follow this demo.