This is from 2020. Besides a small change to the "Introduction to the Command Line" section, it has not been updated.<p>Back in 2015, the author reported using CasperJS to scrape public LinkedIn profiles. The author reported this was a PITA.<p>Here the author recommends using WebDriver implementations, e.g., chromedriver or geckodriver, in addition to scripting language frameworks such as Puppeteer and Selenium. Is scraping LinkedIn still a PITA.<p>Because the examples given are always relatively simple, i.e., not LinkedIn, I am skeptical when I see "web scraping" tutorials using Python frameworks and cURL as the <i>only</i> recommended option for automated public data/information retrieval from the www.[FN1,2] I use none of the above. For small tasks like the examples given in these tutorials, the approaches I use are not nearly as sophisticated/complicated and yet they are faster and use fewer resources than using Python and/or cURL. They are also easier to update if something changes. That is in part because (1) the binaries I use are smaller, (2) I do not rely on scripting languages[FN3] and third party libraries (and so much less code involved), (3) the programs I use start working immediately whereas Python takes seconds to start up and (4) compared to the programs I use, cURL as a means of sending HTTP requests is inflexible, e.g., one is limited to what "options" cURL provides and cURL has no option for HTTP/1.1 pipelining.<p>1. LinkedIn's so-called "technological measures" to prevent retrieval of public information have failed. Similarly, its attempts to prevent retrieval of public information through intimidation, e.g., cease-and-desist letters and threats of CFAA claims, have failed. Tutorials on "web scraping" that extol Python frameworks should use LinkedIn as an example instead of trivial examples for which using Python is, IMHO, overkill.<p>2. What would be more interesting is a Rosetta Code for "web scraping" tasks. There are many, many ways to do public data/information retrieval from the www. Using scripting languages such as Python, Ruby, NodeJS, etc. and frameworks are one way. That approach may be ideally suited for large scale jobs, like those undertaken by what the author calls "internet companies". But for smaller tasks undertaken by individual www users for noncommercial purposes, e.g., this author's concept of "scrapism", there are also faster, less complicated and more efficient options.<p>3. Other than the Almquist shell