How do you know this? What do you mean? They are in the top X% of software engineers and computer scientists in terms of ... (some kind of productivity)?
I'm not familiar with Porter, but Wiegley is a heavy contributor to a number of open source projects, is the author of the Ledger plain text accounting system, has been emacs maintainer since 2015, operates his own software consultancy, and is very active in several open source discussion lists. The guy is a dynamo.
Perhaps I should have said "impactful" instead of "productive", but in my experience the two tend to be correlated.
John Wiegley has authored a number of highly used open source tools and libraries. So has Adam Porter (although perhaps not as much as John).
I wasn't comparing them globally with all SW engineers. My point was that they likely have accomplished more than most, if not all, the commenters in this submission. I, personally, would be cautious in criticizing their workflow unless I can demonstrate something better. Otherwise it's just armchair criticism.
Many of these are points I would agree with, except:
> [comparisons to] commenters in this submission
I wouldn’t make claims as to the level of accomplishment here, not least of which because I’m not willing to pick any one definition of accomplishment.
For example, I’ll avoid the temptation to conflate visibility and popularity with productivity.
> be cautious in criticizing their workflow unless I can demonstrate something better
Yes, making assessments based on comparisons against alternatives is important. And I’m inclined to give their approach attention and consideration, especially for people with their mindset and skillset. But generally, I’m not willing to grant any particular level of broader applicability for a given audience.
So I would reframe this discussion as a question: “Which of their practices are likely to work for [me/my team]?”
Like so many things in life, valid generalization is hard. Especially hard when no one knows the correct answer, so instead we assess better and worse. So, reinforcement learning. And RL in complex environments with large action spaces can be very data intensive!
