Wow, alerted by a few pull requests and then pleasantly surprised to see this here.
As tyingq pointed out, this is not a list of "open source" languages, though I don't think it was too off for the OP to add that since indeed most of them are. It's also a bit broader than "programming languages". The list is termed "computer languages". That is the main category and ~75% of the langs, but formats and other things are counted as well (see table below). Even musical notations make an appearance, as I find those relevant to people interested in designing computer languages for music, or visual languages in general. While the focus is on computer languages, I think it's helpful to have a light touch of some of the earlier developments in language in general. So I didn't draw explicit lines, rather the strategy is to keep focus on programming languages with a peripheral view of the bigger picture.
As to accuracy, in general, there are ~420,000 cells in my "spreadsheet". My initial target accuracy was ~98% or so. Gathering the cells was a mixture of manual curation, crawlers, simple NLP models, and contributions from the community.
This project sadly fell by the wayside. I need to decide whether to 1) abandon it and instead just contribute facts as I find them to the relevant pages on Wikipedia or 2) determine if there's a good reason to build a fact site like this outside Wikipedia and if so get it into gear.
Sorry about any inaccuracies and thank you for the feedback (and especially the pull requests!).
Numeral systems are a relatively recent addition and I haven't yet built a special template/models for them. Might be more like 7 billion users and all jobs :)
Thanks! I found that the development of numeral systems is in many ways similar to the development of programming languages, and the "story" is incomplete without including those in the db. It's a more recent development that I didn't expect.
Perhaps I should have 3 categories: active, legacy, historical. Active would be the language itself is still evolving, legacy would be the language has stopped evolving but some people still use it, and historical is there are no known public users.
My current model that predicts that status is very wrong and needs an update. Added to the todo.
> Got some escaping issues here.
Will fix, thanks!
> redundancy
Thanks! Will fix.
> > #include <objpak.h>
Thanks for the note. Added a link to the source for that one.
Thank you for the feedback! The categories are a loose grouping. In the future there should be a lot more columns so will be better ways to cluster and view groupings.
Yes, a year or two ago I grabbed job listings from sites like Indeed and LinkedIn and trained a very crude basket of models. No one ever seemed to use it so I hadn't touched it since then. Time to refresh that, thanks for the feedback! Interesting points about ADA and I'll try and bring in some of those back channels.
A very basic decision tree/random forest model. I haven't updated it since the first version I did about a year or two ago, because I didn't have users myself and no one provided any feedback. Thank you for the feedback! I've made a todo item to refresh/explain the model.
As tyingq pointed out, this is not a list of "open source" languages, though I don't think it was too off for the OP to add that since indeed most of them are. It's also a bit broader than "programming languages". The list is termed "computer languages". That is the main category and ~75% of the langs, but formats and other things are counted as well (see table below). Even musical notations make an appearance, as I find those relevant to people interested in designing computer languages for music, or visual languages in general. While the focus is on computer languages, I think it's helpful to have a light touch of some of the earlier developments in language in general. So I didn't draw explicit lines, rather the strategy is to keep focus on programming languages with a peripheral view of the bigger picture.
As to accuracy, in general, there are ~420,000 cells in my "spreadsheet". My initial target accuracy was ~98% or so. Gathering the cells was a mixture of manual curation, crawlers, simple NLP models, and contributions from the community.
This project sadly fell by the wayside. I need to decide whether to 1) abandon it and instead just contribute facts as I find them to the relevant pages on Wikipedia or 2) determine if there's a good reason to build a fact site like this outside Wikipedia and if so get it into gear.
Sorry about any inaccuracies and thank you for the feedback (and especially the pull requests!).