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That's really cool. What resources did you use to learn it?


Q for mortals


The book Q for Mortals is good. It’s available online for free at https://code.kx.com/q4m3/

Also the reference and blog posts are useful: https://code.kx.com/v2/

It’s not as easy to get information as other languages, but the mailing list is super helpful and all the questions I’ve asked have been replied to by paid Kx (the people that make Q) employees:

https://groups.google.com/forum/m/#!forum/personal-kdbplus


Not the person above, but I'd recommend learning APL first. Arthur Whitney (the author of k and q) got started programming by his father and Ken Iverson (the inventor of APL) being friends, and Iverson giving him live demos of APL. Iverson has a lot of amazing resources for learning J (APL's successor) and APL, and learning it will teach you how to be more productive in q, k, J, APL and more!


https://code.jsoftware.com/wiki/Essays/Incunabulum

This is the arthurese micro APL implementation, which inspired the J implementation - although IIRC the design for J was already well on its way at the time (and indeed, Iverson did not consider J a new language, but rather an improved APL dialect, so you could say the design goes back to 1956...)

It likely also formed a draft for A/A+ which Arthur did at Morgan Stanley (open sourced at aplusdev.org, but unmaintained and rotting theese days) - which themselves led to the creation of K.


If I remember correctly, Whitney prototyped J in C and only after that did Iverson turn it into a real language.


I thought so on first glance too, but from what I've heard from someone who worked there, it's working software and it makes a lot of money. That's the opposite of a pipe dream, even if far short of AGI.


Is it making real money, or speculative money on the moon shot premise that AGI will rule it all if successful?

I worked at an AI company before, and it was the latter.


My understanding is mostly the latter, but definitely also the former, but it's based off of "I worked there, but our customer list isn't public so I can't tell you who" type statements like you'll see elsewhere.

If I had to guess what it's been actually used for, I'd wager it's money laundering or counter-terrorism type stuff; it's fairly well suited to finding connections between people and entities given a large data-set, and unlike many ML models, it can tell you why it thinks someone is suspicious, which might be needed for justifying further investigation. This is a completely wild-ass guess though so take with a giant grain of salt.


Yes that was the sort of usecase for the AI company I worked at, similar data mining competing with Palantir.


That's really cool! How did you figure this out and what does this representation look like?


https://docs.microsoft.com/en-us/dotnet/api/documentformat.o...

The OpenXML basis format for XSLX is an ISO standard. Most things are documented albeit in a somewhat obtuse way.


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