I’ve often wondered about languages like APL/k, are the programmers actually able to think about problems more efficiently?

Is there a reference which describes how the current architecture evolved? Perhaps from very simple core idea to the famous “all you need paper?”

Otherwise it feels like lots of machinery created out of nowhere. Lots of calculations and very little intuition.

Jeremy Howard made a comment on Twitter that he had seen various versions of this idea come up again and again - implying that this was a natural idea. I would love to see examples of where else this has come up so I can build an intuitive understanding.

Can someone answer CS 101 questions about this please.

I know there are other methods related to matrix factorization, but I’m asking specifically about quantization.

Does quantization literally mean the weight matrix floats are being represented using fewer bits than the 64 bit standard?

Second, if fewer bits are being used, are CPUs able to do math directly on fewer bits? Aren’t CPU registers still 64 bit? Are these floats converted back to 64 bit for math, or is there some clever packing technique where a 64 bit float actually represents many numbers (sort of a hackey simd instruction)? Or do modern CPUs have the hardware to do math on fewer bits?

I’ve tried to understand causal inference several times and failed. Tutorials seem unnecessarily long winded. I wish authors would give simple, to the point examples.

Say I have a simple table of outdoor temperatures and ice cream sales.

What can the machinery of causal inference do for me in this situation?

If it doesn’t apply here, what do I need to add to my dataset to make it appropriate for causal inference? More columns of data? Explicit assumptions?

If I can use causal inference, what can it tell me? If I think of it as a function CA(data), can it tell me if the relationship is actually causal? Can it tell me the direction of the relationship? If there were more columns, could it return a graph of causal relationships and their strength? Or do I need to provide that graph to this function?

I know a wet pavement can be caused by rain or spilled water or that an alarm can go off due to an earthquake or a burglary. I have common sense. I also understand the basics of graph traversal from comp sci classes.

How do I practically use causal inference?

To the authors of future articles on this (or any technical tutorial), please explain the essence, the easy path, then the caveats and corner cases. Only then will abstract philosophizing make sense.

Can someone help me understand where tools like this fit?

Are they Tableau dashboard replacements?

Are the better than a standard bootstrap admin theme?

A website which allows you to practice SQL, without having to install databases on your machine: http://sqlforever.com/

I’m eventually planning on o do more with it, but need some free time in my life.

FIX Parser: https://fixparser.targetcompid.com/

A website which allows people in financial trading companies to more easily understand the FIX protocol.

Obviously this is a very niche app, but very useful! It is somewhat well known in the industry (among the type of people who use FIX).

Amusingly, recently a friend forwarded me a website, run by a prestigious financial software company, which is CLEARLY a copy of my website! They are marketing their site on LinkedIn and, I’m sure, other places.

I keep thinking of developing this firther. I have several ideas, just lack the time.

Unfortunately most of the comments are about site reliability.

This used to be an absolutely fantastic forum. I was a young comp sci graduate who somehow finished school without taking any programming language theory courses. I used to read this every single day. At one point I had every book ever written on ML (ocaml, sml, etc) and most written about various lisps. To this day I love how TAPL was written (Types and Programming Languages by Pierce). I loved the expansive nature of Concepts, Techniques, and Models of Computer Programming by Van Roy. Some books were discussed so often that they were simply referred to by their abbreviations.

There were serious academics, PHD students, industry folks and newbies like myself who could not even understand most abstracts, much less the full papers.

I once asked if a new forum could be created for novices like myself so I could ask my dumb little questions. I was instead encouraged to ask my questions in the main forum :)

For a short while there was a related user group in NYC where people would discuss type theory at random diners.