I don't even know what vector addition should look like.
I think you're trying to imply you're inventing something new and racket enables you to explore... But what I read (as someone with a PhD in deep learning that has worked on sparsity) is you actually don't know the prior art and you're using racket as an excuse to reinvent a whole bunch of stuff that already exists in plenty of mature libraries in more mundane languages (including python/pytorch). Which is of course fine for personal growth but please don't oversell racket as a "superpower" - to wit I can manipulate any part of my stack too because it's all written in cpp.
Unlike their binary counterparts, posits and takums, tekums simultaneously accommodate both ∞ and NaR, while retaining the simplicity of negation by flipping the underlying trit string. Perhaps most strikingly, tekums enable rounding by truncation, a property that eradicates at a stroke some notorious problems of rounding in binary arithmetic: double rounding errors, cascading carries in hardware, and the attendant inefficiencies.
You do realise that you need to store arbitrary binary blobs which don't nicely align to memory words?
And that once you can store them you need to write custom functions that do bitwise manipulation on those arbitrary blocks of memory?
The stuff that's done in hardware for you on all binary fp?
Meanwhile in racket I got arbitrary balanced ternary manista and exponent precision in less time it took to write this post. Something that not available in C/Cpp even for binary fp?
You do realise that you need to store arbitrary binary blobs which don't nicely align to memory words?
And that once you can store them you need to write custom functions that do bitwise manipulation on those arbitrary blocks of memory?
Yes what part of my response to you gave you the impression that I did not?
Meanwhile in racket I got arbitrary balanced ternary manista and exponent precision in less time it took to write this post.
Your claim was that it could not be done in cpp, not that it was faster/simpler/whatever-new-goalpost-you're-now-presenting in racket.
less time it took to write this post
An interpreted language with a runtime and a GC is easier to use than a systems language? I think this novel discovery is worth a turing award indeed! I'll be sure to refer you for one. Maybe even an honorary doctorate at my alma mater.
This is a complete tangent, but since you mentioned MNIST: I accidentally discovered Tsetlin machines this week when someone on r/Julia asked if anyone with an AMD GPU could run the benchmark in their package called Tsetlin.jl. I've got an AMD GPU so I was happy to oblige. Then I looked at what the benchmark was doing: it was training an MNIST classifier to 98% accuracy in 9 seconds - that seemed like a couple of orders of magnitude too fast. I was flabbergasted and wondered what the heck this thing was and that's when I learned about Tsetlin machines. I went on (with the help of Claude) to implement one in an FPGA and again was flabbergasted when it only took 2k LUTs to implement a Tsetlin machine for MNIST classification in hardware.
Well yes, you have to use one of the newer mnist variants these days if you want to get anything meaningful. A linear classifier gets something like 87% on the original one.
Comments
I'm doing the learning in racket because the bottleneck is human understanding.
That mnist takes 30 minutes per epoch isn't a worry when I don't even know what vector addition should look like.
I think you're trying to imply you're inventing something new and racket enables you to explore... But what I read (as someone with a PhD in deep learning that has worked on sparsity) is you actually don't know the prior art and you're using racket as an excuse to reinvent a whole bunch of stuff that already exists in plenty of mature libraries in more mundane languages (including python/pytorch). Which is of course fine for personal growth but please don't oversell racket as a "superpower" - to wit I can manipulate any part of my stack too because it's all written in cpp.
I once replaced IEEE 754 floating point numbers in a model by balanced ternary floating point numbers.
It took me 20 minutes.
Tell me how you'd do that in cpp?
lol the same way we implement all of the reduced precision fp8, fp4 types today: by storing them in the corresponding uint:
https://github.com/ggml-org/llama.cpp/discussions/15095
Balanced ternary fp is not a reduced precision type of binary fp: https://arxiv.org/abs/2512.10964
Yes I can read very well - can you?
You do realise that you need to store arbitrary binary blobs which don't nicely align to memory words?
And that once you can store them you need to write custom functions that do bitwise manipulation on those arbitrary blocks of memory?
The stuff that's done in hardware for you on all binary fp?
Meanwhile in racket I got arbitrary balanced ternary manista and exponent precision in less time it took to write this post. Something that not available in C/Cpp even for binary fp?
Yes what part of my response to you gave you the impression that I did not?
Your claim was that it could not be done in cpp, not that it was faster/simpler/whatever-new-goalpost-you're-now-presenting in racket.
An interpreted language with a runtime and a GC is easier to use than a systems language? I think this novel discovery is worth a turing award indeed! I'll be sure to refer you for one. Maybe even an honorary doctorate at my alma mater.
You need to reread the reply they made. They asked how you would do it in cpp, not that it’s not possible.
You could have made your point sufficiently without being condescending.
Racket is not interpreted. It is compiled to machine code. You don't know what you're talking about.
This is a complete tangent, but since you mentioned MNIST: I accidentally discovered Tsetlin machines this week when someone on r/Julia asked if anyone with an AMD GPU could run the benchmark in their package called Tsetlin.jl. I've got an AMD GPU so I was happy to oblige. Then I looked at what the benchmark was doing: it was training an MNIST classifier to 98% accuracy in 9 seconds - that seemed like a couple of orders of magnitude too fast. I was flabbergasted and wondered what the heck this thing was and that's when I learned about Tsetlin machines. I went on (with the help of Claude) to implement one in an FPGA and again was flabbergasted when it only took 2k LUTs to implement a Tsetlin machine for MNIST classification in hardware.
Well yes, you have to use one of the newer mnist variants these days if you want to get anything meaningful. A linear classifier gets something like 87% on the original one.