Zygote.jl VS hof

If you look at Julia open source projects you'll see that the projects tend to have a lot more contributors than the Python counterparts, even over smaller time periods. A package for defining statistical distributions has had 202 contributors (https://github.com/JuliaStats/Distributions.jl), etc. Julia Base even has had over 1,300 contributors (https://github.com/JuliaLang/julia) which is quite a lot for a core language, and that's mostly because the majority of the core is in Julia itself.

This is one of the things that was noted quite a bit at this SIAM CSE conference, that Julia development tends to have a lot more code reuse than other ecosystems like Python. For example, the various machine learning libraries like Flux.jl and Lux.jl share a lot of layer intrinsics in NNlib.jl (https://github.com/FluxML/NNlib.jl), the same GPU libraries (https://github.com/JuliaGPU/CUDA.jl), the same automatic differentiation library (https://github.com/FluxML/Zygote.jl), and of course the same JIT compiler (Julia itself). These two libraries are far enough apart that people say "Flux is to PyTorch as Lux is to JAX/flax", but while in the Python world those share almost 0 code or implementation, in the Julia world they share >90% of the core internals but have different higher levels APIs.

If one hasn't participated in this space it's a bit hard to fathom how much code reuse goes on and how that is influenced by the design of multiple dispatch. This is one of the reasons there is so much cohesion in the community since it doesn't matter if one person is an ecologist and the other is a financial engineer, you may both be contributing to the same library like Distances.jl just adding a distance function which is then used in thousands of places. With the Python ecosystem you tend to have a lot more "megapackages", PyTorch, SciPy, etc. where the barrier to entry is generally a lot higher (and sometimes requires handling the build systems, fun times). But in the Julia ecosystem you have a lot of core development happening in "small" but central libraries, like Distances.jl or Distributions.jl, which are simple enough for an undergrad to get productive in a week but is then used everywhere (Distributions.jl for example is used in every statistics package, and definitions of prior distributions for Turing.jl's probabilistic programming language, etc.).

I think julia has a lot going for it. I feel like autograd is one of the bigger ones given that it's a language feature basically (https://github.com/FluxML/Zygote.jl for reference). I think the ecosystem is a bit of an uphill battle though.

Also check out https://github.com/FluxML/Zygote.jl which is the AD engine

> There's sadly no performant autodiff system for general purpose Python.

Like there is for general purpose Julia? (https://github.com/FluxML/Zygote.jl)

Thanks again. Like you said it is fun to dream (ask the "Scheme Machine" guys sometime about how they would go about it now), but practically with technology like Julia's Zygote:

https://github.com/FluxML/Zygote.jl

the efficiency of autodiff might be similar to that of an opcode anyway.

So, how did DEC do on the Alpha processor? I always heard good things about it--IIRC it was based on the VAX, but 64 bit. I learned PDP-11 assembler at RPI, during their college program for high school students in about 1984. We hand assembled code and really got to know the architecture.

and make that computable. It seems like line 88 --> 90 of this file in Zygote does that: https://github.com/FluxML/Zygote.jl/blob/master/src/compiler/chainrules.jl

It's super powerful

For example Zygote.jl (https://github.com/FluxML/Zygote.jl) implements reverse mode automatic differentiation, by defining a function that is a generated transformation of the function being differentiated.

Many business analysts use SQL, have for a long time. They are probably not your target audience. With the problem being JAVA specific, you'd likely want to start there

This sounds similar to the goals of my hof tool (https://github.com/hofstadter-io/hof), lift type definitions out of code so they can be defined in one place, then generate the code for all the places. Is that sounding like what you are after?

Having built something similar, the biggest challenge for users is that they have to use a bespoke language, like WASP here. I suspect that it is also your biggest challenge as well.

Mine is built on CUE, which at least has the potential to become a more widely used language. CUE hasn't reached sufficient maturity for broader adoption yet, so I continue to face this same problem.

https://github.com/hofstadter-io/hof

Here's a big one I needed to get ChatGPT to do something more sophisticated with a JSON object response (predates functions and all that)

https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

It no longer worked after a model update some time ago, haven't tried recently.

I found codellama to be much better for this and require fewer instructions, an anecdotal validation for smaller, focussed models

I was surprised out how you can define arbitrary grammars using arbitrary formulation and it would follow it. Of course you have to redo the prompt every time there is an update... such a pain

https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

CUE is indeed a beautiful language, will get those mind juices flowing for sure!

There is more work to be done on the codec implementation, but if you just want to split yaml/json across files, CUE is a great option

You might also like my project, built on CUE: https://github.com/hofstadter-io/hof We have a TUI where you can explore and work with CUE, JSON, Yaml

You can pretty much make up any pseudo grammar like this one, which is a reduced JSON object that is close to CUE: https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

No need to be formal or use a standard format

Yea, in particular for this project, they have created a bespoke templating system.

You can get the same thing with Go text/templates by adding chat function(s) as custom a helper: https://github.com/hofstadter-io/hof/blob/_dev/lib/templates...

100% one of the best things about building a TUI is not having the pain of modern web development. I do think there is a way to have a CLI & TUI come from the same code, so you can get the best of both, or pick the best for the task at hand.

experiments in progress here: https://github.com/hofstadter-io/hof/tree/_dev/lib/tui

CUE is another interesting language to use from within Go, and is rather natural, given CUE is implemented in Go, but you can also do way more cool things with CUE via the Go API.

We're using CUE to validate and transform data, as input to code gen, the basis for a DAG task engine, and more

https://cuelang.org | https://pkg.go.dev/cuelang.org/[email protected]/cue | https://cuetorials.com/go-api (learn about CUE)

https://github.com/hofstadter-io/hof (where we are doing these things)

here is one of our early examples: https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...