sway VS Pytorch

Fuel Labs | Junior and Senior Software Engineers | Full Time | REMOTE only

Fuel Labs is the blockchain industry leader in scaling R&D and is building Fuel, the fastest execution layer for the modular blockchain stack. We're developing blockchain scaling and smart contract development tools. This includes a new blockchain-optimized high-performance virtual machine (the FuelVM) and a new Rust-based DSL (Sway) for developing safer and more efficient smart contracts.

FuelVM specifications: https://github.com/FuelLabs/fuel-specs

Sway: https://github.com/FuelLabs/sway

If you're interested in working with VMs, interpreters, compilers, end-user-facing toolchains, and/or love good error messages from your compiler, look no further.

We're hiring junior and senior software engineers in Rust, TypeScript, and Solidity. No blockchain experience required; complete disdain for blockchains okay.

https://jobs.lever.co/fuellabs

Sway: https://github.com/FuelLabs/sway

If you're interested in working with VMs, interpreters, compilers, end-user-facing toolchains, and/or love good error messages from your compiler, look no further.

We're hiring junior and senior software engineers in Rust, TypeScript, and Solidity. No blockchain experience required; complete disdain for blockchains okay.

https://jobs.lever.co/fuellabs

Sway is a language developed for the Fuel blockchain. doc: The Sway Programming Language.

Smart contracts, mostly. Rust is used for both compilers and as base for DSLs. For instance Ink is a language built on top of Rust with proc macros, and Sway is a language for smart contracts with a compiler written in Rust.

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.