A general-purpose programming language, focused on simplicity, safety and stability.

Overview
Rivet logo

The Rivet programming language

issues status license

A general-purpose programming language, focused on simplicity, safety and stability.

Rivet's goal is to be a very powerful programming language and at the same time easy to use, with a syntax inspired mainly by Rust and C# (which are the coolest languages I've ever seen), and by other languages such as Python, Lua, TypeScript, etc.

It is true that there are many programming languages and each of them specialized in a field; some with strange syntax in my opinion (like Lisp) and others with a high learning curve, but Rivet tries to be an exception by trying to become a unique language with a clear and simple syntax and a low learning curve.

NOTE: Rivet is still in the development phase.

Features

  • Easy-to-learn syntax: A syntax without overload of unnecessary elements.
  • Easy error handling: Via result types: fn alloc() !.
  • Named and optional arguments: Very useful.
  • Struct inheritance and polymorphism: Traits and Tagged Unions are also supported.
  • Ownership and Borrowing: Rivet has a very simplified version of Rust's Ownership and Borrowing system. The lifetimes are inferred by the compiler.
  • Templates: Reuse (and specialize) the same code for different types!.

Dependencies

The Rivet compiler currently generates C code, so a C compiler, which supports C11, is required to generate executables. Over time the compiler will add support for generating binaries directly without the need for a C compiler.

Run the compiler

NOTE: To run the compiler you must have Python 3.

NOTE: Only LINUX for now.

Just execute python3 rivetc.py.


Copyright (C) 2022 The Rivet Team

Owner
The Rivet programming language
A general-purpose programming language, focused on simplicity, safety and stability.
The Rivet programming language
High level network definitions with pre-trained weights in TensorFlow

TensorNets High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 = TF = 1.4.0). Guiding principles Applicability.

Taehoon Lee 1k Dec 13, 2022
This toolkit provides codes to download and pre-process the SLUE datasets, train the baseline models, and evaluate SLUE tasks.

slue-toolkit We introduce Spoken Language Understanding Evaluation (SLUE) benchmark. This toolkit provides codes to download and pre-process the SLUE

ASAPP Research 39 Sep 21, 2022
Reinforcement learning library in JAX.

Reinforcement learning library in JAX.

Yicheng Luo 96 Oct 30, 2022
Easy-to-use library to boost AI inference leveraging state-of-the-art optimization techniques.

NEW RELEASE How Nebullvm Works • Tutorials • Benchmarks • Installation • Get Started • Optimization Examples Discord | Website | LinkedIn | Twitter Ne

Nebuly 1.7k Dec 31, 2022
OneShot Learning-based hotword detection.

EfficientWord-Net Hotword detection based on one-shot learning Home assistants require special phrases called hotwords to get activated (eg:"ok google

ANT-BRaiN 102 Dec 25, 2022
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

Intro This repository contains code to generate data and reproduce experiments from our NeurIPS 2019 paper: Boris Knyazev, Graham W. Taylor, Mohamed R

Boris Knyazev 242 Jan 06, 2023
Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

Discretization Robust Correspondence Benchmark One challenge of machine learning on 3D surfaces is that there are many different representations/sampl

Nicholas Sharp 10 Sep 30, 2022
Repository of the paper Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models at ML4AD @ NeurIPS 2021.

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models Code and supplementary materials Repository of the p

Daniel Bogdoll 4 Jul 13, 2022
Lightweight stereo matching network based on MobileNetV1 and MobileNetV2

MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching

Cognitive Systems Research Group 139 Nov 30, 2022
This repo contains implementation of different architectures for emotion recognition in conversations.

Emotion Recognition in Conversations Updates 🔥 🔥 🔥 Date Announcements 03/08/2021 🎆 🎆 We have released a new dataset M2H2: A Multimodal Multiparty

Deep Cognition and Language Research (DeCLaRe) Lab 1k Dec 30, 2022
Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

Conditional Smiles! (SmileCVAE) About Implementation of AE, VAE and CVAE. Trained CVAE on faces from UTKFace Dataset. Using an encoding of the Smile-s

Raúl Ortega 3 Jan 09, 2022
Code for the Paper: Alexandra Lindt and Emiel Hoogeboom.

Discrete Denoising Flows This repository contains the code for the experiments presented in the paper Discrete Denoising Flows [1]. To give a short ov

Alexandra Lindt 3 Oct 09, 2022
Mengzi Pretrained Models

中文 | English Mengzi 尽管预训练语言模型在 NLP 的各个领域里得到了广泛的应用,但是其高昂的时间和算力成本依然是一个亟需解决的问题。这要求我们在一定的算力约束下,研发出各项指标更优的模型。 我们的目标不是追求更大的模型规模,而是轻量级但更强大,同时对部署和工业落地更友好的模型。

Langboat 424 Jan 04, 2023
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph

VITA 101 Dec 29, 2022
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
People log into different sites every day to get information and browse through these sites one by one

HyperLink People log into different sites every day to get information and browse through these sites one by one. And they are exposed to advertisemen

0 Feb 17, 2022
Official Implementation of "Learning Disentangled Behavior Embeddings"

DBE: Disentangled-Behavior-Embedding Official implementation of Learning Disentangled Behavior Embeddings (NeurIPS 2021). Environment requirement The

Mishne Lab 12 Sep 28, 2022
Code for "Localization with Sampling-Argmax", NeurIPS 2021

Localization with Sampling-Argmax [Paper] [arXiv] [Project Page] Localization with Sampling-Argmax Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-

JeffLi 71 Dec 17, 2022
Customised to detect objects automatically by a given model file(onnx)

LabelImg LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Annotations are saved as XML

Heeone Lee 1 Jun 07, 2022
A3C LSTM Atari with Pytorch plus A3G design

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

David Griffis 532 Jan 02, 2023