Hypershell: A Type-Level DSL for Shell-Scripting in Rust | Context-Generic Programming
Discuss on Reddit, Lobsters, and Hacker News. Summary I am thrilled to introduce Hypershell, a modular, type-level domain-specific language (DSL) for writing shell-script-like programs in Rust. Hypershell is powered by context-generic programming (CGP), which makes it possible for users to extend or modify both the language syntax and semantics. Table of Contents Estimated reading time: 1~2 hours
Install Build and Install Usage Quick Start Guide Lazy Evaluation Unified Memory Indexing Arrays Saving and Loading Arrays Function Transforms Compilation Conversion to NumPy and Other Frameworks Distributed Communication Using Streams Exporting Functions Examples Linear Regression Multi-Layer Perceptron LLM inference Python API Reference Array mlx.core.array mlx.core.array.astype mlx.core.array.a
Regex engine internals as a library - Andrew Gallant's Blog
Over the last several years, I’ve rewritten Rust’s regex crate to enable better internal composition, and to make it easier to add optimizations while maintaining correctness. In the course of this rewrite I created a new crate, regex-automata, which exposes much of the regex crate internals as their own APIs for others to use. To my knowledge, this is the first regex library to expose its interna
Memoization via Representables
What is the most basic container type a language can have? Some people may answer vectors, others would go with hash tables, but in this post I am arguing in favor of functions. Yes, functions. Even though they aren’t generally seem as a data structure per se, we will see that most containers are in fact a way to represent a function with a given storage layout. To illustrate this “functions are c
Book Review: “Quantum Supremacy” by Michio Kaku (tl;dr DO NOT BUY)
The Blog of Scott Aaronson If you take nothing else from this blog: quantum computers won't solve hard problems instantly by just trying all solutions in parallel. Also, please read Zvi Mowshowitz's masterpiece on how to fix K-12 education! Update (June 6): I wish to clarify that I did not write any of the dialogue for the “Scott Aaronson” character who refutes Michio Kaku’s quantum computing hype
Home > Computer Vision > Mastering All YOLO Models from YOLOv1 to YOLOv12: Papers Explained (2025) What is YOLO? You Only Look Once (YOLO): Unified, Real-Time Object Detection is a single-stage object detection model published at CVPR 2016, by Joseph Redmon, famous for having low latency and high accuracy. The entire YOLO series of models is a collection of pioneering concepts that have shaped tod
The Little Book of Deep Learning
The Little Book of Deep Learning François Fleuret François Fleuret is a professor of computer sci- ence at the University of Geneva, Switzerland. The cover illustration is a schematic of the Neocognitron by Fukushima [1980], a key an- cestor of deep neural networks. This ebook is formatted to fit on a phone screen. Contents Contents 5 List of figures 7 Foreword 8 I Foundations 10 1 Machine Learnin
FlexAttention: The Flexibility of PyTorch with the Performance of FlashAttention – PyTorch
Blog FlexAttention: The Flexibility of PyTorch with the Performance of FlashAttention In theory, Attention is All You Need. In practice, however, we also need optimized attention implementations like FlashAttention. Although these fused attention implementations have substantially improved performance and enabled long contexts, this efficiency has come with a loss of flexibility. You can no longer
Graph ML in 2022: Where Are We Now? | Towards Data Science
Thoughts and Theory, State of the Art Digest It’s been quite a year for Graph ML – thousands of papers, numerous conferences and workshops… How do we catch up with so many cool things happening around? Well, we are puzzled as well and decided to present a structured look at Graph ML highlighting 🔥 trends and major advancements. The image was generated by ruDALL-E with a prompt "graphs floating in
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MLX — MLX 0.30.1 documentation
Mastering All YOLO Models from YOLOv1 to YOLOv12