Programming, Math, Science

This is a list of links to different freely available learning resources about computer programming, math, and science. Use it to build a structured path from fundamentals to hands-on practice.

Editorial note: curated source snapshot published by Collider.club under the MIT License. Source attribution is preserved in the front matter.

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Programming, Math, Science

This is a list of links to different freely available learning resources about computer programming, math, and science.

Table of contents

• AI
  • Machine Learning
    • Large Language Models
    • Machine Learning Online Courses
    • Mathematics for Machine Learning
    • Reinforcement learning
  • Computer Games AI
  • Vibe Coding and Spec-Driven Development
• Algorithms
  • Bloom Filters
  • Date-time
  • Diff Algorithms
• Art
• Biology
• Command Line and Tools
  • Curl
  • Linux command line
  • Nix
• Competitions and Interview Preparation Websites
  • Math
    • Preparation Resources
  • Physics
  • Programming
    • Preparation Resources
• Compilers and Interpreters
  • Static Program Analysis
• Computer Graphics
  • Creative Coding
  • Curves and Surfaces
  • DirectX 12
  • Image Processing
    • Dithering
  • Metal
  • OpenGL
  • Ray Tracing
  • Shaders
  • Vulkan
  • WebGPU
• Computer Networks and Network Programming
• Cryptography
• Data Science
• Debuggers
• Databases
  • Databases Development
  • NoSQL
  • SQL
    • MySQL
    • PostgreSQL
    • Oracle
• Demoscene
• Design Patterns
• DevOps
• Digital Signal Processing
• Distributed Systems
• Electronics
• Emulators and Virtual Machines
• Game Programming
  • Game Engines
  • Game Physics
• General Programming
  • Lists of programming projects to try to implement
• Geographic Information Systems
• GUI Programming
• Hardware
• Information Theory
• IQ Tests
• Logical Games
• Low Level Stuff
  • GPU and TPU programming
  • SIMD programming
• Math
  • Algebra
    • Linear Algebra
  • Analysis
  • Calculus
    • Vector Calculus
  • Category Theory
  • Differential Equations
  • Game Theory
  • General problem solving
  • Geometry
    • Differential Geometry
  • Combinatorics
    • Graph Theory
  • High School Math
  • Mathematical Finance
  • Mathematical Logic
  • Measure Theory
  • Number Theory
  • Numerical Analysis
  • Operations Research
  • Probability and Statistics
  • Proofs
  • Theoretical Computer Science
  • Topology*
• Multithreading and Concurrency
• Music Theory
• Operating Systems
  • Operating Systems Development
• Photography
• Physics
• Programming Languages
  • Ada
  • Assembly
  • Bash
  • Basic
  • C
  • C#
  • C++
  • CMake
  • Cuda
  • D
  • Datalog
  • Erlang
  • Forth
  • Go
    • Network programming in Go
  • Haskell
  • Jai
  • Java
  • JavaScript
  • Lean
  • Lisp dialects
    • Clojure
    • Common Lisp
    • Racket
    • Scheme
  • Nim
  • OCaml
  • Oberon-2
  • Odin
  • Pascal
  • PostScript
  • Prolog
  • Pyret
  • Python
  • R
  • Regular expressions
  • Ruby
  • Rust
  • Smalltalk
  • Ya
  • Zig
• Retrocomputing
  • Advent of Code on retro machines
  • Apple II
  • Commodore 64
  • CP/M
  • DOS
  • Game Boy
  • Mega 65
  • NES
  • ZX Spectrum and Z80 CPU
    • ZX Online Articles
• Reverse engineering
• Robotics
• System programming
• Technical Writing
• Testing
• Text editors
  • Emacs
  • Vim
• Unicode
• Version control tools
• Web programming
  • CSS
  • Django
  • HTML
• Personal Websites and Blogs
  • AI
  • C and C++
  • Game and Graphics Programming
  • Hardware Blogs
  • Math and Physics
  • Mixed Programming
  • Performance and Optimization
  • Web Development
• Other
• Other lists

AI

• Agentic Design Patterns by Antonio Gulli

• Maths, CS & AI Compendium by Henry Ndubuaku

• Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig

Machine Learning

• A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone

• A Brief Introduction to Neural Networks by David Kriesel

• A Comprehensive Guide to Machine Learning by Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, Jennifer Listgarten, Anant Sahai [pdf]

• A Course in Machine Learning by Hal Daumé III

• A Gentle Introduction to Graph Neural Networks by Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce and Alexander B. Wiltschko

• Algorithmic Aspects of Machine Learning by Ankur Moitra [pdf]

• Algorithms for Artificial Intelligence by Robert J. Moss [dpf] [Stanford]

• Alice’s Adventures in a differentiable wonderland by Simone Scardapane

• aman.ai - The art of artificial intelligence one concept at a time by Aman Chadha

• An Illustrated Guide to Automatic Sparse Differentiation by Adrian Hill, Guillaume Dalle, and Alexis Montoison

• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani

• Applied Causal Inference Powered by ML and AI by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis

• Applied Machine Learning for Tabular Data by Max Kuhn and Kjell Johnson

• Books by Andriy Burkov

  • The Hundred Page Machine Learning Book
  • The Hundred-Page Language Models Book
  • Machine Learning Engineering Book

• Computer Vision: Algorithms and Applications, 2nd Edition by Richard Szeliski

• Concise Machine Learning by Jonathan Richard Shewchuk [pdf]

• Crash Course in Deep Learning (for Computer Graphics) by Jakub Boksansky [alternative link]

• Data Science and Machine Learning: Mathematical and Statistical Methods by Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman

• Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control by Steven L. Brunton and J. Nathan Kutz [pdf]

• Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

• Deep Learning by Subir Varma and Sanjiv Das

  • Version with Python and R

• Deep Learning Course by François Fleuret

• Deep Learning: Foundations and Concepts by Chris Bishop with Hugh Bishop

• Deep Learning: Foundations, Architectures, and Engineering Practice by Amer Hussein [pdf]

• Deep Learning Interviews by Shlomo Kashani and Amir Ivry

• Deep Learning on Graphs by Yao Ma and Jiliang Tang

• Deep Learning with Python, Second Edition by François Chollet [pdf]

• Dive into Deep Learning

• Dive into Deep Learning Compiler

• Dummy's Guide to Modern LLM Sampling

• Foundations of Computer Vision by Antonio Torralba, Phillip Isola, and William Freeman

• Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar [MIT]

• GNN From Scratch by **

• Harvard's undergraduate course in Machine Learning by William J. Deuschle

• Introduction to Flow Matching and Diffusion Models by Peter Holderrieth and Ezra Erives

• Introduction to ggml by Xuan Son NGUYEN, Georgi Gerganov and slaren

• Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan [pdf]

• Introduction to Machine Learning by Sanjeev Arora, Simon Park, Dennis Jacob and Danqi Chen [Princeton]

• Introduction to Machine Learning [pdf] [MIT]

• Introduction to Machine Learning Interviews by Chip Huyen

• Introduction to Machine Learning Systems by Vijay Janapa Reddi

• Learning Theory from First Principles by Francis Bach [pdf]

• Lecture Notes for Machine Learning and Data Science Courses Information School, University of Washington by Ott Toomet [pdf]

• Lecture Notes for Machine Learning Theory by Tengyu Ma [pdf]

• Machine Learning Engineering Open Book by Stas Bekman

• Machine Learning Lecture Notes by Andrew Ng and Tengyu Ma [pdf] [Stanford]

• Machine Learning Systems by Vijay Janapa Reddi

• Machine learning with neural networks by Bernard Mehling [pdf]

• Neural Networks and Deep Learning by Michael Nielsen

• Natural Language Processing by Sanjiv Ranjan Das

• Notes on AutoGrad by Anton Schreiner

• Patterns, Predictions, and Actions: A story about machine learning by Moritz Hardt and Benjamin Recht

• Physics-based Deep Learning by N. Thuerey, P. Holl, M. Mueller, P. Schnell, F. Trost, K. Um

• Probabilistic Artificial Intelligence by Andreas Krause, Jonas Hübotter

• Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy

• Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy

• Speech and Language Processing, 3rd edition by Daniel Jurafsky and James H. Martin

• Statistical Learning Theory by Percy Liang [pdf]

• The Elements of Differentiable Programming by Mathieu Blondel and Vincent Roulet

• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

• The Engineer's Guide To Deep Learning by Hironobu Suzuki

• The Little Book of Deep Learning by François Fleuret

• The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer by Tianhua Chen

• The Most Important Machine Learning Equations: A Comprehensive Guide by Chizoba Obasi

• The Principles of Deep Learning Theory by Daniel A. Roberts, Sho Yaida, Boris Hanin

• The Principles of Diffusion Models: From Origins to Advances by Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon

• Theory of Deep Learning by Zhao Song [pdf]

• Tutorial on Diffusion Models for Imaging and Vision by Stanley H. Chan

• Undergraduate Fundamentals of Machine Learning by William J. Deuschle

• Understanding Deep Learning by Simon J.D. Prince

• Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David

Large Language Models

• A Visual Guide to Quantization: Demystifying the Compression of Large Language Models by Maarten Grootendorst

• Defeating Nondeterminism in LLM Inference by Horace He

• Foundations of Large Language Models by Tong Xiao and Jingbo Zhu

• How to Scale Your Model: A Systems View of LLMs on TPUs by Jacob Austin, Sholto Douglas, Roy Frostig, Anselm Levskaya, Charlie Chen, and Sharad Vikram

• How to run LLMs on PC at home using Llama.cpp by Tobias Mann

• Language Models Interview Handbook by Lamhot Siagian [pdf]

• LLM Inference Handbook

• Quantization from the ground up by Sam Rose

• The Big Book of Large Language Models by Damien Benveniste

Machine Learning Online Courses

• Google Machine Learning Education

• Neural Networks: Zero to Hero - A course by Andrej Karpathy

• Practical Deep Learning for Coders

Mathematics for Machine Learning

• Linear Algebra for Computer Vision, Robotics, and Machine Learning by Jean Gallier and Jocelyn Quaintance [pdf]

• Mathematical Analysis of Machine Learning Algorithms by Tong Zhang

• Mathematical Foundations of Machine Learning by Robert Nowak [pdf]

• Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory by Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger

• Mathematics for Artificial Intelligence Lecure Notes by Gilles Blanchard [pdf]

• Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

• Mathematics for Machine Learning by Garrett Thomas [pdf]

• Mathematics for Inference and Machine Learning by Marc Deisenroth and Stefanos Zafeiriou [pdf]

• Mathematics of Machine Learning by Philippe Rigollet

• Mathematics of Machine Learning by Rajen D. Shah

• Mathematics of Neural Networks by Bart M.N. Smets

• Matrix Calculus (for Machine Learning and Beyond) by Paige Bright, Alan Edelman, and Steven G. Johnson

  • MIT course
  • GitHub repository

• Optimization for Data Science by Bernd Gartner, Niao He and Martin Jaggi [pdf]

• Pen and Paper Exercises in Machine Learning by Michael U. Gutmann

• The Matrix Calculus You Need For Deep Learning by Terence Parr and Jeremy Howard

Reinforcement learning

• A Little Bit of Reinforcement Learning from Human Feedback by Nathan Lambert

• Deep Reinforcement Learning by Aske Plaat

• Deep Reinforcement Learning: Zero to Hero!

• Distributional Reinforcement Learning by Marc G. Bellemare, Will Dabney and Mark Rowland

• Mathematical Foundations of Reinforcement Learning by Shiyu Zhao

• Multi-Agent Reinforcement Learning: Foundations and Modern Approaches by Stefano V. Albrecht, Filippos Christianos and Lukas Schäfer

• Reinforcement Learning: An Introduction, Second Edition by Richard S. Sutton and Andrew G. Barto

• Reinforcement Learning: An Overview by Kevin Murphy

Computer Games AI

• Artificial Intelligence and Games by Georgios N. Yannakakis and Julian Togelius

• Game AI Pro by Steve Rabin

• Programming Starcraft AI by Peter Kis

Vibe Coding and Spec-Driven Development

• AI-Assisted Coding: A Practical Guide for Software Engineers by Durgesh Rajubhai Pawar

• Basic Claude Code by Harper Reed

• Disciplined AI Software Development by Jay Baleine

• Diving Into Spec-Driven Development With GitHub Spec Kit by Den Delimarsky

• How I program with Agents by David Crawshaw

• How I program with LLMs by David Crawshaw

• How I Use Every Claude Code Feature by Shrivu Shankar

• My LLM codegen workflow atm by Harper Reed

• Spec-driven development with AI: Get started with a new open source toolkit by Den Delimarsky

• Vibe Coding Terminal Editor by Alex Kladov

Algorithms

• A First Course on Data Structures in Python by Donald R. Sheehy

• Advanced Algorithms by Anupam Gupta [pdf] [CMU]

• Advanced Algorithms by Mohsen Ghaffari [pdf] [ETH]

• Advanced Algorithms by Prof. Michel Goemans [MIT]

• Advanced Algorithm Design by Sanjeev Arora [Princeton] [Fall 2015]

• Advanced Algorithm Design by Pravesh Kothari and Christopher Musco [Princeton] [Fall 2018]

• Advanced Data Structures by Erik Demaine

• Algorithms by Jeff Erickson

• Algorithms by Sariel Har-Peled [pdf]

• Algorithms Course by Prof. Frank Stajano [Cambridge]

• Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne

• Algorithms and Data Structures by Kurt Mehlhorn and Peter Sanders [pdf]

• Algorithms Books by Mykel J. Kochenderfer

  • Algorithms for Optimization by Mykel J. Kochenderfer, and Tim A. Wheeler
  • Algorithms for Decision Making by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray
  • Algorithms for Validation by Mykel J. Kochenderfer, Sydney M. Katz, Anthony L. Corso, and Robert J. Moss

• Algorithms Design (in C) by Paulo Feofiloff

• Algorithms for Inference by Prof. Devavrat Shah

• Algorithms for Massive Data by Nicola Prezza

• Algorithms for Modern Hardware by Sergey Slotin

• Algorithms in C by Robert Sedgewick [pdf]

• An Open Guide to Data Structures and Algorithms by Paul W. Bible and Lucas Moser

• Approximation Algorithms by Chandra Chekuri [pdf]

• Book of All-pairs Shortest Paths by Oleg Karasik

• Clever Algorithms: Nature-Inspired Programming Recipes by Jason Brownlee

• Collision Detection by Jeff Thompson

• Data Structure Visualizations by David Galles

• Data Structures & Algorithm Analysis by Clifford A. Shaffer

• Data Structures & Algorithms in C++, Second Edition by Michael T. Goodrich, Roberto Tamassia, and David M. Mount [pdf]

• Data Structures for Data-Intensive Applications: Tradeoffs and Design Guidelines by Manos Athanassoulis , Stratos Idreos and Dennis Shasha [pdf]

• Design and Analysis of Algorithms by Nima Anari and Moses Charikar

• Essential Coding Theory by Venkatesan Guruswami, Atri Rudra, and Madhu Sudan

• Exact String Matching Algorithms by Christian Charras and Thierry Lecroq

• Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan [pdf]

• Functional Data Structures and Algorithms: A Proof Assistant Approach by Tobias Nipkow, Jasmin Blanchette, Manuel Eberl, Alejandro Gómez-Londoño, Peter Lammich, Christian Sternagel, Simon Wimmer, Bohua Zhan

• Hello Algo

• How does B-tree make your queries fast? by Mateusz Kuźmik

• Introduction to Algorithms: A Creative Approach by Udi Manber [pdf]

• Introduction to Multi-Armed Bandits by Aleksandrs Slivkins

• Introduction to Parallel Algorithms by Guy E. Blelloch, Laxman Dhulipala, and Yihan Sun [pdf]

• Kalman Filter from the Ground Up by Alex Becker

• Lecture Notes on Quantum Algorithms by Andrew M. Childs [pdf]

• Let's build a Full-Text Search engine by Artem Krylysov

• Matters Computational: Ideas, Algorithms, Source Code by Jörg Arndt [pdf]

• Monte-Carlo Graph Search from First Principles by David J Wu

• Notes on Algorithms by James Aspnes

• Notes on Data Structures and Programming Techniques by James Aspnes

• Notes on Randomized Algorithms by James Aspnes [pdf]

• Open Data Structures by Pat Morin

• Planning Algorithms by Steven M. LaValle

• Principles of Algorithmic Problem Solving by Johan Sannemo

• Problem Solving with Algorithms and Data Structures using Python by Brad Miller and David Ranum

• Purely Functional Data Structures by Chris Okasaki [pdf]

• Sequential and Parallel Data Structures and Algorithms: The Basic Toolbox by Peter Sanders, Kurt Mehlhorn, Martin Dietzfelbinger, and Roman Dementiev

• The Arcane Algorithm Archive

• The Little Book of algorithms by Duc-Tam Nguyen

• The Skyline algorithm for packing 2D rectangles by Julien Vernay

• Think Data Structures by Allen B. Downey

• Tim Roughgarden's Courses and Lecture Notes

  • Algorithmic Game Theory

Bloom Filters

• Bloom Filters by Eli Bendersky

• Bloom Filters by Example

• Let's implement a Bloom Filter by Onat Yiğit Mercan

Date-time

• A Very Fast 64–Bit Date Algorithm by Ben Joffe

Diff Algorithms

• Building Git by James Coglan

  • The Myers diff algorithm
    • Part 1
    • Part 2
    • Part 3
  • Myers diff in linear space
    • Part 1: Theory
    • Part 2: Implementation
  • Merging with diff3
  • Why merges fail and what can be done about it
  • The patience diff algorithm
  • Implementing patience diff

• Diff Algorithms

• Myers Diff Algorithm - Code & Interactive Visualization by Robert Elder

Art

• Pixel art articles and tutorials by Pedro Medeiros

Biology

• Introduction to Genomics for Engineers

• The Algorithmic Beauty of Plants by Przemyslaw Prusinkiewicz and Aristid Lindenmayer [pdf]

Command Line and Tools

• Command Line Handbook by Petr Stribny

• Driving Compilers by Fabien Sanglard

• Getting started with tmux by ittavern

• How I'm still not using GUIs: A guide to the terminal by Lucas Fernandes da Costa

• How is a binary executable organized? Let's explore it! by Julia Evans

• Learn Makefiles: With the tastiest examples by Chase LambertS

• rsync: Series by Michael Stapelberg

• Terminal colours are tricky by Julia Evans

• The Definitive Guide To Docker

• Use Midnight Commander like a pro by Igor Klimer

• Write a Shell in C by Stephen Brennan

• Writing Programs with NCURSES by Eric S. Raymond and Zeyd M. Ben-Halim

• Writing Your Own Simple Tab-Completions for Bash and Zsh by Li Haoyi

Curl

• Curl Cookbook

• Curl Exercises by Julia Evans

• Mastering curl: interactive text guide by Anton Zhiyanov

Linux command line

• Effective Shell by Dave Kerr

• GameShell: a "game" to teach the Unix shell by Pierre Hyvernat

• Linux command line for you and me by Kushal Das

• The Linux Command Handbook by Flavio Copes

• The Linux Command Line by William Shotts

• Linux Commands Cheat Sheet

Nix

• NixOS & Flakes Book - An unofficial book for beginners by Ryan Yin

• Wombat’s Book of Nix by Amy de Buitléir

Competitions and Interview Preparation Websites

Math

• Erdős

• Erdős Problems

• International Mathematical Olympiad

Preparation Resources

• Easy Putnam Problems [pdf]

• Mathematical Problem Solving by Darij Grinberg

• My Putnam Problems by Avinash Sathaye [pdf]

• Putnam Training Problems [pdf]

• Virginia Tech Regional Mathematics Contest Problems [pdf]

  • Virginia Tech Regional Mathematics Contest Problems Solutions [pdf]

Physics

• International Physics Olympiad

Programming

• Advent of Code - An Advent calendar of small programming puzzles by Eric Wastl

• Code Golf - A site for recreational programming competitions.

• CodeAbbey

• CodeChef

• CodeForces

• Coderbyte

• Codewars

• Codility

• CodingBat - A free site of live coding problems to build coding skill in Java and Python.

• CodinGame

• Daily Programmer

• Edabit

• Exercism

• Hacker Earth

• HackerRank

• International Collegiate Programming Contest

• International Olympiad in Informatics

• Kaggle - ML specific.

• LabEx - Learn Linux, DevOps & Cybersecurity with Hands-on Labs.

• Learn OCaml

• LeetCode

• NeetCode

• Project Euler

• Rosalind - A platform for learning bioinformatics through problem solving.

• Skilled.dev

• Sphere Online Judge

• SQLPad

• takeUforward

• Top Coder

Preparation Resources

• Algorithms for Competitive Programming

• Competitive Programmer's Handbook by Antti Laaksonen

• Competetive Programming by Steven Halim [pdf]

• Competitive Programming in Python: 128 Algorithms to Develop your Coding Skills by Christoph Dürr and Jill-Jênn Vie

Compilers and Interpreters

• A Compiler Writing Journey by Warren

• A practical introduction to parsing by Jan Procházka

• Advanced Compilers: The Self-Guided Online Course by Adrian Sampson

• Build Your Own Lisp by Daniel Holden

• Building a Toy Programming Language in Python by Miguel Grinberg

  • Part 1
  • Part 2

• Building the fastest Lua interpreter.. automatically! by Haoran Xu

  • Part 1
  • Part 2: Building a baseline JIT for Lua automatically

• Compiler Design in C by Allen I. Holub

• Compiling to Assembly from Scratch by Vladimir Keleshev

• Crafting Interpreters by Robert Nystrom

• Creating the Bolt Compiler by Mukul Rathi

• Essentials of Compilation: An Incremental Approach by Geremy G. Siek

• Graal Truffle tutorial by Adam Ruka

• How Clang Compiles a Function by John Regehr

• How LLVM Optimizes a Function by John Regehr

• Introduction to Compilers by Andrew Myers

• Introduction to Compilers and Language Design by Prof. Douglas Thain

• Introduction to parser combinators by James Coglan

  • Part 1
  • Part 2: Precedence and associativity in recursive descent
  • Part 3: Packrat parsing: a top-down performance improvement

• Let's Build a Compiler by Jack Crenshaw

• Let's make a Teeny Tiny compiler by Austin Z. Henley

  • Part 1
  • Part 2
  • Part 3

• Let's Write a Compiler by Brian Robert Callahan

  • Part 1: Introduction, selecting a language, and doing some planning
  • Part 2: A lexer
  • Part 3: A parser
  • Part 4: Testing
  • Part 5: A code generator
  • Part 6: Input and output
  • Part 7: Arrays
  • Part 8: Strings, forward references, and conclusion

• Low-Level Software Security for Compiler Developers by Bill Wendling, Lucian Popescu, and Anders Waldenborg

• Make A Language - A series about making a programming language called Eldiro using the Rust programming language.

• Strategies for very fast Lexers

• Threaded code

• Writing a C compiler in 500 lines of Python by Theia Vogel

• Writing a C Compiler, in Zig by Rahman Sibahi

Static Program Analysis

• Principles of Program Analysis by Flemming Nielson, Hanne Riis Nielson, and Chris Hankin

• Secure Programming with Static Analysis by Brian Chess and Jacob West

• Static Program Analysis by Anders Møller and Michael I. Schwartzbach

Computer Graphics

• 3D Gaussian Splatting in a Weekend by Benjamin Feldman

• 3D Math Primer for Graphics and Game Development by Fletcher Dunn and Ian Parberry

• A fast and precise triangle rasterizer by Kristoffer Dyrkorn

• A trip through the Graphics Pipeline by Fabian Giesen

• A Quick Introduction to Workgraphs by Kostas Anagnostou

  • Part 1
  • Part 2: Performance

• Blocky Planet — Making Minecraft Spherical

• Building Real-Time Global Illumination by Jason McGhee

  • Part 1: Naive GI: A Foundation
  • Part 2: Radiance Cascades

• Collection of AMD Research Publications

• Computer Graphics from Scratch by Gabriel Gambetta

• Crash Course in BRDF Implementation by Jakub Boksansky

• FrostKiwi's Secrets by Wladislav Artsimovich

  • AAA - Analytical Anti-Aliasing
  • How to (and how not to) fix color banding
  • How video games use LUTs and how you can too
  • Video Game Blurs (and how the best one works)

• Fundamentals of Radiance Cascades

• Introduction to Computer Graphics by David J. Eck

• Noise is Beautiful: Part 1: Procedural textures by Stefan Gustavson [pdf]

• Noise is Beautiful by Stefan Gustavson

  • Part 1: Procedural textures [pdf]
  • Part 2: Noise is beautiful [pdf]

• Radiometry: Overview by Christoph Peters

• Rotations with quaternions

• GPU Gems Books Series

  • GPU Gems
  • GPU Gems 2
  • GPU Gems 3

• GPU Performance for Game Artists by Keith O’Conor

• GPU Programming Primitives for Computer Graphics by Daniel Meister, Atsushi Yoshimura, and Chih-Chen Kao

• Implementing a tiny CPU rasterizer by Nikita Lisitsa

• Implementing Order-Independent Transparency by Rubén Osorio López

• Learn Computer Graphics From Scratch

• Let's Make a Voxel Engine

• Lode's Computer Graphics Tutorial by Lode Vandevenne

• Marching Cubes, Explained Using Marching Squares

• Matrix Compendium by Łukasz Izdebski - The main purpose of this article is to gather information in the field of transformation in computer graphics and put it in one place.

• Ocean Rendering, Part 1 - Simulation by Robert Ryan

• Optimizing Software Occlusion Culling by Fabian Giesen

• Order Independent Transparency by Kostas Anagnostou

  • Rasterized
    • Part 1
    • Part 2
    • Part 3
  • Raytraced
    • Part 1
    • Part 2

• Physically Based Rendering in Filament by Romain Guy and Mathias Agopian

• Physically based rendering from first principles

• Physically Based Shading in Theory and Practice by Laurent Belcour, Naty Hoffman, Alain Hostettler, Peter Kutz, Kentaro Suzuki, Hajime Uchimura, Andrea Weidlich, *Kenichiro Yasutomi *

• Probability Theory for Physically Based Rendering by Jacco Bikker

  • Part 1
  • Part 2

• Rasterising a triangle by Jason Tsorlinis

• RasterTek toturials

• Recreating Nanite by Xavier Niochaut

  • Part 1 - The Plan
  • Part 2 - Visibility buffer
  • Part 3 - Cluster rendering
  • Part 4 - LOD generation
  • Part 5 - A basic material pass
  • Part 6 - LOD generation - faster, better, simpler
  • Part 7 - Runtime LOD selection
  • Part 8 - Mesh shader time
  • Part 9 - Raytracing

• Software rasterizing hair by Marcin Matuszczyk

• Texture-less Text Rendering by Tim Gfrerer

• The Geometry Behind Normal Maps by Shlomi Nissan

• Voxel Engine in a Weekend

• WebGL2 Fundamentals

Creative Coding

• Creative Coding Tutorials and Articles by Etienne Jacob

• Inigo Quilez Website

• Paul Bourke's Website

Curves and Surfaces

• A Primer on Bézier Curves

• Cubic spline interpolation by Eli Bendersky

• Curves and Surfaces by Bartosz Ciechanowski

DirectX 12

• A Gentle Introduction to D3D12 by Alex Tardif

• All Sources of DirectX 12 Documentation by Adam Sawicki

• Compute with DirectX 12 by Stefan Pijnacker

  • Part 1: the Theory
  • Part 2: The Setup

• DirectX-Graphics-Samples

• Getting Started With DirectX Raytracing by Seppe Dekeyser

• GPU Work Graphs mesh nodes in Microsoft DirectX® 12 by Max Oberberger

• Ten Years of D3D12 by Matt Pettineo

Image Processing

Dithering

• Atkinson Dithering by John Earnest

• Dithering in Colour by Niklas Oberhuber

• Dithering on the GPU by Alex Charlton

• Ditherpunk — The article I wish I had about monochrome image dithering by Surma

• Image Dithering: Eleven Algorithms and Source Code by Tanner Helland

• Writing My Own Dithering Algorithm in Racket by Amanvir Parhar

Metal

• Drawing Graphics on Apple Vision with the Metal Rendering API by Georgi Nikolov

• Metal Tutorial by Will Martin

OpenGL

• A Guide to Modern OpenGL Functions

• Best Practices for Modern OpenGL

• Learn OpenGL by Joey de Vries

• Learning Modern 3D Graphics Programming by Jason L. McKesson

• OGLdev: Modern OpenGL tutorials by Etay Meiri

• OpenGL Tutorial

Ray Tracing

• A raycasting engine in 7 easy steps by Austin Z. Henley

• Amanatides and Woo's fast Voxel Traversal

• Demystifying multiple importance sampling by Nikita Lisitsa

• How to build a BVH by Jacco Bikker

• Introduction of the Raytracing Technology

  • Part 1
  • Part 2

• Physically Based Rendering: From Theory To Implementation by Matt Pharr, Wenzel Jakob, and Greg Humphreys

• Ray-Casting Tutorial For Game Development And Other Purposes by F. Permadi

• Ray Tracing Gems by Eric Haines and Tomas Akenine-Möller

• Ray Tracing Gems II by Adam Marrs, Peter Shirley, and Ingo Wald

• Ray Tracing in One Weekend: The Book Series by Peter Shirley

• Ray Tracing with Voxels in C++ by Jacco Bikker

• Raymarching explained interactively

Shaders

• 3D Game Shaders For Beginners

• A Beginner's Guide to Coding Graphics Shaders by Omar Shehata

  • Part 1
  • Part 2
  • Part 3

• A Journey Into Shaders by Antoine Mayerowitz

  • Part I: The blob

• GM Shaders - All about shaders for GameMaker!

• Introducing GPU Reshape - shader instrumentation for everyone by Miguel Petersen

• Introduction to Shaders by Karl Bittner

• Learn Shader Programming with Rick and Morty by Daniel Hooper

• Mesh Shaders on RDNA™ Graphics Cards by Max Oberberger, Bastian Kuth and Quirin Meyer

• No More Shading Languages: Compiling C++ to Vulkan Shaders by H. Devillers, M. Kurtenacker*, R. Membarth, S. Lemme, M. Kenzel3, Ö. Yazici1, and P. Slusallek [pdf]

• On Crafting Painterly Shaders

• Post-Processing Shaders as a Creative Medium

• Shaders For People Who Don't Know How To Shader by Manuela Malasaña

• Shader Tutorials by Ronja

• The Best Darn Grid Shader (Yet) by Ben Golus

• The Book of Shaders by Patricio Gonzalez Vivo and Jen Lowe

Vulkan

• A Vulkan introduction by Nikos Papadopoulos

• I Am Graphics And So Can You - a series of blog posts about implementing a Vulkan renderer for Doom 3 by Dustin H. Land

• How I learned Vulkan and wrote a small game engine with it by Elias Daler

• NVIDIA Vulkan Ray Tracing Tutorial by Martin-Karl Lefrançois, Pascal Gautron, Nia Bickford, David Akeley

• Vulkan – Cross platform 3D Graphics

• Vulkan Guide

• Vulkan Raytracing Tutorials

• Vulkan Tutorial by Alexander Overvoorde

• Vulkan Tutorial (Rust) by Kyle Mayes

WebGPU

• Learn WebGPU by Elie Michel

• Learn Wgpu by Ben Hansen

• Migrating from WebGL to WebGPU by Dmitrii Ivashchenko

• WebGPU Fundamentals

• WebGPU Unleashed: A Practical Tutorial by Shi Yan

• Your first WebGPU app by Brandon Jones and François Beaufort

Computer Networks and Network Programming

• A comprehensive guide for Linux Network (Socket) programming

• An Introduction to Computer Networks by Peter L. Dordal

• Computer Networks: A Systems Approach by Larry Peterson and Bruce Davie

• Concurrent Servers by Eli Bendersky

  • Part 1 - Introduction
  • Part 2 - Threads
  • Part 3 - Event-driven
  • Part 4 - libuv
  • Part 5 - Redis case study
  • Part 6 - Callbacks, Promises and async/await

• HTTP2 Explained by Daniel Stenberg