This repository is a Chinese-language translation and expansion of the Learn wgpu tutorial series, teaching the wgpu Rust implementation of the WebGPU graphics API through a Cargo workspace of ~25 runnable example crates. The curriculum progresses from window/surface setup (tutorial1-2), render pipelines and buffers (tutorial3-4), textures/uniforms/instancing/depth (tutorial5-8), through OBJ model loading, Blinn-Phong lighting, normal mapping, camera control, compute-shader terrain generation, HDR rendering, and particle systems. Each example is a small but complete winit application that runs natively and as WebAssembly via wasm-bindgen, sharing a common utils crate that provides the WgpuAppAction lifecycle trait, node-based render abstractions (ViewNode, ComputeNode, BufferlessFullscreenNode), and buffer/bind-group helpers. A VitePress documentation site embeds live WASM demos of the examples, and showcase crates demonstrate a Pong game, offscreen GIF encoding, multithreaded model loading, and compute-shader tangent generation. Although primarily a tutorial codebase, it is genuinely hybrid: it doubles as a deployable documentation site and a reference implementation of modern wgpu patterns.
This repository is the LLM Inference Handbook, a comprehensive technical documentation site built with Docusaurus. It serves as an educational resource for engineers building, optimizing, and deploying LLM inference systems. The site covers inference fundamentals, model preparation, interaction patterns, optimization techniques (batching, KV cache, prefix caching, speculative decoding), kernel optimization (FlashAttention, GPU architecture), and infrastructure operations. It includes interactive React components such as GPU memory calculators, KV cache simulators, batching visualizers, and GPU execution visualizers that demonstrate concepts through live demos. The codebase is well-structured with TypeScript, CSS modules, a custom design system (DLS), and integration with Mantine UI. While primarily a documentation site, the interactive components are self-contained and could be extracted for reuse in other educational contexts. The handbook is framework-agnostic but has a strong emphasis on the Modular MAX ecosystem.
This repository is an interactive educational resource (Jupyter notebooks) teaching prompt engineering for Anthropic's Claude LLM. It is not a software application or a data pipeline; it is a notebook collection, so it is classified as data_research by the notebook signal, but its 'data' is synthetic/demo content (animal names, user/product records, arithmetic operations, legal tax code excerpts) used to illustrate prompt patterns. The primary entry points are the notebooks themselves, with parallel implementations for the Anthropic direct API and AWS Bedrock (both boto3 and anthropic Bedrock clients). Lessons progress from basic prompt structure, clarity, system prompts, XML tags, few-shot examples, step-by-step thinking ('Precognition'), output formatting, avoiding hallucinations, to complex/industry use cases. The notebooks use get_completion wrappers around client.messages.create(...), %store magic to share variables, and a tool-use appendix that implements prompt-injected function calling via XML-like tags and regex parsing. Exercises include golden answers and rubric-based grading (e.g., summary quality scored 1-5). Reusable patterns include the tool-use prompt-injection function construct_successful_function_run_injection_prompt(invoke_results), system-prompt composition (general tool-use explanation + specific tool definitions), and a grader prompt pattern. However, the code is tightly coupled to notebook execution order, API details may drift, and duplicated content across anthropic/boto3 notebooks creates maintenance risk.
This repository is a comprehensive Chinese-language educational tutorial and codebase for Retrieval-Augmented Generation (RAG) systems, created by Datawhale. It systematically covers the full RAG stack from fundamentals (data loading, chunking, embeddings) to advanced techniques (hybrid search, query construction, Text2SQL, reranking, agentic RAG, and Graph RAG). The code is organized by chapter, with runnable Python scripts demonstrating each concept using popular frameworks like LangChain, LlamaIndex, and LangGraph, alongside vector databases (Milvus, FAISS) and graph databases (Neo4j). While primarily an educational resource, the repository contains several well-crafted, reusable components such as a minimal Text2SQL agent framework, a clean LangGraph-based agentic RAG pattern, and a custom ColBERT reranker. The practical application is a Chinese recipe Q&A system ('尝尝咸淡'), which serves as a consistent domain for demonstrating various RAG techniques. The codebase is hybrid in nature, blending structured educational content with functional demo applications. The repository is actively maintained with a large community following. However, users should be aware of technical drift risks including vendored model code, hardcoded API dependencies, and the presence of AI-generated code sections. The core value lies in its structured curriculum and reusable RAG patterns rather than as a production-ready system.
This repository implements a complete 16-bit computer simulator built from fundamental logic gates (NAND, AND, OR, etc.) in Go. It includes a CPU, memory, ALU, memory-mapped I/O (keyboard and 240×160 pixel display), and a custom instruction set. The project provides three command-line tools: a simulator that loads binary programs and runs them with a GLFW-based graphical window, an assembler that converts a custom assembly language into binary, and a program generator that produces example assembly programs (e.g., text writer, sine wave animation, snake brush). The system is a faithful gate-level simulation—every component from bits to the CPU is wired together using Go structs, making it an educational tool for understanding computer architecture.
This repository contains the example code and homework assignments for Casey Muratori's "Performance Aware Programming" course (computerenhance.com). It is primarily educational material, not a production application. The core deliverable is a fully-functional 8086 instruction decoder, disassembler, and cycle estimator (sim86) implemented in C++, with multi-language bindings (C#, Python, Rust, Go, Node.js, WebAssembly, etc.). The rest of the repository comprises over 150 standalone C++ programs and x86 assembly listings that systematically explore performance measurement, memory hierarchy, SIMD (including AVX and FMA), floating-point math optimization (haversine distance), and profiling techniques. Each listing is self-contained, often including a custom repetition tester for accurate benchmarking. The architecture is a collection of independent executables that share common infrastructure via file inclusion (buffer, platform metrics, JSON parser, profiler macros). There is no build system beyond Windows batch scripts; portability to Linux/macOS is community-contributed and not officially tested. The code is well-crafted for teaching low-level performance concepts but is not designed for production reuse, though several components (the 8086 decoder, repetition tester, dead-code prevention macros) could be extracted with minimal effort.
This repository is an educational tutorial series that teaches how to build a nanoGPT-like transformer language model from scratch using PyTorch. It consists of over 30 Jupyter notebooks (with both TODO exercises and answer keys) designed for Google Colab, covering topics from token embeddings and positional encodings to multi-head attention, transformer blocks, training loops, scaling experiments, and instruction tuning. The code is modular, with separate classes for each component (TokenEmbedding, PositionEmbedding, AttentionHead, MultiHeadAttention, FeedForward, TransformerBlock, VocabularyLogits, nanoGPT, DataLoader, Trainer, ModelConfig), making it a valuable learning resource. Training is demonstrated on the Shakespeare dataset and later on a sample of FineWeb-Edu, with experiments on model size, learning rate, and tokenization. While the repository is not a production data pipeline or deployable application, it exhibits strong data/research characteristics through its focus on model training, scaling laws, and loss analysis. The notebooks are well-structured for self-paced learning but rely on Colab-specific workflows and hardcoded data URLs, limiting portability.
This repository contains two distinct Unity projects: the Unity Playground, an educational framework for teaching 2D physics game development through modular scripts and example scenes (Adventure, Defender, Maze, Lander, etc.), and the com.unity.asset-store-tools, an Editor extension for publishing packages to the Unity Asset Store. The Playground provides atomic movement, condition, action, and attribute components that can be combined visually in the Editor, with custom inspectors to guide beginners. The Asset Store Tools implement a full publishing workflow including package validation (30+ automated tests), preview generation (native and custom), package exporting, and upload with authentication and analytics. Both projects are well-structured with clear separation of concerns, but the Asset Store Tools show signs of version-specific conditional compilation and reliance on internal Unity APIs, indicating medium technical drift. The Playground examples are simple but functional, while the tools demonstrate more advanced patterns like service providers and interface-based validation frameworks.
This repository contains solutions to Harvard's CS50P (Introduction to Programming with Python) course problem sets, organized by week from Week0 to Week9. Each week features multiple standalone Python scripts that solve specific programming exercises, such as calculating tips, validating license plates, converting time formats, processing CSV files, and generating images or PDFs. The scripts are designed to be run from the command line, typically taking user input via `input()` and producing output via `print()`, with some utilizing external libraries for specialized tasks like emoji conversion, figure generation, or image manipulation. While many scripts are simple and focused on teaching basic programming concepts, several components demonstrate reusable, well-crafted patterns worthy of highlighting. Notable examples include the time-parsing function in Week1/meal.py (converting strings like '7:30 AM' to float hours), the license plate validation logic in Week2/plates.py (enforcing rules on length, character types, and number placement), the CSV transformation script in Week6/scourgify/scourgify.py (splitting 'Last, First' names into separate columns), the Jar class in Week8/jar/jar.py (modeling a container with deposit/withdraw operations), and the time-range conversion function in Week7/working/working.py (converting '9:00 AM to 5:00 PM' to 24-hour format). These components are isolated, self-contained, and could be extracted for reuse in other projects with minimal modification. The repository is actively maintained, with the most recent commit dated 2026-06-04. It relies on a few external libraries (such as `inflect`, `emoji`, `pyfiglet`, `tabulate`, `Pillow`, `validators`, and `fpdf`) for specific functionalities, but the core logic of the highlighted components uses only the standard library. There is no evidence of significant technical debt or outdated dependencies, indicating a low level of technical drift. The codebase serves as an excellent educational resource for beginner to intermediate Python programmers, showcasing practical problem-solving techniques.
This repository is the content generation and build system for the interactive textbook *Mathematics in Lean*. It is a hybrid project: primarily a set of Python scripts and a Lean 4 project that together produce the textbook in multiple formats (Sphinx RST/HTML/PDF), as well as a separate user repository with exercise stubs and solutions. The core pipeline parses Lean source files annotated with custom directives (`TEXT`, `EXAMPLES`, `SOLUTIONS`) via `scripts/mkdoc.py` to generate textbook prose and Lean exercise files. A Sphinx build then produces the final HTML/PDF documentation. The Lean source files themselves cover mathematics from basic arithmetic to measure theory, heavily leveraging the Mathlib library. The repository includes CI/CD workflows (GitHub Actions), developer environments (Devcontainers, Gitpod), and testing scripts that verify the generated Lean files compile. While not a traditional application, it functions as a specialized toolchain for creating and deploying formal mathematics educational content.
TutoriaLLM is a self-hosted, full-stack web application designed as a programming learning platform for K-12 education. It features a Blockly-based visual programming interface integrated with Minecraft, AI-powered tutoring via OpenAI for real-time guidance and tutorial generation, and comprehensive session tracking including dialogue, workspace state, and multimedia. The architecture follows a monorepo structure with pnpm workspaces, separating concerns into frontend (React/Vite/Tailwind), backend (Node.js/Hono/PostgreSQL with Drizzle ORM), Blockly extensions, and documentation. Key functionalities include user authentication, session persistence, admin dashboard for content management, vector-based tutorial recommendations, and progress analytics. The system relies on external services like OpenAI API, PostgreSQL with pgvector, and S3-compatible storage (MinIO) for media.
This repository is a self-paced learning roadmap for algorithmic trading, structured as a hierarchical collection of markdown files. It curates resources, certifications, and project ideas across artificial intelligence, computer science, mathematics, finance, cloud engineering, and software engineering. The repository is entirely documentation-driven; there are no executable applications, data pipelines, or machine learning models. Its primary value lies in its organized curriculum, reusable topic templates, and role-based learning paths (e.g., quant developer, algorithmic trader). While not a software project, it demonstrates high organizational craftsmanship through consistent documentation structure, templates, and CI/CD for link checking and diagram rendering.
This repository contains the starter files and completed solutions for Wes Bos's 'JavaScript30' course - a collection of 30 vanilla JavaScript exercises, each a self-contained mini-application demonstrating a specific browser API or JavaScript pattern. The exercises cover: DOM manipulation and events (Drum Kit, Clock, Whack-a-Mole), modern array methods (Array Cardio Days 1-2), localStorage persistence, HTML5 Canvas drawing, custom video player controls, speech recognition/synthesis, webcam/media devices API, geolocation, CSS variables manipulation, flexbox transitions, sticky navigation, drag-to-scroll, type-ahead autocomplete, shift-click range selection, konami code detection, scroll-triggered animations, dropdown navigation, link highlighting, and various console/debugging techniques. Each exercise exists as a pair: START (boilerplate HTML/CSS) and FINISHED (working solution). The code is intentionally written as inline scripts in HTML files to match the video tutorials, not as modular, reusable components. There are no build tools, test suites, or dependency management (except browser-sync for live-reload in a few exercises). The repo is actively maintained to stay in sync with the course videos, but the content is pedagogically frozen - PRs that improve code but deviate from the videos are explicitly rejected.
This repository is a comprehensive educational resource for linear algebra and matrix computations, serving as the companion code for the book "Book 4: The Power of Matrices" from the "Iris Flower Book" series. It contains Jupyter notebooks and Streamlit apps that visualize core concepts from vectors and matrices to eigenvalue decomposition, SVD, and data projections. The code is pedagogical, using synthetic data and the Iris dataset to illustrate mathematical ideas through interactive plots and heatmaps. It is not a production application or data pipeline but a teaching tool. The repository is well-organized by chapter, though code is duplicated across notebooks rather than modularized, reflecting its educational nature.
This repository is the Korean translation of the Modern JavaScript Tutorial (https://javascript.info). It contains a comprehensive collection of educational articles, tasks, solutions, and interactive code examples covering core JavaScript, browser APIs, networking, animations, web components, and regular expressions. The content is structured as static markdown files with embedded code snippets and self-contained HTML demos. It is not a production software application but a static educational resource. The repository demonstrates consistent translation conventions and includes reusable teaching patterns such as decorators, custom events, drag-and-drop algorithms, and event delegation. Some content references older browser behaviors and deprecated APIs (e.g., `__proto__`, XMLHttpRequest), but also covers modern features like async/await, Promises, and modules. The code examples are clean and well-documented, making it a valuable learning resource for Korean-speaking developers.
This repository is an educational tutorial for Apache Spark using Scala, designed to teach distributed data processing concepts through practical examples. It functions as a data research and engineering learning resource, featuring Jupyter notebooks and Scala source code that demonstrate RDD operations, SparkSQL, and streaming pipelines. The project includes bundled datasets such as the King James Version Bible and the Enron Spam/Ham email corpus, along with golden output files for test verification. While it serves as a comprehensive guide for Spark development, it primarily focuses on teaching data transformation patterns rather than deploying a production service. The codebase demonstrates solid engineering practices for an educational context, including unit tests, custom utility classes for CLI argument parsing, and structured data schemas. However, the technical stack is significantly outdated, relying on Spark 2.3.0 and Scala 2.11.8, which are no longer supported. The examples frequently use deprecated APIs, such as the RDD-based DStream for streaming, which has been superseded by Structured Streaming in modern Spark versions. This creates a high technical drift, making the code less suitable for new projects without significant refactoring. Despite the outdated dependencies, the repository remains a valuable resource for understanding Spark fundamentals. The inclusion of golden files and test utilities provides a reproducible framework for validating data processing logic. The tutorial structure progresses logically from basic word counts to complex SQL and streaming operations, making it effective for learning. Users should be aware that while the patterns are sound, the specific API implementations will require updates to align with current Spark LTS versions.
This repository is the official code companion to the book '50 ML projects to understand LLMs' by Mike X Cohen. It contains over 50 Jupyter notebooks organized into chapters (2–7), each exploring a specific concept in large language model interpretability and internals. Topics span tokenization, embeddings, cosine similarity, softmax/logits, loss functions, sampling, hidden state analysis, effective dimensionality, logit lens, attention mechanisms, MLP projections, probing classifiers, and bias measurement. The code is purely educational and experimental—no production services, APIs, or deployable applications. All notebooks are designed to run on Google Colab with minimal setup, using standard libraries (PyTorch, Hugging Face Transformers, scikit-learn, numpy, matplotlib) and pretrained models (GPT-2, BERT, RoBERTa, Pythia). Each project has a 'helper' notebook (incomplete code for students) and a 'solution' notebook (complete reference implementation). The repository is a valuable resource for learning mechanistic interpretability and hands-on LLM analysis, but it lacks modularization, dependency pinning, and test coverage, making it unsuitable for direct reuse in production pipelines.
This repository is a companion documentation site for Zhou Zhihua's 'Machine Learning' (Watermelon Book), providing detailed step-by-step formula derivations for all chapters. It is built with VitePress, a static site generator, and deployed to GitHub Pages. The site includes 16 chapters of mathematical derivations, an errata page tracking corrections across print editions, and French translations for the first two chapters. While the content is educational and not a software application in the traditional sense, the repository functions as a VitePress project with custom theme configuration, MathJax rendering, and CI/CD deployment. It is actively maintained by the Datawhale community and serves as a learning resource for machine learning practitioners.
This repository is the companion code for the O'Reilly book 'Hands-On Large Language Models' by Jay Alammar and Maarten Grootendorst. It provides 12 chapters of Jupyter notebooks covering fundamental and advanced LLM topics including tokenization, embeddings, text classification, clustering, prompt engineering, semantic search, multimodal models, fine-tuning, and generation. All notebooks are designed to run on Google Colab with free GPU resources and rely heavily on Hugging Face libraries (transformers, datasets, sentence-transformers, TRL) and external APIs (OpenAI, Cohere). The code is educational in nature, with clear step-by-step explanations and visualizations. While it is not a production application or a novel research pipeline, it serves as a high-quality, reproducible tutorial resource for practitioners learning LLM workflows. The repository includes pinned dependencies, Conda environment setup, and local installation guides, making it relatively easy to reproduce in a controlled environment, though API keys are required for some sections.
This repository is the official companion code for the O'Reilly book 'Natural Language Processing with Transformers' by Tunstall, von Werra, and Wolf. It contains 11 Jupyter notebooks, one per chapter, covering the full spectrum of NLP with Transformers: text classification, named entity recognition, text generation, summarization, question answering, model compression, few-shot learning, and training a transformer from scratch. The code is built on the Hugging Face ecosystem (transformers, datasets, accelerate) and is designed to be run interactively on Colab, Kaggle, or local GPU environments. The notebooks are well-structured with clear section headings, explanatory text, and visualizations, making them an excellent educational resource for learning state-of-the-art NLP techniques. Although the repository is primarily educational, it includes a SageMaker adaptation for some chapters, demonstrating deployment of Hugging Face models on AWS. The codebase is not intended as a production-grade library; rather, it serves as a tutorial and reference for practitioners. The overall quality is solid, with clean, reproducible code patterns, but the reliance on pinned, outdated library versions (e.g., transformers 4.16.2, datasets 1.16.1) and the deprecated Chapter 7 (farm-haystack) indicate moderate technical drift. The concepts remain relevant, but the exact code may require adjustments to run with the latest versions.