A list of cool features of Git and GitHub.
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Overview of ECMAScript 6 features
A cheatsheet of modern C++ language and library features.
- cpp
- cpp11
- cpp14
- cpp17
An overview of Go syntax and features.
- cheatsheet
- go
- golang
- cheat-sheets
ECMAScript 6: Feature Overview & Comparison
📚 Sample Android Components Architecture on a modular word focused on the scalability, testability and maintainability written in Kotlin, following best practices using Jetpack.
- kotlin
- clean-architecture
- clean-code
- testing
Cheatsheet for some PHP knowledge you will frequently encounter in modern projects.
- php8-features
- cheatsheet
Descriptions of C++17 features, presented mostly in "Tony Tables" (hey, the name wasn't my idea)
- cpp
- cpp17
- tony-tables
- template-metaprogramming
Code examples for new features of watchOS 2.
An overview of Elm syntax and features
Starter app for Flutter that includes many different production app features; some not typically included in demo apps.
- flutter
- flutter-examples
- auth0
- internationalization
Modular application for database CRUD with Blazor. Uses an in-memory database and features dummy data generation and data pagination.
- azure-devops
- pagination
- azure
- azure-container-apps
An intro to some ES6 features via a set of self-guided workshops.
Shell wrapper for OpenAI's ChatGPT, Whisper, and TTS. Features LocalAI, Ollama, Gemini, Anthropic, and more.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning
A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.
- art-history
- color-analysis
- color-extraction
- color-palette