A comprehensive Domain-Driven Design example with problem space strategic analysis and various tactical patterns.
- domain-driven-design
- ddd
- ddd-architecture
- c4
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A comprehensive Domain-Driven Design example with problem space strategic analysis and various tactical patterns.
Emulator for rapid prototyping of Software Defined Networks
Modern concurrency tools including agents, futures, promises, thread pools, supervisors, and more. Inspired by Erlang, Clojure, Scala, Go, Java, JavaScript, and classic concurrency patterns.
BitChord - A modern YouTube music client with clean aesthetics inspired from Apple Music
Universal API for request permission and get its statuses.
A local-first desktop workspace for persistent AI teammates. Run Codex, Claude, and Grok with dedicated workspaces, task queues, file sharing, browser control, and agent-to-agent collaboration.
noUiSlider is a lightweight, ARIA-accessible JavaScript range slider with multi-touch and keyboard support. It is fully GPU animated: no reflows, so it is fast; even on older devices. It also fits wonderfully in responsive designs and has no dependencies.
Browser compatibility data for Web technologies as displayed on MDN
Welcome to the home of the Hot Chocolate GraphQL server for .NET, the Strawberry Shake GraphQL client for .NET and Nitro the awesome Monaco based GraphQL IDE.
Intelligent adaptive brightness for your external monitors
Hasklig - a code font with monospaced ligatures
Ultra-fast bootstrapping with Angular and Electron :speedboat:
An interactive and powerful Git interface for Neovim, inspired by Magit
A fast, highly extensible, and event driven SMTP server
😎Awesome GIS is a collection of geospatial related sources, including cartographic tools, geoanalysis tools, developer tools, data, conference & communities, news, massive open online course, some amazing map sites, and more.
🍎 Screensaver inspired by Apple's Event on October 30, 2018
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
:tomato: Simple and visually-pleasing Pomodoro timer
The most popular open source electronic health records and medical practice management solution.
Speed up downloads by combining multiple network connections in parallel
lakeFS - Data version control for your data lake | Git for data
:angel: The ultimate angle brackets parser library parsing HTML5, MathML, SVG and CSS to construct a DOM based on the official W3C specifications.
A ClojureScript framework for building user interfaces, leveraging React
Building powerful interactive prompts in Go, inspired by python-prompt-toolkit.