🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.
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🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.
A collection of public resources about how software companies test their software
One webpage for every book ever published!
Web Application ASP.NET 9 using Clean Architecture, DDD, CQRS, Event Sourcing and a lot of good practices
HumHub is an Open Source Enterprise Social Network. Easy to install, intuitive to use and extendable with countless freely available modules.
A curated list of awesome neural radiance fields papers
Community hub for Requestly API Client — bugs, feature requests, and roadmap. The privacy-first Postman alternative.
🚀 Ready-to-run Prisma example projects
SwiftUI sample app using Clean Architecture. Examples of working with SwiftData persistence, networking, dependency injection, unit testing, and more.
Implements a deep pretty printer for Go data structures to aid in debugging
Roadmap to becoming an iOS developer in 2018.
An Information Security Reference That Doesn't Suck; https://rmusser.net/git/admin-2/Infosec_Reference for non-MS Git hosted version.
The repository host gives no description.
An Open-Source Collection of Flash Cards to Help You Preparing Your Algorithms & Data Structures and System Design Interviews 💯
Roadmap to becoming a game developer in 2022
A curated list of awesome infosec courses and training resources.
:dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
Ultra-fast bootstrapping with Angular and Electron :speedboat:
Example projects using the AWS CDK
😎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.
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.
An authoritative list of awesome devsecops tools with the help from community experiments and contributions.
A collection of AWESOME things about domain adaptation
The Mobile App Pentest cheat sheet was created to provide concise collection of high value information on specific mobile application penetration testing topics.