Search Results for "code framework"
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Showing 33 open source projects for "code framework"
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Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Source Code Hunter
Source code analysis of Spring, MyBatis, Redis, Netty, and more
Source Code Hunter is an open source project by Doocs that focuses on analyzing and explaining the source code of widely used Java frameworks and libraries. It helps developers deepen their understanding of internal implementations, design patterns, and performance optimizations by walking through actual codebases such as Spring, MyBatis, Netty, Tomcat, and others. The project aims to bridge the gap between theoretical knowledge and real-world application by providing step-by-step annotated... -
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PocketFlow Tutorial Codebase Knowledge
Codebase to Tutorial
PocketFlow Tutorial Codebase Knowledge is a project that demonstrates how to build an AI agent capable of analyzing arbitrary codebases and generating beginner-friendly tutorials that explain how they work, turning complex source code into clear educational content. The repository builds on a lightweight 100-line LLM framework and uses natural language models to inspect repository structures, identify core abstractions, map dependencies, and articulate the reasoning behind code design and interactions. By crawling code files, extracting higher-level patterns, and using large language models to narrate explanations, the system aims to help developers — especially those new to a codebase — understand unfamiliar projects without manual deep reading. ... -
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LLM101n
LLM101n: Let's build a Storyteller
LLM101n is an educational repository that walks you through building and understanding large language models from first principles. It emphasizes intuition and hands-on implementation, guiding you from tokenization and embeddings to attention, transformer blocks, and sampling. The materials favor compact, readable code and incremental steps, so learners can verify each concept before moving on. You’ll see how data pipelines, batching, masking, and positional encodings fit together to train a... -
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GSY Flutter Book
Flutter complete development practical explanation series
GSY Flutter Book is an open-source book (or tutorial style project) built for learning Flutter (the cross-platform mobile UI toolkit from Google) and other related mobile UI development topics. It uses the Flutter framework (Dart language) and provides practical examples, code snippets, screenshots, and detailed explanations of UI components, state management, navigation, animations, architecture, etc. The structure of the book is such that you’ll get both theoretical explanation and hands-on code you can run and adapt. It aims at developers who already have some experience and want to deepen their Flutter skills, particularly for production-grade mobile apps, with best practices, component libraries, and architecture guidance. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. -
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Qualify
Claude code to help you do lead research and qualification
...Built with simplicity and extensibility in mind, the project includes REST APIs, database models, and a user interface that can be extended or embedded into existing internal systems. Because it prioritizes lean data structures and predictable code paths, developers can customize pipelines, fields, and behaviors to align with their unique business logic. -
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React in patterns
A free book that talks about design patterns/techniques
...The book explains concepts such as communication, event handlers, composition, controlled inputs, container components, one-way data flow, Flux, Redux, dependency injection, styling, and third-party integration. It includes source code examples that support the patterns described in the text. The repository also provides the book in multiple formats, including web, PDF, Mobi, and ePub. It is useful as a conceptual guide for developers who want stronger React architecture habits without relying only on framework-specific recipes. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines of code, ktrain allows you to easily and quickly. ktrain purposely pins to a lower version of transformers to include support for older versions of TensorFlow. ... -
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aguacate
With aguacate you get a better taste
Aguacate is a framework with focus on implement business logic functionality, using configuration files (json) instead of programming. See the wiki and the videos See https://github.com/mcnew/aguacate-swagger-demo for a functional demo using docker-compose Videos in Spanish: https://www.youtube.com/playlist?list=PLSHQIkodyHroW2XF7BWCrymDv_Nlj7HRS -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. ... -
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UnityMol
Molecular viewer and prototyping framework
!!! New website is https://unity.mol3d.tech and new releases/source code are now on Github : https://github.com/LBT-CNRS/UnityMol-Releases !!!! Implementation of a visualization prototype for molecular structures and networks using the Unity3D game engine. Implementation of numerous graphical methods as spheres, particle systems, bond, etc.. as well as our recent HyperBalls methodology (see: www.hyperballs.sf.net). -
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Spring and Hibernate for Beginners
Source code for the course: Spring and Hibernate for Beginners
This repository contains code and resources for learning Spring and Hibernate, tailored to beginners looking to understand Java-based web development. Here is a Spring Boot Learning Path and Roadmap to help you learn more about Spring Boot. These are links to luv2code courses on Udemy. You can purchase the courses to further your studies. -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. ... -
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Learn and Play
XML-based framework for developing games
XML-based framework for developing games in (ActionScript 2.0, discontinued) HTML5 Phaser.io's engine It'd been thought to develope a project with a great amount of games on drag&drop, painting, typing, hunting, puzzles.There's no need to write the same code to similar games! Just write some XML lines (check samples) and one JavaScript instruction to run your game. -
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms,... -
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3 Rs of Software Architecture
A guide on how to write readable, reusable, and refactorable software
This guide aims to help software developers think more clearly about how to build systems that are not only functional today but maintainable into the future. It focuses on three architectural “ilities”: readability, reusability, and refactorability, presenting them in a hierarchical framework so developers can evaluate and improve their code and system design. The project uses a simple shopping-cart application written in JavaScript and React/Redux to illustrate how code evolves from “bad” via “better” to “good” across those three dimensions. By examining common smells—poor naming, deep nesting, long functions, tight coupling—readers learn how to restructure code to improve maintainability. ... -
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Neural MMO
Code for the paper "Neural MMO: A Massively Multiagent Game..."
Neural MMO is a massively multi-agent simulation environment developed by OpenAI for reinforcement learning research. It provides a persistent, procedurally generated world where thousands of agents can interact, compete, and cooperate in real time. The environment is inspired by Massively Multiplayer Online Role-Playing Games (MMORPGs), featuring resource gathering, combat mechanics, exploration, and survival challenges. Agents learn behaviors in a shared ecosystem that supports long-term... -
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A symbolic framework for C++, Python and Octave implementing automatic differentiation by source code transformation in forward and reverse modes on sparse matrix-valued computational graphs.
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FB Chart Designer
Create multi-degree polynominals charts. Designed in FbEdit & WinAPI.
...This is the one of the fastest way to create window application with visual design methods, and implement into it an ellegant and flexible chart, that can be choosen from four different diagram kinds (if you edit code and use it in your projects). The source code chart library by Lothar Schirm is based on the consolidated solutions from FireFly Designer by Paul Squires. FB Chart Designer shows the complete example of connection those mentioned solutions to achieve thiny file and less code project. It makes more simply to analyze code and provides faster implementation of rebuilted chart library. ... -
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.NET Book
.NET Platform Architecture book (English, Chinese, Russian)
This book was thought as a full description of how .NET CLR and to some extent .NET Framework function. I tried to draw the reader's attention to their internals from a different, unusual point of view. Primarily, it is caused by the statement, quite disputable, that any software developer must know C/C++. Why? The reason is that among all high-level languages these are the nearest to the CPU, and if you build your program in these languages you feel better about how it runs. -
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Electron API Demos
Desktop app to demonstrate the core features of the Electron API
This is a desktop app that interactively and with sample code demonstrates core features of the Electron API. It's built with Electron, too, of course. This app works on Windows, macOS and Linux operating systems. The Electron API Demos app interactively demonstrates the most important features of the Electron API. Use this app to see what you can do with Electron and use the source code to learn how to create a basic Electron app. -
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panFMP is a generic framework suitable for harvested XML metadata that is searchable through Apache Lucene without any additional RDBMS. Fields can be defined by XPath allowing for full text queries on all types of fields including numerical ranges. The code was moved to Github: https://github.com/pangaea-data-publisher/panfmp
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. -
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OpenRTDynamics
A framework for implementing real-time control systems
- - - T H I S P R O J E C T M O V E D T O G I T H U B - - - A framework for the implementation of advanced real-time control systems which uses signal/block-based schematics (like Simulink) extended by several features like state machines and online-exchangeable sub-simulations. Besides, this framework properly handles multiple threads, their communication, allows to synchronise control systems to external events (e.g. variable timers or incoming network packages) and provides... -
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Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10).
