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Collect! is a highly configurable debt collection softwareThe flexible & scalable debt collection software built to automate your workflow. From startup to enterprise, we have the solution for you.
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Memobase
Fast backend for long-term AI user memory via structured profiles
Memobase is an open source backend system that enables long-term user memory functionality for AI applications by capturing and structuring information about users across interactions. Its design centers on creating user profiles and recording event timelines, allowing AI systems to remember, understand, and evolve in their behaviour toward individual users over time. Instead of relying purely on traditional embedding-based retrieval or RAG systems, Memobase uses profile and timeline structures to deliver memory that reflects user context efficiently and meaningfully. The system focuses on three principal performance metrics: high search performance, reduced large language model (LLM) costs through batch processing techniques, and low latency with minimal SQL operations. Memobase supports integration with existing LLM workflows via APIs and SDKs (including Python, Node, and Go), making it easy to adopt within diverse application stacks. -
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AutoClicker6.0
This AutoClicker is the best free auto clicker for fast clicking.
AutoClicker 6.0 is the best free auto clicker for anyone looking for a fast auto clicker download with powerful customization, smooth performance, and easy click automation. Designed for speed, accuracy, and simplicity, AutoClicker 6.0 is a lightweight auto clicking software that helps you automate repetitive mouse clicking tasks on your computer with maximum efficiency. If you need an auto clicker for Windows, a safe auto clicker, or a low CPU usage auto clicker, AutoClicker 6.0 is one of the top choices for gamers, productivity users, and testers. This advanced auto clicker 6.0 download tool supports left click auto clicker, right click auto clicker, and middle click auto clicker modes, making it a flexible mouse macro and clicking automation software for nearly any task. With adjustable click speeds, you can set a CPS clicker style rapid click rate or configure a precise timer for controlled clicking. AutoClicker 6.0 also includes hotkey auto clicker support. -
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Catalina is a C compiler plus a set of C libraries and device drivers for use with the Parallax Propeller microcontroller. Catalina is a cross-compiler based on the retargetable C compiler "lcc". Catalina runs on Windows or Linux.
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BoldTrail Real Estate CRMBoldTrail, the #1 rated real estate platform, is built to power your entire brokerage with next-generation technology your agents will use and love. Showcase your unique brand with customizable websites for your company, offices, and every agent. Maximize lead capture with a modern, portal-like consumer search experience and intelligent behavior tracking. Hyper-local area pages, home valuation pages and options for rich lifestyle data keep customers searching with your brokerage as the local experts. The most robust lead gen tools on the market help your brokerage, teams & agents effectively drive new business - no matter their budget. Empower your agents to generate free leads instantly with our simple to use landing pages & IDX squeeze pages. Drive more leads with higher quality and lower cost through in-house tools built within the platform. Diversify lead sources with our automated social media posting, integrated Google and Facebook advertising, custom text codes and more.
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OpenJUMP is a community driven fork of JUMP the "Java Unified Mapping Platform" GIS software. The original JUMP was developed by Vivid Solutions, released under GPL2 in 2003 and discontinued in 2006. During 2004 already some enthusiastic developers joined together to enhance further the features of JUMP. They launched an independent development branch called OpenJUMP. The name gives credit to the original JUMP development, and at the same time describes the objectives of this project to be fully open to anyone wanting to contribute. These days OpenJUMP is developed and maintained by (some few) volunteers around the globe. If you need functionality or even better want to contribute you are very welcome to contact us at our mailing list.
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Database connection pool with support for lots of languages and databases.
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Paleta de Componentes para as Linguagens Delphi e Lazarus, compatível com Windows e Linux, que permite acesso direto a equipamentos de Automação Comercial, sem DLL's, interagindo com eles direto na porta serial.
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Steel Bank Common Lisp
Common Lisp compiler and runtime
A high performance Common Lisp compiler. In addition to standard ANSI Common Lisp, it provides an interactive environment including an a debugger, a statistical profiler, a code coverage tool, and many other extensions. -
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ADB Sync
Synchronize files between a PC and Android device using ADB
adb-sync is a command-line utility designed to synchronize files between a PC and an Android device over the Android Debug Bridge (ADB). It simplifies the process of transferring and mirroring directories without requiring root access or complex configuration. By comparing file states between the host and the device, adb-sync efficiently updates only changed files, reducing transfer time and bandwidth usage. The tool also supports reverse synchronization, allowing users to copy data from an Android device back to their PC. While this project has been deprecated in favor of better-adb-sync, it remains a lightweight and effective option for managing file transfers and backups over USB debugging connections. -
Nonprofit Budgeting SoftwareMartus' collaborative and easy-to-use budgeting and reporting platform will save you hundreds of hours each year. It's designed to make the entire budgeting process easier and create unlimited financial transparency.
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JsAction
JsAction is a small event delegation library
JSAction is a JavaScript framework developed by Google that provides a structured, event-driven architecture for managing user interactions in large-scale web applications. It simplifies event handling by declaratively binding actions to DOM elements through HTML attributes, enabling clean separation between markup and behavior. JSAction helps improve performance, maintainability, and reliability by minimizing the use of inline scripts and global event listeners. It is especially useful in complex front-end environments where efficient event delegation and well-defined interaction flows are crucial. -
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WWWBasic
wwwBASIC is an implementation of BASIC that runs on Node.js & the Web
wwwBASIC is a JavaScript-based implementation of the classic BASIC programming language designed to run seamlessly in web browsers and Node.js environments. Created by Google, it allows developers and enthusiasts to write and execute BASIC programs directly within HTML pages or via command-line tools. The interpreter compiles BASIC source code into JavaScript at load time, enabling efficient execution within modern web environments without requiring external emulators or plugins. It supports traditional BASIC constructs such as loops, conditionals, and I/O operations, along with 24-bit color graphics functions (PSET, LINE, CIRCLE) and input handling (INKEY$, GETMOUSE). wwwBASIC brings retro programming into the modern web era, making it ideal for educational purposes, historical software preservation, and interactive demonstrations -
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The repository includes implementations, experimental data, and supporting research papers that accompany published studies. Notable works such as Weight Agnostic Neural Networks and Neuroevolution of Self-Interpretable Agents highlight the team’s exploration of how AI can learn more efficiently and transparently. Overall, this repository serves as an open research hub for sharing ideas and advancing the understanding of intelligent systems. -
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Skylark
Skylark in Go: the Skylark configuration language
Skylark, now known as Starlark, is an interpreter for a Python-like language implemented in Go. It is designed as a lightweight, deterministic, and embeddable configuration and scripting language ideal for use within larger applications. Skylark maintains Python’s familiar syntax and high-level data types while omitting features that could cause nondeterminism, such as concurrency and dynamic module imports. The interpreter supports first-class functions, dictionaries, lists, and comprehensions, allowing developers to define reusable logic and structured configuration data. Originally developed for Bazel, Google’s build tool, Skylark enables users to define build rules and macros that extend system functionality. Because it runs deterministically and isolates execution from system state, it’s well-suited for reproducible build systems and other sandboxed environments. The Go implementation focuses on parallel scalability and easy integration into Go-based projects. -
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SwissGL
SwissGL is a minimalistic wrapper on top of WebGL2 JS API
SwissGL is a compact JavaScript library that provides a streamlined abstraction layer over the WebGL2 API, designed to minimize boilerplate when building GPU-accelerated graphics, simulations, and procedural visualizations. Acting as a "Swiss Army knife" for WebGL2, it simplifies shader, texture, and framebuffer management into a single, expressive interface that enables developers to write complex GPU workflows in just a few lines of code. The library centers around one main function that unifies rendering and compute operations, allowing the creation of particle systems, GPGPU effects, and real-time simulations entirely on the GPU. Despite its simplicity and small size (under 1000 lines of code), SwissGL demonstrates remarkable flexibility, from basic visual experiments to complex multi-pass rendering pipelines. It’s also designed as an exploration of minimalist graphics API design, serving as an early experimental step toward the upcoming WebGPU era. -
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Shaderc
A collection of tools, libraries, and tests for Vulkan shader
Shaderc is a collection of tools and libraries for compiling shaders—small programs that run on GPUs—into SPIR-V, the intermediate representation used by the Vulkan graphics API. It provides both a command-line tool (glslc) and a C/C++ library (libshaderc) that wrap the functionality of glslang (the Khronos reference compiler for GLSL) and SPIRV-Tools to deliver a modern, scriptable, and efficient shader compilation workflow. The glslc compiler offers a GCC/Clang-like interface for building GLSL and HLSL shaders, making it easy to integrate into existing build systems. Meanwhile, libshaderc exposes a stable API that allows developers to programmatically compile shader strings into SPIR-V modules within graphics engines and tools. Shaderc supports advanced features such as file inclusion (#include), concurrency, and cross-platform builds, and it maintains backward compatibility for long-term projects. -
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Mozc
Mozc - a Japanese Input Method Editor designed for multi-platform
Mozc is an open source Japanese Input Method Editor (IME) developed by Google, designed to provide Japanese text input across multiple operating systems including Android, macOS, Windows, GNU/Linux, and Chromium OS. The project originated as a subset of Google Japanese Input, released publicly under the BSD 3-Clause license for community use and development. Mozc offers core IME functionality such as text conversion, prediction, and dictionary-based input, enabling users to efficiently type and edit Japanese text. While Mozc shares much of its codebase with Google’s internal IME, it operates as an independent open source project without official support, guarantees, or stable release cycles. Developers can build Mozc from source for their preferred platform, and the repository includes detailed build instructions for Android, Linux, macOS, and Windows environments. -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. Gin is particularly popular in TensorFlow and PyTorch projects, where researchers and developers need to tune numerous interdependent parameters across models, datasets, optimizers, and training pipelines. -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
youtube-8m is Google’s open source starter code and reference implementation for training and evaluating machine learning models on the YouTube-8M dataset, one of the largest video understanding datasets publicly released. The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. The code demonstrates how to process frame-level features, train logistic and deep learning models, evaluate them using metrics like global Average Precision (gAP) and mean Average Precision (mAP), and export trained models for MediaPipe inference. -
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Forma
An efficient vector-graphics renderer
Forma is an experimental vector graphics renderer written in Rust, developed by Google to explore high-performance, parallelized rendering techniques across multiple platforms. The project aims to achieve portability, performance, simplicity, and small footprint through a streamlined four-stage rendering pipeline. Forma provides both CPU (software) and GPU (hardware) backends, relying on Rust’s SIMD auto-vectorization, Rayon for multithreading, and WebGPU (wgpu) for hardware acceleration. The renderer processes Bézier curves, line segments, and pixels through stages of flattening, rasterization, sorting, and painting, updating only changed tiles for efficiency. This design allows Forma to render complex vector scenes—such as large-scale SVGs—at interactive frame rates even on CPUs. -
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This is a freedom software and it obeys opensource initiative. The main purpose of this project is to bring extra updates to the existing Octave OCL project with more supporting PC devices like Intel GPU, AMD APUs, and Apple Silicon GPU as well as mobile devices like Qualcomm Adreno and ARM Mali GPUs. Ever since started providing extra GPU Compute support, it has been for OpenCL-enabled hardware and this promise will not change for OpenSource community. My profile is here: https://www.researchgate.net/profile/Jinchuan-Tang-4. Meanwhile, I have a plan to add clBlast to this project to speed up matrix multiplication by 5 times or more. Please join my summon to tuning your GPU to make clBlast better: https://github.com/CNugteren/CLBlast/issues/1#issuecomment-1570253475
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Menagerie
A collection of high-quality models for the MuJoCo physics engine
MuJoCo Menagerie, developed by Google DeepMind, is a curated collection of high-quality simulation models designed for use with the MuJoCo physics engine. It serves as a comprehensive library of accurate and ready-to-use robotic, biomechanical, and mechanical models, ensuring users can perform reliable simulations without having to build or tune models from scratch. The repository aims to improve reproducibility and quality across robotics research by providing verified models that adhere to consistent design and physical standards. Each model directory contains its 3D assets, MJCF XML definitions, licensing information, and example scenes for visualization and testing. The collection spans a wide range of categories including robotic arms, humanoids, quadrupeds, mobile manipulators, drones, and biomechanical systems. Users can access models directly via the robot_descriptions Python package or by cloning the repository for use in interactive MuJoCo simulations. -
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The Neural Process Family
This repository contains notebook implementations
Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance. -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. The project also provides scripts to fetch and preprocess monolingual data, learn BPE codes, and train cross-lingual embeddings that bootstrap unsupervised alignment between languages. Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training. -
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Cookbook (Google Gemini)
Examples and guides for using the Gemini API
The Gemini Cookbook is an official repository of examples and guides for using Google’s Gemini API. It provides a structured learning path with quick-start tutorials for beginners and practical examples for advanced users. The repository covers a wide range of Gemini capabilities, including text, images, video, speech, robotics, and multimodal interactions. It highlights newly introduced features such as Gemini 2.5 models (Flash and Pro), Gemini’s native image generation, Veo for video generation, robotics-focused reasoning models, and Lyria for TTS and music generation. The Cookbook also includes tutorials on advanced API workflows such as grounding answers with external tools, batch-mode request handling, and live multimodal interactivity with LiveAPI. Designed as a hands-on resource, it helps developers quickly explore Gemini’s potential while serving as a reference for integrating cutting-edge multimodal AI into applications.
