Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 19
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Showing 1130 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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Project Planning and Management Software | PlanviewPlanview® ProjectAdvantage (formerly Sciforma) is an enterprise-centric project and portfolio management (PPM) software designed to enable change, drive innovation, and lead in a company's digital transformation. With ProjectAdvantage, teams can strategically track and monitor project data in order to make relevant decisions. It offers multiple features focused on strategic management, functional management, and execution management. A highly scalable and cost-effective solution, ProjectAdvantage is available in various deployment models.
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Curtain LogTrace File Activity MonitoringCurtain LogTrace File Activity Monitoring is an enterprise file activity monitoring solution. It tracks user actions: create, copy, move, delete, rename, print, open, close, save. Includes source/destination paths and disk type. Perfect for monitoring user file activities.
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OM1
Modular AI runtime for robots
...The framework integrates reasoning modules, planning strategies, and tool interfaces that allow agents to operate in dynamic environments. Developers can extend the system by connecting new tools, services, or data sources to the agent architecture. The platform also includes mechanisms for coordinating workflows and managing the state of ongoing tasks. -
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MemOS
AI memory OS for LLM and Agent systems
...The project explores rethinking system abstractions by tightly coupling computation with memory objects so that programs can operate on large datasets without expensive serialization or context switching. It aims to support advanced workflows like persistent in-memory data structures, crash-resilient state handling, and seamless sharing of data across tasks without copying. By abandoning some of the historical assumptions of Unix-style operating systems, MemOS attempts to unlock new performance and scalability tradeoffs for applications that need high throughput and low latency on memory-intensive workloads. -
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UCP Python SDK
The official Python SDK for UCP
...UCP itself is a modern, open-source standard that empowers seamless commerce interactions between platforms, AI agents, merchants, and payment providers without requiring bespoke integrations for every participant in the commerce ecosystem. This SDK provides Pydantic models for UCP schemas, making it easy for Python developers to construct, validate, and serialize protocol messages and data structures according to the UCP specification. By adhering to the official protocol standards, applications built on this SDK can participate in tasks like capability discovery, checkout flows, order management, and more, while remaining interoperable across different UCP implementations and surfaces. -
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D4RL
Collection of reference environments, offline reinforcement learning
D4RL (Datasets for Deep Data-Driven Reinforcement Learning) is a benchmark suite focused on offline reinforcement learning — i.e., learning policies from fixed datasets rather than via online interaction with the environment. It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. -
Create and manage the email signature you needWith WiseStamp it’s easy to unify your brand and turn your emails into a powerful marketing tool. Get the most out of your emails with a professionally designed custom email signature.
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Instructor
Structured outputs for llms
Instructor is a tool that enables developers to extract structured data from natural language using Large Language Models (LLMs). Integrating with Python's Pydantic library allows users to define desired output structures through type hints, facilitating schema validation and seamless integration with IDEs. Instructor supports various LLM providers, including OpenAI, Anthropic, Litellm, and Cohere, offering flexibility in implementation. -
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Interpretable machine learning
Book about interpretable machine learning
...As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. The later chapters focus on analyzing complex models and their decisions. ... -
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bbox-visualizer
Make drawing and labeling bounding boxes easy as cake
Make drawing and labeling bounding boxes easy as cake. This package helps users draw bounding boxes around objects, without doing the clumsy math that you'd need to do for positioning the labels. It also has a few different types of visualizations you can use for labeling objects after identifying them. There are optional functions that can draw multiple bounding boxes and/or write multiple labels on the same image, but it is advisable to use the above functions in a loop in order to have... -
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NVIDIA NeMo
Toolkit for conversational AI
...NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. ... -
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Elasticsearch MCP Server
A Model Context Protocol (MCP) server implementation
This MCP server implementation provides interaction capabilities with Elasticsearch and OpenSearch, enabling functionalities such as document searching, index analysis, and cluster management through a set of tools. -
Virtual data rooms designed to achieve better outcomesAnsarada is a SaaS company that provides world-leading AI-powered Virtual Data Rooms and dealmaking tools. These tools include advanced AI insights and automation, next level Q&A and collaboration, plus pre-built, digitized and customizable workflows and checklists - known as Pathways - for M&A, capital raising, business audits, tenders and other high stakes outcomes.
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Papermerge
Open Source Document Management System for Digital Archives
...Each user can be assigned different permissions to perform only a specific kind of action e.g. view only documents from a specific folder. OCR technology is vital part of Papermerge. It extracts text information from scanned documents, PDF, JPEG, TIFF files. -
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TensorFlow
TensorFlow is an open source library for machine learning
Originally developed by Google for internal use, TensorFlow is an open source platform for machine learning. Available across all common operating systems (desktop, server and mobile), TensorFlow provides stable APIs for Python and C as well as APIs that are not guaranteed to be backwards compatible or are 3rd party for a variety of other languages. The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow... -
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Darts
A python library for easy manipulation and forecasting of time series
...The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting. We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool (conda, venv, virtualenv with or without virtualenvwrapper). -
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Windows-MCP
MCP server enabling AI agents to control and automate Windows OS
Windows-MCP is a lightweight open source project designed to connect AI agents with the Windows operating system through a Model Context Protocol server. It acts as a bridge that allows large language models to directly interact with desktop environments, enabling automated control over applications, files, and system interfaces. Windows-MCP provides capabilities such as file navigation, application management, UI interaction, and QA testing workflows, making it suitable for building autonomous desktop agents. It focuses on native interaction with Windows UI elements rather than relying on traditional computer vision techniques, which simplifies integration and improves efficiency. ... -
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Claude Scientific Skills
A set of ready to use Agent Skills for research, science, engineering
Claude Scientific Skills is a large open source collection of ready-to-use scientific capabilities that extend AI coding agents into full research assistants. The project provides more than 170 curated skills covering domains such as genomics, drug discovery, medical imaging, physics, and advanced data analysis. Each skill bundles documentation, examples, and tool integrations so agents can reliably execute complex multi-step scientific workflows. The framework follows the open Agent Skills standard and works with multiple AI development environments including Claude Code, Cursor, and Codex. Its primary goal is to reduce the friction of scientific computing by giving AI agents structured access to specialized libraries, databases, and research pipelines. ... -
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Instructor Python
Structured outputs for llms
Instructor is a Python library that bridges OpenAI responses with structured data validation using Pydantic models. It lets developers specify expected output schemas and ensures that the responses from OpenAI APIs are automatically parsed and validated against those models. This makes integrating LLMs into structured workflows safer and more predictable, especially in production applications. -
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Kor
LLM
This is a half-baked prototype that “helps” you extract structured data from text using LLMs. Specify the schema of what should be extracted and provide some examples. Kor will generate a prompt, send it to the specified LLM and parse out the output. You might even get results back. -
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pycm
Multi-class confusion matrix library in Python
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. -
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NVIDIA Isaac GR00T
NVIDIA Isaac GR00T N1.5 is the world's first open foundation model
...It accepts multimodal inputs—such as language and images—and uses a diffusion transformer architecture built upon vision-language encoders, enabling adaptive robot behaviors across diverse environments. It is designed to be customizable via post-training with real or synthetic data. The vision-language model remains frozen during both pretraining and finetuning, preserving language understanding and improving generalization. Streamlined MLP connection between vision encoder and LLM with added layer normalization. -
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CAMEL AI
Finding the Scaling Law of Agents. A multi-agent framework
...Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. -
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Mentat
Mentat - The AI Coding Assistant
Mentat is the AI tool that assists you with any coding task, right from your command line. Unlike Copilot, Mentat coordinates edits across multiple locations and files. And unlike ChatGPT, Mentat already has the context of your project, no copy and pasting is required. Run Mentat from within your project directory. Mentat uses Git, so if your project doesn't already have Git set up, run git init. List the files you would like Mentat to read and edit as arguments. Mentat will add each of them to context, so be careful not to exceed the GPT-4 token context limit. -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. Using this dataset, the project constructs matchup features that represent team performance trends and contextual information about each game. Machine learning models are then trained to estimate the probability that a team will win a game as well as whether the total score will fall above or below the sportsbook’s predicted total. ... -
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Integuru v0
The first AI agent that builds permissionless integrations
...Instead of relying on official developer documentation or publicly available APIs, the system analyzes network traffic generated by user interactions within a web application. Developers capture browser requests and authentication data, which the agent then uses to infer the structure of the platform’s internal API endpoints. Based on this information, the system generates executable code that can replicate the original action programmatically. This approach allows developers to automate workflows and build integrations with services that do not provide official APIs or developer tools. ... -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
...The project addresses the long-standing challenge that efficient CUDA programming typically requires deep hardware expertise by training an autonomous coding agent capable of iterative improvement through execution feedback. Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. ... -
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LingBot-VLA
A Pragmatic VLA Foundation Model
...Because LingBot-VLA includes not just the model weights but also a full production-ready codebase with tools for data handling, training, and evaluation, developers can adapt it to custom robots or simulation environments efficiently. -
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RecBole
A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library. We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms.
