Search Results for "linux windows agent"
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Showing 73 open source projects for "linux windows agent"
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AI Researcher
An autonomous AI researcher
AI Researcher is an experimental open-source project that demonstrates how multiple AI agents can collaborate to conduct complex research tasks from start to finish with minimal human intervention. It orchestrates agents that can generate research questions, perform literature reviews, execute experiments, analyze results, and synthesize findings into structured outputs like reports or code. Each agent operates with clear roles — such as researcher, analyst, and summarizer — and they... -
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LangChain Open Deep Research
Fully open source deep research agent
Open Deep Research is a configurable, fully open-source agent for producing detailed research reports from complex questions. It separates work across models used for summarization, active research, information compression, and final report generation. Users can select from multiple language model providers as long as the chosen models support tool calling and structured outputs. Search can be powered by several APIs, native provider search, or external tools connected through MCP. The agent... -
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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. It can automatically generate code for experiments, run... -
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Local Deep Research
95% on SimpleQA (e.g. Qwen3.6-27B on a 3090)
Local Deep Research is an open-source AI-powered research assistant designed to perform deep, iterative investigations by combining large language models with multi-source search capabilities. It runs locally, giving users full control over their data, privacy, and infrastructure while supporting both local and cloud-based LLMs. The system breaks down complex queries into smaller steps, performs parallel searches across web and academic sources, and generates structured, citation-backed... -
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Concordia
Crowdsourcing platform for full text transcription and tagging
Concordia is a platform for crowdsourcing transcription and tagging of text in digitized images. It was developed by the Library of Congress so that volunteers of all backgrounds could transcribe and tag digitized images of manuscripts and typed materials from the Library’s collections that could not otherwise be done by optical character recognition. -
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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of... -
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Archivematica
Free and open-source digital preservation system
Archivematica is a web- and standards-based, open-source application which allows your institution to preserve long-term access to trustworthy, authentic, and reliable digital content. Our target users are archivists, librarians, and anyone working to preserve digital objects. You are free to copy, modify, and distribute Archivematica with attribution under the terms of the AGPLv3 license. Archivematica is an open-source application based on recognized standards that makes it possible to... -
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Megatron-LM
Ongoing research training transformer models at scale
Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and... -
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Open Source Vizier
Python-based research interface for blackbox
Open Source (OSS) Vizier is a Python-based interface for blackbox optimization and research, based on Google’s original internal Vizier, one of the first hyperparameter tuning services designed to work at scale. Allows a user to setup an OSS Vizier Server, which can host black-box optimization algorithms to serve multiple clients simultaneously in a fault-tolerant manner to tune their objective functions. Defines abstractions and utilities for implementing new optimization algorithms for... -
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PRAXIST
Autonomous research system for measurable computer-executable research
PRAXIST is an autonomous research system for measurable, computer-executable problems. It turns an already runnable project into a persistent research process instead of a series of disconnected prompts. Parallel research peers explore competing hypotheses and implementations while evaluators convert outcomes into structured evidence. That evidence is carried across generations so later work can build on promising strategies and avoid repeating weak ones. The system supports multi-metric... -
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US Job Market Visualizer
A research tool for visually exploring Bureau of Labor Statistics
US Job Market Visualizer is a research tool for interactively exploring Occupational Outlook Handbook data from the U.S. Bureau of Labor Statistics. It organizes information for 342 occupations into a treemap where area represents employment and color can represent different metrics. Users can compare projected growth, median pay, education requirements, and estimated digital AI exposure. The repository includes tools for scraping BLS pages, converting them to structured data, and building... -
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Growth Lab
An end-to-end growth tool that understands the product
Growth Lab is an open-source agentic growth workspace that connects product understanding, market research, execution, measurement, and iteration. It uses Codex or Claude Code as the runtime and treats natural-language conversations as the control surface. Collectors gather product, market, content, and channel evidence, while executor skills help create, publish, review, and coordinate growth work. Each growth model follows an observe-act-review loop and keeps persistent memory of evidence,... -
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Recommenders
Best practices on recommendation systems
The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several... -
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moebinv
C++ libraries for manipulations in non-Euclidean geometry
These are two C++ libraries for symbolic, numeric and graphical manipulations in non-Euclidean geometry. There is GUI which allows to interact with these libraries by mouse clicks. On a dipper level the first library Cycle implements basic operations on cycles (quadrics) through FSCc construction. The second library Figure operates on ensembles of cycles connected by Moebius-invariant relations, e.g. orthogonality. Both libraries are based on the Clifford algebra capacities of the... -
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xrayutilities
a package with useful scripts for X-ray diffraction physicists
xrayutilities is a python package used to analyze x-ray diffraction data. It can support with performing diffraction experiments and used for common steps in the data analysis. It can read experimental data from several data formats (spec, edf, xrdml, ...); convert them to reciprocal space for arbitrary goniometer geometries and different detector systems (point, linear as well as area detectors); for further processing the data can be gridded (transformed to a regular grid). More... -
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Libro
An interactive program for statistical analysis of texts
A cross-platform text analysis program written in Python and Free Pascal/Lazarus which scans a whole text file (in plain text, HTML, EPUB, or ODT formats) and ranks all used words according to frequency, performing a quantitative analysis of the text using Shannon-Weaver information statistic and Zipf power law function. It counts words, sentences, chars, spaces, and syllables. Also computes readability indexes (Gunning-Fog, Coleman-Liau, Automated Readability Index (ARI), SMOG grade,... -
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Zettel
Zettel allows taking notes from several references and organizing them
Zettel is a program for taking notes from bibliographic references. Instead of marking the text on paper and then going crazy looking for where the copy ended up, the notes are saved in a database, linked to the reference from where they were copied. Notes can be tagged and retrieved in several ways. Zettel é um programa para fichamento de referências bibliográficas. Ao invés de marcar o texto em papel e depois enlouquecer procurando onde a cópia foi parar, as notas são guardadas numa... -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to... -
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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... -
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Catalyst
Accelerated deep learning R&D
...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. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving). -
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Zero Install is a decentralised cross-distribution software installation system. Create one package that works everywhere! With dependency handling and automatic updates, full support for shared libraries, and integration with native package managers
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GeoSolver
A Python library for solving geometric constraint problems
GeoSolver is a Python library for solving geometric constraint problems. A graphical testing and demostration application, the Geometric Constraint Workbench is included with the software. -
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Python/FEniCS Examples
phase-field simulation and other examples with Python/FEniCS
The main goal of this project was developing phase-field simulations of lithium dendrite growth with FEniCS programmed in Python. The problem was based in the grand potential-based model of Zijian Hong and Venkatasubramanian Viswanathan (https://doi.org/10.1021/acsenergylett.8b01009) . Some simpler examples were developed before for a first approach with FEniCS: heat equation and combustion model. -
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NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging... -
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Pydicom by examples
Basic and intermediate examples of DICOM library with Jupyter
Basic and intermediate examples to read, modify and write DICOM files with Python code using Jupyter - To install Jupyter - https://jupyter.org/install ====== All examples are based on Pydicom. An open source library - https://pydicom.github.io/
