Search Results for "computer" - Page 5
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Showing 507 open source projects for "computer"
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Create a personalized AI chatbot for each team in minutesQueryPal is the lifeline your team needs. Our AI chatbot integrates seamlessly with your communication channels, using advanced language understanding to identify and auto-answer repetitive questions — in seconds.
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Attack Surface Management | Criminal IP ASMCriminal IP’s Attack Surface Management (ASM) is a threat-intelligence–driven platform that continuously discovers, inventories, and monitors every internet-connected asset associated with an organization, including shadow and forgotten resources, so teams see their true external footprint from an attacker’s perspective. The solution combines automated asset discovery with OSINT techniques, AI enrichment and advanced threat intelligence to surface exposed hosts, domains, cloud services, IoT endpoints and other Internet-facing vectors, capture evidence (screenshots and metadata), and correlate findings to known exploitability and attacker tradecraft. ASM prioritizes exposures by business context and risk, highlights vulnerable components and misconfigurations, and provides real-time alerts and dashboards to speed investigation and remediation.
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CogView4
CogView4, CogView3-Plus and CogView3(ECCV 2024)
...It emphasizes bilingual usability, making it well-suited for cross-lingual multimodal applications. The model also supports fine-tuning and downstream customization, extending its applicability to creative content generation, human–computer interaction, and research on vision-language alignment. -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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AI Deadlines
AI conference deadline countdowns
AI Deadlines is an open-source project that provides a centralized system for tracking important submission deadlines for major artificial intelligence and machine learning conferences. The repository powers a website that displays countdown timers and structured information for top research conferences across subfields such as computer vision, natural language processing, machine learning, and robotics. The project maintains a curated dataset of conferences that includes metadata such as submission deadlines, abstract deadlines, event dates, conference locations, and related information. Researchers and students use the platform to plan their paper submissions and manage academic schedules without manually tracking multiple conference announcements. ... -
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Z80-μLM
Z80-μLM is a 2-bit quantized language model
...The repository provides a complete workflow where you train or fine-tune conversational models in Python, then export them into a format that can be executed on classic Z80 systems. A key deliverable is producing CP/M-compatible .COM binaries, enabling a genuinely vintage “chat with your computer” experience on real hardware or accurate emulators. The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment. -
More Bookings. Better Experience.The all-in-one solution built to help you stay organised and get more bookings with thousands of connections to online travel agencies (OTAs), resellers and suppliers.
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Cua
Open-source infrastructure for Computer-Use Agents. Sandboxes
Cua is an open-source command-line utility and workflow orchestrator designed to help developers define, compose, and run common tasks with a unified interface, promoting consistency and reuse across projects. It introduces a declarative syntax for specifying build scripts, automation pipelines, environment setups, and project-specific commands so contributors don’t need to memorize disparate scripts or tooling across languages and ecosystems. Cua can also manage task dependencies, handle... -
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Bytebot
Bytebot is an AI desktop agent that automates computer tasks
Bytebot is an open-source AI assistant and automation framework built to help developers and teams automate repetitive software development tasks, accelerate coding workflows, and integrate intelligent assistance into everyday tooling. It typically includes capabilities for code generation, refactoring suggestions, automated testing assistance, and integration with source control systems to make commits or generate pull requests guided by natural language prompts. Bytebot can be embedded... -
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chatd
Chat with your documents using local AI
chatd is an open-source desktop application that allows users to interact with their documents through a locally running large language model. The software focuses on privacy and security by ensuring that all document processing and inference occur entirely on the user’s computer without sending data to external cloud services. It includes a built-in integration with the Ollama runtime, which provides a cross-platform environment for running large language models locally. The application typically runs models such as Mistral-7B and allows users to load and analyze documents while asking questions in natural language. ... -
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StarVector
StarVector is a foundation model for SVG generation
StarVector is a multimodal foundation model designed for generating Scalable Vector Graphics (SVG) from images or textual descriptions. The system treats vector graphics creation as a code generation problem, producing SVG code that can render detailed vector images. Its architecture combines computer vision techniques with language modeling capabilities so it can understand visual inputs and textual prompts simultaneously. The model converts raster images or text instructions into structured vector representations, enabling high-quality vectorization and design generation. This approach allows StarVector to create scalable graphics that maintain visual quality regardless of resolution, which is especially useful for design tools and illustration workflows. ... -
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Open Model Zoo
Pre-trained Deep Learning models and demos
Open Model Zoo is a large repository of high-quality pre-trained deep learning models and demonstration applications designed to work with the OpenVINO™ toolkit, offering a comprehensive starting point for a wide range of AI and computer vision workloads. It includes hundreds of models covering object detection, classification, segmentation, pose estimation, speech recognition, text-to-speech, and more, many of which are already converted into formats optimized for inference on CPUs, GPUs, VPUs, and other accelerators supported by OpenVINO. In addition to model files, Open Model Zoo provides demo applications that show realistic usage patterns and help developers quickly prototype and understand inference pipelines in C++, Python, or via the OpenCV Graph API. ... -
Caller ID Reputation provides the most comprehensive view of your caller ID scores across all carriersKeep your agents on the phone with increased connection rates by monitoring your phone number reputation across all major carriers and call blocking apps.
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NVIDIA NeMo Framework
Scalable generative AI framework built for researchers and developers
NVIDIA NeMo is a scalable, cloud-native generative AI framework aimed at researchers and PyTorch developers working on large language models, multimodal models, and speech AI (ASR and TTS), with growing support for computer vision. It provides collections of domain-specific modules and reference implementations that make it easier to pre-train, fine-tune, and deploy very large models on multi-GPU and multi-node infrastructure. NeMo 2.0 introduces a Python-based configuration system, replacing YAML with more flexible, programmable configs that can be versioned and composed for different experiments. ... -
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Dough
Dough is a open source tool for steering AI animations with precision
...To unlock the multitude of control types and artistic flows possible with AI, we want to build tooling and infrastructure to empower a community of tool-builders, who in turn empower a world of budding artists. If you have a powerful computer, you can run Dough locally for free. If you don't, you can use our Discord bot for a small fee. -
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Machine Learning Notebooks
Machine Learning Notebooks
...Each notebook is intentionally lightweight, avoiding unnecessary complexity so that users can easily understand the core concepts and adapt the code to their own projects. The repository supports a wide range of applications, including natural language processing, computer vision, and general deep learning workflows. It is also designed to be easily extensible, allowing developers and researchers to build upon the provided examples without needing to restructure the codebase. -
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OSMO
The developer-first platform for scaling complex Physical AI workloads
...It was originally built internally at NVIDIA to support robotics and embodied AI systems, where workflows span multiple stages such as data generation, training, simulation, and hardware testing. The platform addresses what NVIDIA refers to as the “three computer problem” by unifying these stages into a single pipeline defined through simple YAML configurations. It enables users to orchestrate tasks across Kubernetes clusters, automatically managing dependencies, scheduling, and resource allocation without requiring deep infrastructure expertise. -
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Automaton
The first AI that can earn its own existence, replicate, and evolve
...The repository focuses on giving developers and researchers a programmable environment to experiment with state machines, language recognition models, and algorithmic behaviors that mirror theoretical computer science constructs. Its architecture emphasizes modularity so users can extend or customize automaton types without rewriting core logic. The project is particularly useful in educational contexts, where visualizing or testing automata behavior helps reinforce concepts such as deterministic and nondeterministic machines. Developers can also use it as a foundation for research prototypes involving parsing, pattern recognition, or language processing. ... -
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AI-Job-Notes
AI algorithm position job search strategy
AI-Job-Notes is a pragmatic notebook for landing roles in machine learning, computer vision, and related engineering tracks. It assembles study paths, checklists, and interview prep materials, but also covers job-search mechanics—portfolio building, resume patterns, and communication tips. The emphasis is on doing: practicing with project ideas, setting up reproducible experiments, and showcasing results that convey impact. -
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compromise
Modest natural-language processing
Language is complicated and there's a gazillion words. Compromise is a javascript library that interprets and pre-parses text and makes some reasonable decisions so things are way easier. Compromise tries its best to parse text. it is small, quick, and often good-enough. It is not as smart as you'd think. Conjugate and negate verbs in any tense. Play between plural, singular and possessive forms. Interpret plain-text numbers. Handle implicit terms. Use it on the client-side or as an... -
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Codebase to Course
A Claude Code skill that turns any codebase into an HTML course
Codebase to Course is an AI-powered development tool that converts any software repository into a fully interactive educational experience presented as a self-contained HTML course. It is implemented as a skill for Claude Code and is designed to help users understand how a codebase works without requiring a formal computer science background. The tool analyzes the structure and behavior of a project and generates a visually rich, scroll-based course that includes diagrams, animations, and contextual explanations. It pairs real code with plain-English interpretations, allowing learners to follow execution flows and grasp concepts intuitively. The generated course also includes interactive quizzes and glossary tooltips to reinforce understanding through application rather than memorization. ... -
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Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
...It is structured as a series of progressively complex Jupyter notebooks that emphasize writing and understanding code before diving into theory, reinforcing learning through repetition and application. The material covers core machine learning workflows including regression, classification, computer vision, natural language processing, and time series forecasting, allowing users to build a well-rounded understanding of modern AI tasks. It also integrates milestone projects that simulate real-world scenarios, helping users translate abstract concepts into deployable solutions. -
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RF-DETR
RF-DETR is a real-time object detection and segmentation
RF-DETR is an open-source computer vision framework that implements a real-time object detection and instance segmentation model based on transformer architectures. Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. -
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learning
A log of things I'm learning
The learning repository by Amit Chaudhary is a continuously updated log of concepts, technologies, and skills related to software engineering and computer science. Rather than being a traditional software library, the repository acts as a structured knowledge base documenting the author’s ongoing learning journey across topics such as programming, system design, machine learning, and generative AI. The content is organized into categories that cover both core engineering skills and adjacent technologies, enabling readers to follow a practical roadmap for developing strong technical foundations. ... -
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Machine Learning Systems
Introduction to Machine Learning Systems
...The repository includes textbook content, supporting labs, and companion tools such as TinyTorch to help learners move from theory to hands-on experimentation. Its mission is to establish AI systems engineering as a foundational discipline alongside traditional software and computer engineering. The project is structured to guide users through reading, building, and deploying workflows, including running labs on edge devices like Arduino and Raspberry Pi. -
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Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. ... -
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SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it
...We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, and view, edit, and execute code files. We call this an Agent-Computer Interface (ACI). -
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Label Sleuth
Open source no-code system for text annotation and building of text
...Domain experts can quickly start labeling their data through an intuitive user interface. Developed by researchers across industry and academia, Label Sleuth incorporates the latest research from human-computer interaction, natural language processing, and artificial intelligence. Label Sleuth has been designed with an extensible architecture allowing the easy integration of new components, such as additional model architectures or active learning techniques. -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
face.evoLVe is a high-performance face recognition library designed for research and real-world applications in computer vision. The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems.
