Search Results for "research" - Page 6
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Showing 3506 open source projects for "research"
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ProtoMotions
ProtoMotions is a GPU-accelerated simulation and learning framework
ProtoMotions 3 is a GPU-accelerated framework for training physically simulated digital humans and humanoid robots. It provides modular environments and learning components for animation, robotics, and reinforcement learning research. Large motion datasets such as AMASS and BONES can be divided across multiple GPUs for scalable policy training. A built-in PyRoki workflow retargets human motion data to different robot bodies with a single command. Policies can be tested across Isaac Gym, Isaac Lab, Newton, Genesis, MuJoCo, and high-fidelity Isaac Sim environments. ... -
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SIA
AI framework to autonomously improve the performance of any AI system
...It uses an iterative loop where a meta-agent creates or updates a task-specific target agent, while a feedback agent studies results and proposes improvements. The framework can refine both the harness around the task and the agent implementation itself. It is aimed at research and experimentation across tasks such as machine learning benchmarks, legal classification, code optimization, and scientific workflows. It includes built-in tasks, a command-line runner, and a visual dashboard for following generations as they evolve. It also lets users define custom providers, profiles, seed agents, and task directories without changing the core code. -
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Knowledge Work Plugins
Open source repository of plugins intended for knowledge workers
...It is designed to give AI assistants structured domain instructions rather than relying on generic chat behavior. The repository includes plugins for practical office, research, legal, and business workflows, with each plugin stored as editable Markdown. This makes the system easy to inspect, fork, customize, and adapt to an organization’s own process. Its goal is to help agents perform repeatable knowledge work with clearer expectations, domain constraints, and workflow patterns. The project is best suited for teams that want reusable AI work instructions without building a full application around them. -
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wasm-tools
CLI and Rust libraries for low-level manipulation of WebAssembly
...The toolkit also supports advanced operations like mutating, shrinking, and generating test cases for WebAssembly modules, making it particularly valuable for testing, debugging, and research purposes. In addition to its CLI, wasm-tools exposes many of its capabilities as reusable Rust libraries, allowing developers to integrate WebAssembly manipulation directly into their applications. The project actively implements and supports evolving WebAssembly proposals, ensuring compatibility with the latest features of the ecosystem. -
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min-sized-rust
How to minimize Rust binary size
min-sized-rust is a practical, research-oriented repository that demonstrates how to aggressively reduce the size of compiled Rust binaries through a combination of compiler flags, linker optimizations, and code-level techniques. By default, Rust prioritizes performance, debuggability, and compile speed, which often results in relatively large binaries, especially in debug mode or when including standard libraries. -
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Recursive Language Models
General plug-and-play inference library for Recursive Language Models
RLM (short for Reinforcement Learning Models) is a modular framework that makes it easier to build, train, evaluate, and deploy reinforcement learning (RL) agents across a wide range of environments and tasks. It provides a consistent API that abstracts away many of the repetitive engineering patterns in RL research and application work, letting developers focus on modeling, experimentation, and fine-tuning rather than infrastructure plumbing. Within the framework, you can define custom agents, environments, policy networks, and reward structures while leveraging built-in dataset utilities, logging, and checkpointing for reproducible experiments. ... -
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Kodus
AI code reviews, just like your senior dev would do
...It provides a structured set of tools and abstractions that help teams design agent behaviors, orchestrate data pipelines, optimize inference, and integrate AI capabilities with applications or services. The platform often includes model management, scalable training workflows, and orchestration patterns that help teams move from research or prototypes to production-ready AI deployments. Through configurable pipelines and a focus on modularity, it supports experimentation while maintaining reproducibility and performance. Its tooling is typically designed to handle real-world imperatives like logging, monitoring, versioning, and hooking into operational infrastructure. -
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CoreNet
CoreNet: A library for training deep neural networks
...Its distributed runtime manages synchronization, load balancing, and mixed-precision computation to maximize throughput while minimizing communication bottlenecks. CoreNet integrates tightly with Apple’s proprietary ML stack and hardware, serving as the foundation for research in computer vision, language models, and multimodal systems within Apple AI. The framework includes monitoring tools, fault tolerance mechanisms, and efficient checkpointing for massive training runs. -
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Grey Wolf Optimizer for Path Planning
Grey Wolf Optimizer (GWO) path planning/trajectory
...Users can adjust objective function weights and experiment with multiple heuristic search strategies to explore optimal solutions. This project demonstrates applications in multi-agent and multi-UAV cooperative path planning, making it useful for research and educational purposes in the field of intelligent optimization and robotics. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Metarget
Framework for automatic construction of vulnerable infrastructures
Metarget = meta- + target, a framework providing automatic constructions of vulnerable infrastructures, used to deploy simple or complicated vulnerable cloud native targets swiftly and automatically. During security research, we might find that the deployment of a vulnerable environment often takes much time, while the time spent on testing PoC or ExP is comparatively short. In the field of cloud-native security, thanks to the complexity of cloud-native systems, this issue is more terrible. There are already some excellent security projects like Vulhub, and VulApps in the open-source community, which pack vulnerable scenes into container images so that researchers could utilize them and deploy scenes quickly. ... -
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RecBole
A unified, comprehensive and efficient recommendation library
...RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. It can be installed from pip, conda and source, and is easy to use. We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation and sub-packages. -
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DevSec Hardening
This Ansible collection provides battle tested hardening
...The project founders where tasked with the challenge to automate different security requirements of Deutsche Telekom for their infrastructure. Deutsche Telekom, T-Labs and Telekom Security funded the initial research and allowed the team to open source the automation. -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
Jupyter Docker Stacks provides a curated set of ready-to-run Docker container images that bundle Jupyter applications with popular data science and computing tools, enabling users to quickly start working in a reproducible environment. These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run... -
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mgrep
A calm, CLI-native way to semantically grep everything, like code
...It also includes features such as background indexing to keep your search index up to date without interrupting your workflow and web search integration to expand the scope of queries beyond local files. Designed for both programmers and agents, it integrates naturally into development and research workflows while offering thoughtful defaults that keep output clean and informative. -
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Best-websites-a-programmer-should-visit
Some useful websites for programmers
Best-websites-a-programmer-should-visit is a living, community-curated directory of links that programmers consistently find useful throughout their careers. Rather than being a random bookmark dump, it organizes resources into practical categories such as algorithms, competitive programming, reading materials, podcasts, newsletters, interview prep, design, security, performance, and more. The list aims to reduce the “what should I learn next?” friction by pointing you to high-signal,... -
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LangExtract
A Python library for extracting structured information
LangExtract is a Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text—such as clinical notes, research papers, or literary works—based on user-defined instructions. It is designed to transform free-form text into reliable, schema-constrained data while maintaining traceability back to the source material. Each extracted entity is precisely grounded in its original context, allowing visual inspection and validation via automatically generated interactive HTML visualizations. ... -
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GenAI Processors
GenAI Processors is a lightweight Python library
GenAI Processors is a lightweight Python library for building modular, asynchronous, and composable AI pipelines around Gemini. Its central abstraction is the Processor, a unit of work that consumes an asynchronous stream of parts (text, images, audio, JSON) and produces another stream, making it natural to chain operations and keep everything streaming end-to-end. Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which... -
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K8tools
Security- and exploitation-oriented utilities and proof-of-concepts
K8tools is a large, curated GitHub repository collecting dozens (hundreds) of security- and exploitation-oriented utilities, proof-of-concepts, and payloads aimed at penetration testing, privilege escalation, and vulnerability exploitation. The project bundles exploits for many well-known CVEs, remote get-shell scripts, local privilege-escalation helpers, credential-harvesting utilities, scanning and brute-force tools, and a variety of platform-specific binaries and archives organized into... -
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Image Harmonization Dataset iHarmony4
The first large-scale public benchmark dataset for image harmonization
...The iHarmony4 dataset comprises four sub-datasets (HCOCO, HAdobe5k, HFlickr, Hday2night), each making composite images by combining a foreground from one image with a background from another, along with associated ground truth harmonized images and foreground masks. The dataset is intended as a benchmark resource to enable and standardize research in image harmonization. Each composite sample has: composite image, foreground mask, and corresponding real harmonized image. -
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BLESSED for Android
BLESSED, a Bluetooth Low Energy (BLE) library for Android
BLESSED is a very compact Bluetooth Low Energy (BLE) library for Android 5 and higher, that makes working with BLE on Android very easy. It takes care of many aspects of working with BLE you would normally have to take care of yourself. The BLESSED library was inspired by CoreBluetooth on iOS and provides the same level of abstraction, but at the same time it also stays true to Android by keeping most methods the same and allowing you to work with the standard classes for Services,... -
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EthereumJS Monorepo
Monorepo for the Ethereum VM TypeScript Implementation
...They are complemented by helper packages like RLP for data encoding/decoding or Util, providing helper functionalities like (byte) conversion, signatures, types and others. Finally, the EthereumJS Execution Client (EthereumJS) has been in active development for some time now. It already serves a variety of purposes like testing, research (e.g. EIPs) and developer tooling. -
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Opacus
Training PyTorch models with differential privacy
...Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
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DocTR
Library for OCR-related tasks powered by Deep Learning
DocTR provides an easy and powerful way to extract valuable information from your documents. Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document... -
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Deepchecks
Test Suites for validating ML models & data
...Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. ... -
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Top Deep Learning Projects
A list of popular github projects related to deep learning
...Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly discover reputable and well-maintained repositories. This way one can survey state-of-the-art projects, find learning resources, or pick stable libraries for production — without manually sifting through hundreds of repos. The repository is openly licensed under MIT, making it easy to fork, extend, or contribute updates (e.g. adding newer projects or reordering by recent popularity).
