Search Results for "data capture framework" - Page 7
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Showing 2278 open source projects for "data capture framework"
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Koito
Koito is a modern, themeable scrobbler
Koito is a lightweight, self-hostable collaborative knowledge base and note-taking platform designed to help teams and individuals capture, organize, and link ideas with the reliability of a local or private server rather than relying on managed SaaS tools. It combines concepts from wikis, Zettelkasten systems, and modern note apps by letting you create interconnected notes, bidirectional links, and structured collections that mirror how knowledge naturally grows. -
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DeerFlow
Deep Research framework, combining language models with tools
DeerFlow is an open-source, community-driven “deep research” framework / multi-agent orchestration platform developed by ByteDance. It aims to combine the reasoning power of large language models (LLMs) with automated tool-use — such as web search, web crawling, Python execution, and data processing — to enable complex, end-to-end research workflows. Instead of a monolithic AI assistant, DeerFlow defines multiple specialized agents (e.g. -
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bettercap
The Swiss Army knife for 802.11, BLE, IPv4 and IPv6 networks
bettercap is a powerful, easily extensible and portable framework written in Go which aims to offer to security researchers, red teamers and reverse engineers an easy to use, all-in-one solution with all the features they might possibly need for performing reconnaissance and attacking WiFi networks, Bluetooth Low Energy devices, wireless HID devices and Ethernet networks. -
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Live-Forensicator
A suite of Tools to aid Incidence Response and Live Forensics
Live-Forensicator is a toolkit intended for live forensic collection and initial triage on Windows machines. It automates the capture of volatile information—running processes, network connections, loaded drivers, account sessions, and in-memory artifacts—into a consistent artifact set that investigators can analyze offline. The tool tries to be non-invasive while collecting sensitive data quickly and logs the collection steps to preserve chain-of-custody details and to help auditors understand potential collection side effects. ... -
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Coluna.jl
Branch-and-Price-and-Cut in Julia
Coluna is a branch-and-price-and-cut framework written in Julia. You write an original MIP that models your problem using the JuMP modeling language and our specific extension BlockDecomposition offers a syntax to specify the problem decomposition. Then, Coluna reformulates the original MIP and optimizes the reformulation using the algorithms you choose. Coluna aims to be very modular and tweakable so that you can define the behavior of your customized branch-and-price-and-cut algorithm. -
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MCP Server Playwright
MCP server for browser automation using Playwright
An MCP (Model Context Protocol) server that leverages Playwright to provide browser automation capabilities, enabling large language models (LLMs) to interact with web pages, take screenshots, and execute JavaScript within a real browser environment. -
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Corcel
Use WordPress backend with Laravel or any PHP application
A collection of Model classes that allows you to get data directly from a WordPress database. Corcel is a collection of PHP classes built on top of Eloquent ORM (from Laravel framework), that provides a fluent interface to connect and get data directly from a WordPress database. You can use WordPress as the backend (administration panel) or CMS, for inserting posts, custom types, etc, and any other PHP app in the other side querying those data (as a Model layer). ... -
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NVIDIA PhysicsNeMo
Open-source deep-learning framework for building and training
NVIDIA PhysicsNeMo is an open-source deep learning framework designed for building artificial intelligence models that incorporate physical laws and scientific knowledge into machine learning workflows. The framework focuses on the emerging field of physics-informed machine learning, where neural networks are used alongside physical equations to model complex scientific systems. PhysicsNeMo provides modular Python components that allow developers to create scalable training and inference pipelines for models that combine data-driven learning with physics-based constraints. ... -
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AgentForge
Extensible AGI Framework
AgentForge is a framework for creating and deploying AI agents that can perform autonomous decision-making and task execution. It enables developers to define agent behaviors, train models, and integrate AI-powered automation into various applications. -
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Kotest
Powerful, elegant and flexible test framework for Kotlin
Kotest is a flexible and comprehensive testing tool for Kotlin with multiplatform support. Powerful, elegant, and flexible test framework for Kotlin with additional assertions, property testing, and data-driven testing. The Kotest test framework enables tests to be laid out in a fluid way and execute them on JVM, Javascript, or native platforms. With built-in coroutine support at every level, the ability to use functions such as test lifecycle callbacks, extensive extension points, advanced conditional evaluation, powerful data-driven testing, and more. ... -
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Bloom
An open source, agentic Loom alternative
Bloom is an open-source, agent-centric video recording and processing platform designed as a modern alternative to tools like Loom, but with a strong emphasis on AI-driven workflows and programmability. It allows users to record sessions locally while simultaneously preparing those recordings to be processed, indexed, and analyzed by AI systems, effectively turning simple screen captures into structured, queryable data. The project is part of the broader VideoDB ecosystem, which focuses on... -
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Common Core Ontologies
The Common Core Ontology Repository
The Common Core Ontologies (CCO) comprise twelve ontologies that are designed to represent and integrate taxonomies of generic classes and relations across all domains of interest. CCO is a mid-level extension of Basic Formal Ontology (BFO), an upper-level ontology framework widely used to structure and integrate ontologies in the biomedical domain (Arp, et al., 2015). BFO aims to represent the most generic categories of entity and the most generic types of relations that hold between them,... -
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Alpine.js
A rugged, minimal framework for composing JavaScript behavior
Alpine.js is a lightweight JavaScript framework designed to bring declarative, reactive behavior directly into HTML markup, functioning similarly in spirit to Vue but with a much smaller footprint. It’s often described as “jQuery for the modern web,” enabling you to sprinkle interactivity onto static HTML without needing a full SPA framework. You use directives like x-data, x-bind, x-on, x-if, and others to attach reactive state, event listeners, and conditional rendering straight in the DOM. ... -
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Tulip.jl
Interior-point solver in pure Julia
Tulip is an open-source interior-point solver for linear optimization, written in pure Julia. It implements the homogeneous primal-dual interior-point algorithm with multiple centrality corrections and therefore handles unbounded and infeasible problems. Tulip’s main feature is that its algorithmic framework is disentangled from linear algebra implementations. This allows to seamless integration of specialized routines for structured problems. -
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Render
Go package for easily rendering JSON, XML, binary data, and HTML
Render is a package that provides functionality for easily rendering JSON, XML, text, binary data, and HTML templates. Render can be used with pretty much any web framework provided you can access the HTTP.ResponseWriter from your handler. The rendering functions simply wraps Go's existing functionality for marshaling and rendering data. HTML: Uses the html/template package to render HTML templates. JSON: Uses the encoding/json package to marshal data into a JSON-encoded response. ... -
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Relay
JavaScript framework for building data-driven React applications
Relay is data-fetching turned declarative. Components declare their data dependencies, without worrying about how to fetch them. Relay guarantees that the data each component needs is fetched and available. This keeps components decoupled and promotes reuse. With Relay, components and their data dependencies can be quickly modified without modifying other parts of the system. That means you won't accidentally break other components as you refactor or make changes to your app. Relay's... -
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Kedro
A Python framework for creating reproducible, maintainable code
Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems together. ... -
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FISSURE
The RF and reverse engineering framework for everyone
FISSURE is an open-source radio frequency analysis and signal intelligence framework built to support software-defined radio research, wireless security experimentation, and protocol reverse engineering. The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured... -
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Meridian
Meridian is an MMM framework
...The framework provides a robust foundation for constructing in-house MMM pipelines capable of handling both national and geo-level data, with built-in support for calibration using experimental data or prior knowledge. Meridian uses the No-U-Turn Sampler (NUTS) for Markov Chain Monte Carlo (MCMC) sampling to produce statistically rigorous results, and it includes GPU acceleration to significantly reduce computation time. -
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TimeMixer
Decomposable Multiscale Mixing for Time Series Forecasting
TimeMixer is a deep learning framework designed for advanced time series forecasting and analysis using a multiscale neural architecture. The model focuses on decomposing time series data into multiple temporal scales in order to capture both short-term seasonal patterns and long-term trends. Instead of relying on traditional recurrent or transformer-based architectures, TimeMixer is implemented as a fully multilayer perceptron–based model that performs temporal mixing across different resolutions of the data. ... -
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Douyin TikTok Download API
Douyin TikTok Download API
Use the official interface to capture Douyin|TikTok data, support API calls, Web portals, and batch analysis. Fast, asynchronous, free, open source, ad-free, long-term maintenance. This project is based on PyWebIO , FastAPI , HTTPX , a fast and asynchronous Douyin / TikTok data crawling tool, and realizes online batch parsing and downloading of watermark-free videos or atlases through the web, data crawling API, and iOS shortcut instructions for watermark-free download and other functions. ... -
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Integrant
Micro-framework for data-driven architecture
Integrant is a minimalistic micro-framework for building applications following a data-driven architecture. It lets you define system components declaratively as configuration data and handles lifecycle actions (init, halt, resume) in dependency order, serving as a modern alternative to Component or Mount. Integrant was built as a reaction to fix some perceived weaknesses with Component. -
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dd-trace
JavaScript APM Tracer
dd-trace is an npm package that you can install in your Node.js application to capture APM (Application Performance Monitoring) data. In Datadog terminology this library is called a Tracer. This data is then sent off to a process that collects and aggregates the data, called an Agent. Finally, the data is sent off to the Datadog servers where it's stored and made available for querying in a myriad of ways, such as displaying in a dashboard or triggering alerts. -
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JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling... -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
...The project introduces a new recurrent neural network design that incorporates exponential gating mechanisms and enhanced memory structures to overcome limitations of traditional LSTM models. By introducing innovations such as matrix-based memory and improved normalization techniques, xLSTM improves the ability of recurrent networks to capture long-range dependencies in sequential data. The architecture aims to provide competitive performance with transformer-based models while maintaining advantages such as linear computational scaling and efficient memory usage for long sequences. Researchers have demonstrated that xLSTM models can scale to billions of parameters and large training datasets while maintaining efficient inference speeds.
