Search Results for "domain"
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Showing 312 open source projects for "domain"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Transfer Learning Repo
Transfer learning / domain adaptation / domain generalization
Transfer Learning Repo is an open-source repository that compiles resources, code implementations, and academic references related to transfer learning and its related research areas. The project functions as a large knowledge hub that organizes papers, tutorials, datasets, and software implementations across topics such as domain adaptation, domain generalization, multi-task learning, and few-shot learning. The repository includes surveys and theoretical explanations that help readers understand how transfer learning methods allow models trained in one domain to adapt to new tasks or datasets. In addition to academic references, the project provides practical code implementations of many transfer learning algorithms so that researchers can reproduce experiments or build their own applications. ... -
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WhisperJAV
Uses Qwen3-ASR, local LLM, Whisper, TEN-VAD
...WhisperJAV introduces a specialized pipeline that separates text generation from timestamp alignment, allowing the system to generate transcripts and then align them with audio using forced alignment techniques. The framework supports several speech recognition models, including Qwen-based ASR systems and fine-tuned Whisper models trained on domain-specific dialogue. -
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FinGLM
Committed to building an open, public welfare
FinGLM is an open-source financial large language model initiative aimed at advancing artificial intelligence applications within the finance industry. The project focuses on developing domain-specific language models that understand financial terminology, corporate reports, and economic datasets. By combining large language model architectures with financial datasets such as corporate annual reports and structured financial records, FinGLM aims to improve AI performance on tasks that require domain expertise. The repository also provides educational materials and tutorials that help developers learn how to build and fine-tune financial AI systems using the GLM model ecosystem. ... -
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Perplexica
Perplexica is an AI-powered answering engine.
...Perplexica offers multiple search modes—Speed, Balanced, and Quality—so you can trade off latency and depth depending on the task. It also enhances the experience with widgets and rich search types like images, videos, and domain-limited queries. With local search history, file uploads, and an easy Docker-based setup, it’s built for everyday research without sacrificing control or privacy. -
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PentestGPT
Automated Penetration Testing Agentic Framework Powered by LLMs
PentestGPT is an AI-powered autonomous penetration testing agent designed to perform intelligent, end-to-end security assessments using large language models. Published at USENIX Security 2024, it combines advanced reasoning with an agentic workflow to automate tasks traditionally handled by human pentesters. The platform supports multiple penetration testing categories, including web security, cryptography, reversing, forensics, privilege escalation, and binary exploitation. PentestGPT runs... -
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AI-Crawler
Crawl a website starting from a URL, find relevant pages
...Unlike traditional web scrapers that rely on static selectors and manual scripting, it uses AI to dynamically identify and prioritize pages based on user intent, making it more flexible and resilient to changes in website structure. Users can define their data requirements in plain English, and the system will interpret those instructions to crawl a domain and extract structured data. The tool supports output formats such as JSON and Markdown, and it can generate or accept schemas to ensure that extracted data is structured according to application needs. It is designed as a low-code solution, reducing the complexity of building and maintaining custom scraping pipelines. -
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Huatuo-Llama-Med-Chinese
Instruction-tuning LLM with Chinese Medical Knowledge
...The goal of the project is to improve the reliability and domain expertise of language models when answering medical questions or assisting with healthcare-related tasks. By combining domain-specific training data with instruction-tuning techniques, the project produces models capable of generating more accurate medical responses than general-purpose models. -
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Hugging Face Skills
Definitions for AI/ML tasks like dataset creation
...The project is designed to be interoperable across major agent ecosystems, including Claude Code, OpenAI Codex, Gemini CLI, and Cursor, making it a cross-platform building block for agent automation. By formalizing best practices and workflows, Skills helps transform general-purpose coding agents into domain-aware assistants that can execute complex ML pipelines with less manual prompting. The repository also includes ready-to-use skills for common Hugging Face operations and encourages teams to extend them with custom domain logic. -
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Giskard
Collaborative & Open-Source Quality Assurance for all AI models
...Giskard automatically generates relevant tests based on the vulnerabilities detected by the scan. You can easily customize the tests depending on your use case by defining domain-specific data slicers and transformers as fixtures of your test suites. -
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Learn Claude Code
Bash is all you need, write a claude code with only 16 line code
...It emphasizes a hands-on learning path where each version (from v0 to v4) adds conceptual building blocks like the core agent loop, todo planning, task decomposition, and domain knowledge skills, illuminating the patterns behind what makes a true AI agent tick. The goal is to demystify agent architectures like Claude Code by having learners build simplified versions themselves and observe how tools, memory management, planning constraints, and context isolation contribute to reliable agent behavior. Along the way, the project teaches fundamentals such as how to let models call external tools, maintain clean memory for long tasks, and inject domain expertise without retraining the model. -
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Browser Harness
Self-healing browser harness that enables LLMs to complete any task
...The implementation is intentionally compact, with a small set of core files handling installation, day-to-day usage, helper methods, and the daemon layer that maintains the CDP websocket bridge. The repository also includes domain and interaction skills, suggesting that it is meant to be used as part of a broader agentic workflow rather than only as a low-level developer tool. -
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freenom
Freenom domain name renews automatically
Freenom domain name automatic renewal. -
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OpenClaw Medical Skills
The largest open-source medical AI skills library for OpenClaw
OpenClaw-Medical-Skills is an open-source library that provides a large collection of specialized medical capabilities designed for the OpenClaw AI agent ecosystem. The project organizes domain-specific “skills” that enable autonomous agents to perform tasks related to biomedical research, healthcare analysis, and clinical data interpretation. Each skill is packaged as a modular component that can be integrated into an OpenClaw-based AI assistant, allowing the agent to perform expert-level reasoning and workflows in medical contexts. ... -
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Harness
A meta-skill that designs domain-specific agent teams
Harness is a meta-skill framework designed to automate the creation and orchestration of domain-specific AI agent teams within Claude Code environments. Instead of manually defining agents and their behaviors, it generates specialized agents, assigns them roles, and produces the skills they need to execute tasks effectively. The system focuses on scaling agent-based workflows by dynamically assembling teams tailored to specific problems. -
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LLM Datasets
Curated list of datasets and tools for post-training
...The repository aims to make datasets easy to inspect and transform, with scripts for downloading, deduping, cleaning, and converting to formats like JSONL that slot into training pipelines. It highlights instruction-tuning and conversation-style corpora while also pointing to code, math, or domain-specific sets for targeted capabilities. Quality is a recurring theme: examples and utilities help filter low-value samples, enforce length limits, and split train/validation consistently so results are comparable. Licensing and provenance are surfaced to encourage compliant usage and to guide dataset selection in commercial settings. ... -
16
Aerosolve
A machine learning package built for humans
...Built around sparse, human-intuitive features (like geography, pricing), it supports feature quantization, interaction specification, and rule-based priors—enabling domain experts to contribute directly to model behavior. -
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Microsoft Agent Skills
Skills, MCP servers, Custom Agents, Agents.md for SDKs
Microsoft Agent Skills is an actively maintained repository of skills, custom agents, templates, and MCP configuration files designed to extend AI coding assistants with deep knowledge about Azure SDKs and Microsoft AI Foundry services. The project bundles over a hundred domain-specific skills that teach AI agents how to perform tasks like Azure resource provisioning, SDK usage patterns, infrastructure setup, and common DevOps workflows, bridging the gap between agent reasoning and real-world Microsoft platform needs. In addition to the skills themselves, the repo includes templates for agent configuration (e.g., Agents.md), marketplace setup files, and command utilities to install skills into directories like .github/skills or .claude/skills. ... -
18
MedgeClaw
Open-source AI research assistant for biomedicine
MedgeClaw is a specialized AI-powered research assistant tailored for biomedical and scientific workflows, built on top of OpenClaw and Claude Code architectures. It integrates a large library of domain-specific skills, enabling it to perform complex analyses in areas such as genomics, drug discovery, and clinical research. The system connects conversational interfaces with computational environments, allowing users to initiate research tasks through messaging platforms while the backend executes analyses using tools like R and Python. ... -
19
audioFlux
A library for audio and music analysis, feature extraction
...Can be used for deep learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. audioflux is a deep learning tool library for audio and music analysis, feature extraction. It supports dozens of time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature combinations. It can be provided to deep learning networks for training and is used to study various tasks in the audio field such as Classification, Separation, Music Information Retrieval(MIR) ASR, etc. -
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GaiaNet
Install and run your own AI agent service
Gaia is building an active, intelligent ecosystem that supports applications that learn, improve and grow over time. Put your knowledge to work and watch it evolve by creating a node on Gaia or by contributing to a domain supporting an existing knowledge base. Gaia’s decentralized platform ensures robust protection for user data and IP. Gaia allows secure ownership and monetization of IP without compromising privacy. Gaia’s living knowledge organisms continuously adapt and grow in real-time, keeping solutions relevant and cutting-edge. Developers can build applications that evolve and improve over time. -
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Kimi K2.5
Moonshot's most powerful AI model
Kimi K2.5 is Moonshot AI’s open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed vision and text tokens. Based on a 1T-parameter Mixture-of-Experts (MoE) architecture with 32B activated parameters, it integrates advanced language reasoning with strong visual understanding. K2.5 supports both “Thinking” and “Instant” modes, enabling either deep step-by-step reasoning or low-latency responses depending on the task. Designed for... -
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Antigravity Kit
AI Agent templates with Skills, Agents, and Workflows
Antigravity Kit is an AI agent capability expansion toolkit that provides structured templates, skills, and workflows designed to boost the productivity of AI assistants and autonomous agents in coding environments. It comes with an extensive library of predefined agent personas and domain-specific skills that help in performing targeted tasks such as frontend development, backend engineering, quality assurance, and more. With this kit, developers can rapidly initialize a project scaffold and gain access to specialist agents and command workflows that guide the agent through multi-step tasks and slash-command procedures. ... -
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TorchRec
Pytorch domain library for recommendation systems
TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. -
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AutoAgent AI
Autonomous harness engineering
...It runs inside Docker for safe execution and reproducibility. This approach shifts agent development from manual design to automated optimization. The system is particularly useful for building domain-specific agents that need continuous performance improvement. -
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Machine Learning and Data Science Apps
A curated list of applied machine learning and data science notebooks
...The repository organizes resources by industry categories such as finance, healthcare, agriculture, manufacturing, government, and retail, allowing practitioners to explore domain-specific machine learning use cases. Most examples are written in Python and frequently use Jupyter notebooks to present practical implementations and experiments. The project encourages contributions from data scientists and domain experts who want to share applied analytics projects and techniques that address real business challenges.
