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Showing 120 open source projects for "case"
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Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Prompt Master
A Claude skill that writes the accurate prompts for any AI tool
...It provides a structured way to store and reuse prompts, making it easier to maintain consistency and improve performance across different applications. The project emphasizes clarity and organization, allowing users to categorize prompts by use case, domain, or functionality. It also supports experimentation, enabling users to refine prompts and compare results to achieve better outputs. The repository can be used as both a learning resource and a practical toolkit for developers working with AI systems. It reflects the growing importance of prompt engineering as a discipline in modern AI development. -
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System Design Academy
If you want to become good at AI & system design, join this newsletter
System Design Academy is a curated learning repository for system architecture, distributed systems, AI engineering, and technical interviews. It organizes a large collection of articles and case studies into searchable alphabetical sections. Real-world examples examine how major services scale storage, messaging, streaming, payments, collaboration, and other workloads. Fundamental topics include APIs, caching, consistency, databases, DNS, Docker, Kafka, Kubernetes, load balancing, microservices, and service discovery. Separate guides cover interview frameworks and design exercises for products such as ChatGPT, Spotify, WhatsApp, and YouTube. ... -
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AI Infra
Understanding of AI Infra: Quantitative Analysis and System Design
...It approaches inference and training through quantitative analysis of hardware limits, data movement, model architecture, and distributed systems. The book contains twelve chapters supported by formulas, diagrams, experiments, and case studies. Companion tools help readers reproduce resource calculations and inspect the assumptions behind system-design decisions. Additional material covers accelerators, networking, KV caches, inference serving, training systems, mixture-of-experts models, and performance engineering. The source is written in Markdown and automatically built into a XeLaTeX PDF and a searchable website. ... -
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WooYun Legacy
wooyun-legacy skill for claude code
...The repository organizes information into methodology references, technical knowledge, vulnerability categories, examples, and evaluation data. A lightweight installation provides condensed indexes, while the full repository exposes the complete case collection for deeper research. It is intended for authorized testing, risk analysis, code review, and defensive security work. The maintainers have stated that the project is no longer actively maintained. -
One Monitoring Tool for IT, OT and Cloud | Free TrialModern infrastructure spans data centers, cloud platforms and factory floors, and every blind spot between them is a risk. PRTG supports SNMP, WMI, SSH and other standard protocols to monitor IT, OT and hybrid environments through one customizable dashboard. Build the views your team needs, from network health to application performance, without switching tools. Try PRTG free for 30 days now.
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Flower
Flower: A Friendly Federated Learning Framework
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems. Different machine learning frameworks have different strengths. ... -
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Rhino
On-device Speech-to-Intent engine powered by deep learning
...It directly infers intent from spoken commands within a given context of interest, in real-time. The end-to-end platform for embedding private voice AI into any software in a few lines of code. Design with no limits on top of a modular platform. Create use-case-specific voice AI models in seconds. Develop voice features with a few lines of code using intuitive and cross-platform SDKs. Deliver voice AI everywhere: on-device, mobile, web browsers, on-premise, or cloud. Measure adoption, learn, and iterate. Continuously re-design and re-train to optimize engagement. Building accurate, responsive, and private voice technology is difficult. ... -
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UltraRAG
Less Code, Lower Barrier, Faster Deployment
...The toolkit comes with built-in support for popular RAG datasets, large corpora, and canonical baselines, plus documentation that walks from “quick start” to debugging and case analysis. It encourages pipeline composition via configuration, enabling researchers to swap retrievers, rerankers, and generators without heavy refactoring. Community posts highlight its focus on reducing engineering overhead so more effort goes to experimental design. Backed by the OpenBMB org, it is actively maintained with tutorials and updates. -
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MING
A large-scale model of medical consultation in Chinese
MING is an open-source medical large language model designed for intelligent medical consultation and question answering in Chinese. The project focuses on building a healthcare-focused conversational system capable of responding to medical questions, analyzing case descriptions, and guiding diagnostic reasoning. It is trained using medical instruction tuning so that the model can understand patient symptoms and respond with structured explanations and clinical suggestions. One of its primary goals is to simulate a multi-round medical consultation process, allowing the system to ask follow-up questions before offering diagnostic recommendations. ... -
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Telegram SMS
An SMS-forwarding Robot Running on Your Android Device
...Receiving and sending SMS, relaying APP notifications, monitoring battery status. All stuff can be done with a single Telegram bot. You can use the bot in both private chat and group chat, in case you have more than 2 Android phones, or sharing the bot with other people. Telegram SMS connects with Telegram's bot API server directly, no 3rd-party services involved. -
Demo Series - Small Business Backup By VeeamWatch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
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AG2
Framework for building and orchestrating multi-agent AI systems
...AG2 is intended for developers experimenting with autonomous systems, research prototypes, or production-grade agent pipelines. AG2 emphasizes flexibility, allowing users to integrate different models and customize behaviors depending on their use case. Overall, it serves as a foundation for building scalable and modular AI agent ecosystems. -
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SenseVoice
Multilingual speech recognition and audio understanding model
...It is built to achieve high transcription accuracy while maintaining efficient inference performance. It includes different model variants optimized for either speed or accuracy, allowing developers to choose a configuration suitable for their use case. In addition to speech transcription, SenseVoice can detect emotional cues in speech and identify common sound events such as applause, laughter, or coughing. It also provides tools for running inference, exporting models to formats like ONNX or LibTorch, and deploying the system through APIs. -
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Guidance
A guidance language for controlling large language models
Guidance is an efficient programming paradigm for steering language models. With Guidance, you can control how output is structured and get high-quality output for your use case—while reducing latency and cost vs. conventional prompting or fine-tuning. It allows users to constrain generation (e.g. with regex and CFGs) as well as to interleave control (conditionals, loops, tool use) and generation seamlessly. -
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Pixelle-Video
AI Fully Automated Short Video Engine
...It is built to support experimentation with generative video models, making it useful for research and creative applications. The system emphasizes modularity, allowing developers to plug in different models or processing steps depending on the use case. It can be used for tasks such as content generation, video editing, or visual storytelling. Overall, Pixelle-Video provides a flexible environment for building AI-powered video generation and processing workflows. -
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Awesome LLM Apps
Collection of awesome LLM apps with AI Agents and RAG using OpenAI
Awesome LLM Apps is a community-curated directory of interesting, practical, and innovative applications built on or around large language models, serving as a discovery hub for developers, researchers, and enthusiasts. The list spans a wide range of categories including productivity tools, creative assistants, utilities, education platforms, research frameworks, and niche vertical apps, showcasing how generative models are being used across domains. Each entry includes a brief description,... -
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Nano Bananary
One-click generation of various gameplay, no prompt words required
Nano Bananary is an open-source image and video creation interface built around Gemini image-generation workflows. It provides one-click creative presets so users can generate results without writing prompts manually. Users can upload images, select a use case, generate content, or paint a local region for targeted editing. Each result can become the input for another generation, enabling continuous iterative workflows. The app also includes history, enlarged previews, side-by-side and slider comparisons, custom prompts, a Chinese interface, a light theme, and video generation. It can run through AI Studio or locally with Node.js and a configured Gemini API key. -
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LlamaParse
Parse files for optimal RAG
LlamaParse is a GenAI-native document parser that can parse complex document data for any downstream LLM use case (RAG, agents). Load in 160+ data sources and data formats, from unstructured, and semi-structured, to structured data (API's, PDFs, documents, SQL, etc.) Store and index your data for different use cases. Integrate with 40+ vector stores, document stores, graph stores, and SQL db providers. -
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Generative AI
Sample code and notebooks for Generative AI on Google Cloud
Generative AI is a comprehensive collection of code samples, notebooks, and demo applications designed to help developers build generative-AI workflows on the Vertex AI platform. It spans multiple modalities—text, image, audio, search (RAG/grounding) and more—showing how to integrate foundation models like the Gemini family into cloud projects. The README emphasises getting started with prompts, datasets, environments and sample apps, making it ideal for both experimentation and... -
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Cactus Needle
26m function call model that runs on incredibly small devices
...The project provides open weights, training details, dataset generation resources, and a playground for testing the model with custom tools. Needle is optimized for single-shot function calling rather than broad conversational ability, so its core use case is selecting the right tool and producing structured arguments. It can be fine-tuned locally, including on consumer machines, which makes it useful for experimentation with small personalized agents. The project is best suited for researchers and developers exploring tiny AI models, edge inference, and lightweight tool-calling systems. -
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Claude Code Router
Use Claude Code as the foundation for coding infrastructure
...The project is useful for developers who want to keep the Claude Code interface while experimenting with different models, cost profiles, and specialized request handling. It supports project-level configuration, making it possible to adapt behavior depending on the codebase or use case. It also includes an extensible agent-oriented architecture for custom tools and workflows, including support for image-related tasks. Overall, it gives technical users more control over Claude Code infrastructure without abandoning the familiar coding assistant workflow. -
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Antigravity Kit
AI Agent templates with Skills, Agents, and Workflows
...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. The repository follows a clear pattern for defining agents and their behaviors, making it easier to extend with new skills or tailor existing ones to your use case. Because it’s framework-agnostic at its core, the Antigravity Kit can integrate with IDEs and other development tooling for smoother AI-augmented coding. -
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Shapash
Explainability and Interpretability to Develop Reliable ML models
Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria. -
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Live Agent Studio
Open source AI Agents hosted on the oTTomator Live Agent Studio
Live Agent Studio is a curated repository of open-source AI agents associated with the oTTomator Live Agent Studio platform, showcasing a variety of agent implementations that illustrate how autonomous and semi-autonomous tools can be constructed using modern AI frameworks. Each agent in the collection is designed for a specific use case — such as content summarization, task automation, travel planning, or RAG workflows — and is provided with the code or configuration needed to explore and extend it on your own, making the repository both a learning resource and a practical starting point for real projects. The repository is community focused, with sample agents like tweet generators, smart selectors, research assistants, and multi-tool workflows that show how agents can integrate with tools like n8n or custom Python code. ... -
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apfel
Apple Intelligence from the command line
...The project appears to follow a philosophy of reducing unnecessary complexity while still enabling practical functionality for developers who prefer lean systems over heavy frameworks. It is designed to be adaptable, allowing developers to extend or modify its behavior depending on their specific use case. Apfel may include utilities or structural patterns that streamline development workflows, particularly in environments where speed and clarity are more important than feature richness. Its architecture likely avoids over-engineering, making it suitable for small projects, prototypes, or educational purposes. The project encourages direct interaction with code rather than relying on extensive abstraction layers, giving developers more control over implementation details. -
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Amical
Open Source AI Dictation App
Amical is an open source, AI-powered desktop dictation and note-taking application that enables users to dictate hands-free, transcribe meetings, and capture notes effortlessly with unmatched speed, accuracy, and privacy. It leverages both local and cloud-based AI models, letting users seamlessly switch between providers for the ideal balance of speed, precision, and control, and understands the context of each app in use to automatically format text in a tone and style appropriate to the... -
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NVIDIA Model Optimizer
A unified library of SOTA model optimization techniques
...By providing standardized workflows and APIs, it enables developers to experiment with different optimization strategies and select the best approach for their use case.
