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  • 1
    Agent Framework

    Agent Framework

    Framework for building, orchestrating, and deploying AI agents

    ...It also includes components such as agent sessions for managing state, context providers for maintaining memory, and middleware for intercepting and extending agent behavior. Developers can integrate external tools and services so that agents can execute actions beyond text generation.
    Downloads: 2 This Week
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  • 2
    Just the Browser

    Just the Browser

    Remove AI features, telemetry data reporting, sponsored content

    ...It includes setup scripts for Windows and Mac/Linux, and directories with manual configuration files that explain what each setting does, allowing power users to tailor the experience. Because the project leans on official group policy options, the changes persist as long as browsers support these settings, and users can easily revert them or extend them manually.
    Downloads: 2 This Week
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  • 3
    Everywhere

    Everywhere

    Context-aware desktop AI assistant that understands screen content

    ...It distinguishes itself from traditional AI tools by eliminating the need for manual input methods such as copying text or taking screenshots, instead allowing users to invoke assistance instantly through a shortcut. It can analyze on-screen information in real time and provide contextual responses, making it useful for tasks like troubleshooting errors, summarizing articles, translating text, and refining written content. It integrates with multiple large language model providers and supports various tools, enabling flexible and extensible AI-powered workflows. ...
    Downloads: 2 This Week
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  • 4
    Generative AI for Beginners (Version 3)

    Generative AI for Beginners (Version 3)

    21 Lessons, Get Started Building with Generative AI

    Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. ...
    Downloads: 2 This Week
    Last Update:
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  • Go From AI Idea to AI App Fast Icon
    Go From AI Idea to AI App Fast

    One platform to build, fine-tune, and deploy ML models. No MLOps team required.

    Access Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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  • 5
    DeepAudit

    DeepAudit

    AI multi-agent platform for automated code security auditing system

    ...It also includes automated proof-of-concept validation using a sandboxed environment, allowing detected issues to be tested for real exploitability. DeepAudit integrates retrieval-augmented generation techniques to enhance contextual understanding and reduce false positives during analysis. Users can import projects and trigger a full audit workflow that includes risk identification, exploit generation, validation, and final report creation.
    Downloads: 2 This Week
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  • 6
    Microsoft Learn MCP Server

    Microsoft Learn MCP Server

    Official Microsoft Learn MCP Server, powering LLMs and AI agents

    ...Rather than relying on training data that may be outdated or incomplete, MCP servers let agents like GitHub Copilot, Claude, or other LLM-based tools search and pull context directly from up-to-date Microsoft Learn content, including Azure, .NET, and other tech docs. By connecting to the MCP endpoint, coding agents can answer questions, retrieve code examples, and offer best practices grounded in authoritative sources without requiring API keys or manual browser searches. This capability helps eliminate hallucinations, improve accuracy, and streamline developer workflows by keeping relevant tech guidance close at hand.
    Downloads: 2 This Week
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  • 7
    windsurf.vim

    windsurf.vim

    Free, ultrafast Copilot alternative for Vim and Neovim

    ...The aim is to provide a “free, ultrafast” alternative to other AI code assistants (such as GitHub Copilot) directly within Vim/Neovim. Once installed and configured, windsurf.vim can suggest code completions, generate multi-line snippets based on comments or invitation in code, and make the editing experience more predictive and context-aware. The plugin supports major programming languages and allows you to trigger completions as you type—especially after comments or partial code constructs. Because it is designed to integrate with Vim’s editing model, it offers suggestions in-line and leverages virtual text or inline indicators when supported. ...
    Downloads: 1 This Week
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  • 8
    Claw Hunter

    Claw Hunter

    MDM-ready scripts for detecting and monitoring OpenClaw

    Claw Hunter is an open-source security tool designed to detect, analyze, and mitigate risks associated with autonomous AI agents, specifically those built on platforms like OpenClaw. As agentic AI systems gain popularity, they introduce a new class of security challenges because they can execute commands, access files, and interact with external systems with minimal human oversight. Claw-Hunter addresses this emerging threat landscape by providing visibility into these agents, helping organizations identify instances running within their environments. It focuses on uncovering “shadow AI,” which refers to unauthorized or unmanaged AI agents that operate outside traditional security controls. ...
    Downloads: 0 This Week
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  • 9
    Generative AI for Beginners .NET

    Generative AI for Beginners .NET

    Hands-on .NET course for building real-world generative AI apps

    ...Generative AI for Beginners .NET supports tools like GitHub Models, Azure OpenAI Service, and local models, giving flexibility in how projects are built and tested. Developers can run examples locally or in cloud-based environments such as GitHub Codespaces. It focuses on practical implementation rather than theory, helping users move from simple experiments to complete AI-powered solutions while understanding responsible AI usage and modern development workflows.
    Downloads: 0 This Week
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  • Train ML Models With SQL You Already Know Icon
    Train ML Models With SQL You Already Know

    BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

    Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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  • 10
    AI File Sorter

    AI File Sorter

    Local AI file organization with categorization and rename suggestions

    AI File Sorter is a cross-platform desktop application that uses AI (local LLMs run on your computer) to organize files and suggest meaningful file names based on real content, not just filenames or extensions. The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common text files. For supported audio and video files, AI File Sorter can read embedded metadata (such as ID3, Vorbis, and MP4 tags) to suggest normalized names like year_artist_album_title.ext. ...
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    Downloads: 390 This Week
    Last Update:
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  • 11
    ML.NET Samples

    ML.NET Samples

    Samples for ML.NET, an open source and cross-platform machine learning

    ...We're working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. The "best" model and the code for running it will be generated for you. The ML.NET CLI (command-line interface) is a tool you can run on any command prompt (Windows, Mac or Linux) for generating good quality ML.NET models based on training datasets you provide. In addition, it also generates sample C# code to run/score that model plus the C# code that was used to create/train it so you can research what algorithm and settings it is using.
    Downloads: 0 This Week
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