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Source channel @githubtrending · Post #14895 · Jul 2

#python#copilot#csharp#dotnet#github#github_copilot#github_copilot_chat#github_copilot_for_azure#github_copilot_free#github_copilot_training#javascript#lab#labs#microsoft#python#sql#tutorial#tutorial_code#tutorial_exercises#visual_studio_code#vscode GitHub Copilot’s new Agent Mode is a powerful AI coding partner that goes beyond just suggesting code—it can independently write, debug, and improve your code, handle complex workflows, and even fix its own mistakes automatically. It works with multiple programming languages and integrates with popular development tools, helping you save time on repetitive tasks like testing, deployment, and refactoring. By using natural language prompts, you can guide it to complete multi-step projects, making coding faster and easier whether you’re a beginner or an expert. This course teaches you how to fully use these features, boosting your productivity and coding skills. https://github.com/microsoft/Mastering-GitHub-Copilot-for-Paired-Programming

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GitHub Trends

@githubtrending · Post #15265 · 11/03/2025, 12:00 PM

#python#ai#llm#rag#reasoning#retrieval PageIndex is an advanced AI tool that helps you find the most relevant information in long professional documents by thinking and reasoning like a human expert, rather than just matching keywords. It organizes documents into a clear tree structure, similar to a table of contents, and searches through this structure to give precise, trustworthy answers with exact page references. This method avoids the common problems of traditional vector-based search, making it ideal for complex reports, legal texts, or financial filings. You can use it easily via cloud services or run it locally, improving your ability to analyze and understand large documents quickly and accurately. https://github.com/VectifyAI/PageIndex

Machinelearning

@ai_machinelearning_big_data · Post #8801 · 10/17/2025, 10:13 AM

⚡️ Omni-Embed-Nemotron - новая единая модель от NVIDIA для поиска по тексту, изображениям, аудио и видео Модель обучена на разнообразных мультимодальных данных и может объединять разные типы входных сигналов в общее векторное представление. - Поддержка всех типов данных: текст, изображение, аудио, видео. - Основана на архитектуре Qwen Omni (Thinker-модуль, без генерации текста). - Контекст - до 32 768 токенов, размер embedding — 2048. - Оптимизирована под GPU, поддерживает FlashAttention 2. Это делает её идеальной для: - кросс-модального поиска (поиск текста по видео или изображению); - улучшения RAG-проектов; - систем мультимодального понимания контента. Просто, быстро и эффективно - всё в одном открытом решении. 🌐 Открытая модель: https://huggingface.co/nvidia/omni-embed-nemotron-3b @ai_machinelearning_big_data #crossmodal#retrieval#openAI#NVIDIA#OmniEmbed#multimodal#AIModels#OpenSource#Search#UnifiedEmbedding