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Source channel @githubtrending · Post #15180 · Sep 29

#other You can use a set of markdown files to guide AI coding assistants step-by-step in building software features. This method breaks down your feature idea into a clear Product Requirement Document (PRD), then into detailed tasks, and finally lets the AI work on each task one at a time while you review and approve progress. This structured workflow helps you keep control, avoid errors, and track progress visually, making AI-assisted development more reliable and manageable. It works with many AI tools and improves the quality and clarity of AI-generated code, saving you time and reducing frustration during complex feature development. https://github.com/snarktank/ai-dev-tasks

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​​Совсем лайтовая статья для новичков "10 главных конструкций языка R". Содержание: - Комментарии - Переменные и векторы - Внешние модули - Ввод и вывод - Присваивание и сравнение - Условный оператор if - Цикл for - Функции - Классы, методы и объекты #статьи #easy

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@githubtrending · Post #15433 · 01/23/2026, 02:30 PM

#python#deepseek#demo#easy#embedding#flask#gpt#huggingface_transformers#llm#mcp#multimodal#openai#qwen#rag#sentence_transformers#ui#vllm#vlm UltraRAG is a lightweight framework that makes building retrieval-augmented generation (RAG) systems simple and fast. It uses a low-code approach where you write just dozens of lines of YAML configuration instead of complex code to create sophisticated AI workflows with conditional logic and loops. The framework includes a visual development environment where you can drag-and-drop to build pipelines, adjust parameters in real-time, and instantly convert your logic into interactive chat applications. This means you can deploy powerful AI systems that ground answers in your own data—reducing hallucinations and improving accuracy—without needing extensive coding expertise or lengthy development cycles. https://github.com/OpenBMB/UltraRAG