#scala
X's Recommendation Algorithm uses machine learning to show you posts and content you are most likely to engage with across its platform, including the "For You" timeline and notifications. It gathers a large pool of posts from people you follow and others you might like, then ranks them by predicting your interest based on your past actions like likes, clicks, and replies. It also filters out unwanted content and mixes in sponsored posts to keep your feed relevant and diverse. This means your feed is personalized to show you the most interesting and safe content, improving your experience on X.
https://github.com/twitter/the-algorithm
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
#Easy#Credit#T#i#ch#nh#s
Join the Easy Credit - Tài chính số beta on ✈️#TestFlight
🔗 Link: https://testflight.apple.com/join/B8XYxOWV
Shared by Dimitri
#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