“Computer vision, data labeling, robotics — AI is all of these things. Staying ahead in AI means keeping up with multiple technologies: • It’s no longer enough to just label data — you need to work with synthetic data. • Fine-tuning models isn’t enough — you need to use RAG. https://x.com/TheTuringPost/status/1899631747828174852
CohereForAI/c4ai-command-a-03-2025 · Hugging Face https://huggingface.co/CohereForAI/c4ai-command-a-03-2025
“📄✨ Useful PDF-to-Markdown comparison tool: Compare 8+ parsing methods side-by-side, visualize results instantly, and download as ZIP. Test it yourself https://x.com/fdaudens/status/1898815305448665335
“📄 File search—retrieve precise information from large document collections, with built-in query optimization and custom reranking. https://x.com/OpenAIDevs/status/1899531586950795662
“Build an agentic corrective RAG, step-by-step: – It can search through your docs – and fallback to web search if needed https://x.com/akshay_pachaar/status/1897853624187076758
“Personal AI Agent Mind-Map This is a Multi-agent. Tech Stack: – Python’s (Langchain and PydanticAI) – Supabase as DB for RAG and Memory I am building this for my Sem-end-Project. https://x.com/PratikKadam_/status/1896565808937189630
“Today @cohere is very excited to introduce Command A, our new model succeeding Command R+. Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding usecases. 🧵 https://x.com/aidangomez/status/1900169306987524440
Introducing Command A: Max performance, minimal compute https://cohere.com/blog/command-a
“Cohere has launched Command A, a 111B parameter dense model that represents a huge leap from their previous Command R/R+ models Key results: ➤ Artificial Analysis Intelligence Index of 40, only just behind OpenAI’s latest version of GPT-4o ➤ Cohere’s first party API for https://x.com/ArtificialAnlys/status/1900606602501341518
“Build a multilingual, multimodal RAG system with @llama_index and @qdrant_engine 🌐🖼️ Learn how to create a powerful Retrieval-Augmented Generation system that handles multiple languages and modalities: 🔍 Ingest and retrieve content in English, Spanish, Chinese, and more 🖼️ https://x.com/llama_index/status/1899147105035579701
“Our friends at @huggingface wrote a whole educational course on building agents with us! Covers: ➡️ The components of LlamaIndex ➡️ Doing RAG ➡️ Using tools ➡️ Creating Agents ➡️ Building Workflows Check it out! https://x.com/llama_index/status/1897030370144739403
How to Boost Your RAG Accuracy – Unstructured https://unstructured.io/webinars/boost-rag-accuracy-contextual-chunking
