
Building a Screen-Aware AI with ScreenEnv and Tesseract
Learn how to build screen-aware AI using ScreenEnv and Tesseract for dynamic, real-time screen content
Learn how to build screen-aware AI using ScreenEnv and Tesseract for dynamic, real-time screen content
CXOs must lead talent transformation to build Agentic AI-ready teams through upskilling, mentoring, and applied
As AI systems become more autonomous, organizations face new governance and compliance challenges. This article
The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.
LLM caching in LangChain addresses deployment challenges by storing and reusing generated responses.
LightRAG simplifies and streamlines the development of retriever-agent-generator pipelines for LLM applications.
Discover the power of llama-agents: a comprehensive framework for creating, iterating, and deploying efficient multi-agent
RAVEN enhances vision-language models using multitask retrieval-augmented learning for efficient, sustainable AI.
NuMind’s NuExtract model for zero-shot or fine-tuned structured data extraction.
Deep Lake: an advanced lakehouse for efficient AI data storage and retrieval, perfect for RAG
Explore Microsoft’s Florence-2: Unifying vision and language tasks with prompt-based AI integration.
Compare and contrast between different vector databases and understand their utilities.
Discover Microsoft’s AutoGen Studio for easy multi-agent system development and deployment.
Discover Nvidia’s Nemotron-4 340B models, revolutionising synthetic data generation and LLM training challenges.
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