Retrieval-Augmented Generation (RAG) has revolutionized how we build intelligent applications that can access and reason over external knowledge bases. In this comprehensive tutorial, we’ll explore how to build production-ready RAG applications using Ollama and Python, leveraging the latest techniques and best practices for 2025. What is RAG and Why Use Ollama? Retrieval-Augmented Generation combines the […]

A technical exploration of autonomous AI systems that move beyond content generation to real-world execution

Top Picks for Best Ollama Models 2025 A comprehensive technical analysis of the most powerful local language models available through Ollama, including benchmarks, implementation guides, and optimization strategies Introduction to Ollama’s 2025 Ecosystem The landscape of local language model deployment has dramatically evolved in 2025, with Ollama establishing itself as the de facto standard for […]

Ollama vs Docker Model Runner: Key Differences Explained In recent months, the LLM deployment landscape has been evolving rapidly, with users experiencing frustration with some existing solutions. A Reddit thread titled “How to move on from Ollama?” highlights growing discontent with Ollama’s performance and reliability issues. As Docker enters this space with Model Runner, it’s […]

Hold onto your hats, folks, because the world of Artificial Intelligence has just been given a significant shake-up. Meta has unveiled their latest marvels: the Llama 4 herd, marking what they’re calling “the beginning of a new era of natively multimodal AI innovation”. This isn’t just another incremental update; it’s a leap forward that promises […]

Revolutionizing AI Automation: Unleashing the Power of CrewAI In this blog today, let us discover how CrewAI – a fast, flexible, and standalone multi-agent automation framework – is transforming the way developers build intelligent, autonomous AI agents for any scenario. What is CrewAI? CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely independent […]

TensorRT-LLM is essentially a specialized tool that makes large language models (like ChatGPT) run much faster on NVIDIA hardware. Think of it this way: If a regular language model is like a car engine that can get you from point A to point B, TensorRT-LLM is like a high-performance tuning kit that makes that same […]

Over the past year, Microsoft developments with AutoGen have underscored the remarkable capabilities of agentic AI and multi-agent systems. Microsoft is thrilled to unveil AutoGen v0.4 , a major update shaped by invaluable feedback from our vibrant community of users and developers. This release marks a comprehensive overhaul of the AutoGen library, designed to elevate […]

Have you ever wanted to deploy a large language model (LLM) that doesn’t just work well but also works lightning-fast? Meet vLLM—a low-latency inference engine built to handle LLMs like a pro. Now, pair that with the versatility and scalability of Docker, and you’ve got yourself a dynamic duo that’s changing the way we think […]

Large Language Models (LLMs) have emerged as a groundbreaking force in artificial intelligence, demonstrating remarkable capabilities in understanding and generating human-like text. However, their true potential shines brightest when applied to specific challenges in vertical industries, as Garry Tan mentioned in a recent interview. This article explores the power and limitations of LLMs, focusing on […] The post Large Language Models in Vertical Industries: Revolutionizing Medical Documentatio

Retrieval Augmented Generation also known as (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. In the the rapidly evolving landscape of AI and natural language processing (NLP), RAG Techniques have emerged as a […]