This article provides a quick guide for building and deploying a Retrieval Augmented Generation (RAG) application in minutes, perfect for hackathon environments. It details how to leverage Google Cloud's Vertex AI Studio and Vertex AI Search to create a grounded Large Language Model (LLM) application that can answer questions based on a specific knowledge base, such as company documentation. The process involves preparing data in Google Cloud Storage, creating a searchable data store with Vertex AI Search, crafting and grounding a prompt in Vertex AI Studio, and then deploying the AI assistant as a web app using Cloud Run. The guide emphasizes speed and automation, allowing users to focus on data and user experience rather than complex technical setups.

Demetrios and Mohan Atreya break down the GPU madness behind AI — from supply headaches and sky-high prices to the rise of nimble GPU clouds trying to outsmart the giants. They cover power-hungry hardware, failed experiments, and how new cloud models are shaking things up with smarter provisioning, tokenized access, and a whole lotta hustle. It's a wild ride through the guts of AI infrastructure — fun, fast, and full of sparks!

As AI systems grow more multimodal and context-aware, traditional vector stores fall short. Graph-based vector databases offer a way to model relationships, context, and connections, making them an increasingly practical choice for modern AI applications.

Demetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Machine Control Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful.

The world of MLOps is very complex as there is an endless amount of tools serving its purpose, and it is very hard to get your head around it. Instead of combining various tools and managing them, it may make sense to opt for a platform instead. Databricks is a leading platform for MLOps. In this discussion, I will explain why it is the case, and walk you through Databricks MLOps features.

At a packed Microsoft Reactor event in SF, CrewAI, LlamaIndex, and Lambda laid out how agents are quietly becoming core enterprise infrastructure. From scaling headaches to state management, cost tradeoffs to hallucination risks, this wasn’t a hype fest - it was a hands-on look at what it really takes to get agents into production.

This blog introduces GenV (Generative AI for Video Analytics), a practical Python-based agent designed to extract actionable insights from Google Meet recordings using multimodal AI. Built with tools like Google Colab, Google Cloud Storage, and Vertex AI's Gemini models, GenV automates the tedious process of summarizing meetings, identifying action items, and capturing key decisions. The workflow—Locate → Prepare → Analyze → Report—leverages structured Pydantic schemas to ensure consistent and useful outputs such as summaries, project discussions, and technical insights. The result is a powerful demonstration of how focused, agentic AI can streamline knowledge retrieval and improve meeting productivity, especially for professionals in AI and MLOps.

Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.