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AI-assisted coding is on the rise, with tools like Copilot, Cursor, and Windsurf enabling a more intuitive, fast-paced approach known as “vibe coding.” Instead of carefully planning each line, developers now prompt, accept, and tweak code in real time—often feeling more like they’re jamming than programming. While this method can accelerate prototyping and spark creativity, it comes with risks: without foundational coding skills, it's easy to introduce silent errors. Sonam shares personal wins and frustrations with vibe coding and reminds us that while AI is a powerful partner, human judgment is still essential.

Learn why stuffing prompts with excessive context actually hurts AI performance. This guide shows how irrelevant information confuses LLMs, the 'lost in the middle' effect, and proven techniques for cleaning and optimizing prompts to improve accuracy, reduce costs, and boost reliability in production AI systems.

Anthropic's Model Context Protocol (MCP) is revolutionizing AI integration by providing a standardized way for LLMs to connect with external systems. Learn the core concepts, architecture, and build a practical Hacker News server that extends Claude's capabilities with real-time data access.

The slow, batch-processing nature of the data lake is obsolete for modern Generative AI, which requires instant access to fresh data. In this article, the author proposes a shift away from centralizing data, advocating instead for an API-first approach. This allows AI applications to directly and quickly access live data from its source, enabling truly real-time, responsive features.

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.

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.

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.