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Google is once again redefining the boundaries of digital creativity. Its Gemini platform now lets users transform ordinary still images/photos into short, animated video clips, complete with sound. This fresh capability , revealed by David Sharon, who leads Multimodal Generation for Gemini Apps, is powered by the company’s latest video model, Veo 3 . How It Works? Breathing life into a static photo might sound like something out of a sci-fi movie, but with Gemini, the process feels intuitive and fun. Inside the Gemini interface , users can head over to the prompt area and select the “Videos” option. Once a photo is uploaded, all that’s left to do is describe what the scene should look like in motion, and optionally, suggest accompanying audio. That’s all it takes. A few inputs later, your snapshot evolves into an eight-second animated video. Whether you're reimagining a childhood drawing or adding motion to a scenic photo from a recent hike, the possibilities feel nearly limitless...

One of the highlights revealed during the  Google Cloud NEXT 2025 was the Agent Development Kit (ADK) , a new open-source framework designed to streamline the creation and deployment of intelligent, autonomous AI agents and advanced  multi-agent systems easier. As the AI landscape evolves beyond single-purpose models, building coordinated teams of agents presents new challenges, which ADK aims to solve by providing a full-stack, end-to-end development solution. The framework is the same one that powers agents within Google's own products, including Agentspace and the Google Customer Engagement Suite (CES). By open-sourcing ADK, Google intends to empower developers with powerful and flexible tools for building in the rapidly changing agentic AI space. ADK is built with flexibility in mind, supporting different models and deployment environments, and designed to make agent development feel similar to traditional software development. Core Pillars Guiding Development ADK ...

In the rapidly evolving world of artificial intelligence, even seemingly small gestures of human courtesy towards chatbots like ChatGPT come with a price tag. OpenAI CEO Sam Altman recently revealed that users saying " please " and " thank you " to the company's AI models is costing "tens of millions of dollars". While the notion of politeness having a significant financial impact on a tech giant might seem surprising, experts explain that this cost is a consequence of how these powerful AI systems operate on an immense scale. How AI Processes Language (And Politeness) Understanding the cost involves looking into the technical underpinnings of AI chatbots. Large language models (LLMs) like ChatGPT process text by breaking it down into smaller units called tokens . These tokens can be words, parts of words, or even punctuation marks. When a user inputs a prompt, the AI processes each token, requiring computational resources like processing power ...

Last month, I stumbled across an article about a new AI agent called Manus that was making waves in tech circles. Developed by Chinese startup Monica, Manus promised something different from the usual chatbots – true autonomy. Intrigued, I joined their waitlist without much expectation. Then yesterday, my inbox pinged with a surprise: I'd been granted early access to Manus, complete with 1,000 complimentary credits to explore the platform. As someone who's tested every AI tool from ChatGPT to Claude, I couldn't wait to see if Manus lived up to its ambitious claims. For context, Manus enters an increasingly crowded field of AI agents. OpenAI released Operator in January, Anthropic launched Computer Use last fall, and Google unveiled Project Mariner in December. Each promises to automate tasks across the web, but Manus claims to take autonomy further than its competitors. This post shares my unfiltered experience – what Manus is, how it works, where it shines, where it st...

As artificial intelligence continues to grow more advanced—especially with the rapid rise of Large Language Models (LLMs)—there’s been a persistent roadblock: how to connect these powerful AI models to the massive range of tools, databases, and services in the digital world without reinventing the wheel every time. Traditionally, every new integration—whether it's a link to an API, a business application, or a data repository—has required its own unique setup. These one-off, custom-built connections are not only time-consuming and expensive to develop, but also make it incredibly hard to scale up when things evolve. Imagine trying to build a bridge for every single combination of AI model and tool. That’s what developers have been facing—what many call the "N by N problem": integrating n LLMs with m tools requires n × m individual solutions. Not ideal. That’s where the Model Context Protocol (MCP) steps in. Introduced by Anthropic in late 2024, MCP is an open standa...

Google's Gemini ecosystem has expanded its capabilities with the introduction of Gemini Deep Research, a sophisticated feature designed to revolutionize how users conduct in-depth investigations online. Moving beyond the limitations of traditional search engines, Deep Research acts as a virtual research assistant , autonomously navigating the vast expanse of the internet to synthesize complex information into coherent and insightful reports. This AI-powered tool promises to significantly enhance research efficiency and provide valuable insights across diverse domains for professionals, researchers, and individuals seeking a deeper understanding of complex subjects. Unpacking Gemini Deep Research: Your Personal AI Research Partner Gemini Deep Research is integrated within the Gemini Apps, offering users a specialized feature for comprehensive and real-time research on virtually any topic. It operates as a personal AI research assistant , going beyond basic question-answering to...