标签：#gemini

Google has announced Gemini 3.1 Pro , a new preview model positioned as a substantial upgrade to the Gemini family for complex tasks. Introduced on February 19, 2026, the model is rolling out across Google's consumer products, developer environments and enterprise platforms, making the release a platform-wide expansion rather than an isolated model update. Google's official Gemini 3.1 Pro announcement describes upgraded core intelligence intended to serve all three audiences. The most consequential part of the announcement is the model's distribution. Developers, enterprises and end users are being offered access through distinct Google products, while Google frames Gemini 3.1 Pro as a step toward more capable agentic workflows . General availability is planned after the preview phase, with broader availability described as coming soon. A broader Gemini 3.1 Pro rollout Google says Gemini 3.1 Pro is designed to be smarter at complex tasks, with improved reasoning as a central focus. The company highlighted a 77.1% score on ARC-AGI-2 , a benchmark it cited as evidence of the model's reasoning progress. That result is a company-reported benchmark figure, not a complete measure of how the model will perform in every production workflow. The release follows earlier Gemini family progress, including Gemini 3 Pro and Gemini 3 Deep Think. However, the announcement's practical message is not limited to a new model name or benchmark. Google is placing Gemini 3.1 Pro into the tools through which people build, deploy and use AI systems. Audience Announced access points Rollout status Developers Gemini API in Google AI Studio, Gemini CLI, Antigravity and Android Studio Preview Enterprises Vertex AI and Gemini Enterprise Preview Consumers Gemini app and NotebookLM Preview rollout What the multi-channel release means A model can matter strategically only if it reaches the environments where work happens. By making Gemini 3.1 Pro available through the Gemini API and development too

But will Gemini's surge survive slowing model releases?

Gemini is keeping pace with OpenAI’s ChatGPT, which hit 1 billion monthly active users back in June.

The Scenario Every Developer Recognizes It is mid-2026, and you have a feature to ship. You open ChatGPT or Claude, type something like "build me a function that parses webhook payloads and routes them to the right handler," and wait. The model returns something plausible. You paste it in, run it, and it almost works. So you prompt again: "fix the edge case where the payload is missing the event key." Another round. Then another. Forty-five minutes later, you have code that functions, but you also have a conversation thread that looks like a debugging session rather than a build session. You never actually described what you were building. You just started building it. This is the default mode for most developers using AI coding assistants in 2026, and it is expensive. According to McKinsey's State of AI in 2024 report ( source ), organizations that adopt structured design and planning approaches before implementing AI tools report higher success rates and better integration outcomes compared to those using ad-hoc implementation strategies. The pattern holds at the individual developer level too. Jumping straight into prompting skips the step that makes prompting useful: knowing precisely what you want before you ask for it. The fix is not a better model. It is a different sequence. What Design-First Actually Means in Practice Design-first means producing a written artifact that describes your system before you write a single prompt asking an AI to build it. Not a full technical document. A tight, structured description of inputs, outputs, constraints, and edge cases. Think of it as the brief you would hand to a contractor before they start work. The contractor analogy is useful because it reframes the relationship: you are not collaborating with the model in real time, you are commissioning it with a clear scope. Here is what that looks like concretely. Instead of opening Google Gemini and typing "help me build a webhook router," you spend ten minutes writing this

Assistant will disappear, leaving only Gemini for voice control in the coming weeks.

Google has broadened access to its Google AI Plus subscription in 35 new countries and territories, including the United States. The expansion strengthens Gemini's international footprint, but it does not establish that non-subscribers can use all Gemini capabilities worldwide. Free-tier access exists in some contexts, while location, feature eligibility, demand, and subscription status can still determine what users can access. The distinction matters for people evaluating Gemini as a personal productivity tool, as well as businesses considering how broadly an AI workflow can be deployed. Google's rollout is meaningful because it expands a lower-priced AI plan across more markets. Yet the available evidence points to a tiered, country-by-country model , not unconditional global access to Gemini's full feature set. What Google AI Plus expansion confirms In its official Google AI Plus availability announcement , Google said the plan became available in 35 new countries and territories. The company listed the United States among the new locations and gave a U.S. price of $7.99 per month . Google AI Plus is part of Google's paid AI-plan lineup. The announcement describes a broadening of paid-plan availability, while Google's Gemini Apps help and subscription information documents that access levels differ between free and paid users. That makes the expansion important for markets that previously had fewer Google AI subscription options, but it should not be read as a universal free Gemini rollout. Google also says its AI plans are available only in supported locations. Availability therefore remains connected to the countries and territories where Google has enabled the relevant plan and service, rather than being identical everywhere Gemini is known or marketed. Access route What the available research supports Key limitation Free Gemini access Available for certain uses and features in some contexts Feature access can vary by country, eligibility, demand, and usage l

The camera flash will evolve into a multicolor LED that illuminates when your phone is face down.

Introduction Hello from Japan! 🇯🇵 I am tosane932 , a professional truck driver working in logistics while teaching myself Python. In my previous article, I tested a Docker multi-stage build and measured the actual change in image size. At the end of that article, I said that I would write next about pytest and CI/CD. This article was supposed to be the practical follow-up. However, while preparing for that work, I encountered an unexpected side issue. I only intended to introduce Flask-Migrate. Instead, the pip installation logs revealed that the version of a library in my local development environment had been changed without me noticing. The library was: google-genai From there, I went through the following process: Identify the version mismatch Restore the version that had already been tested locally Update requirements.txt Manually verify the Gemini API functionality Run pytest to check for regressions This article records that process without hiding the inconvenient parts. https://github.com/tosane932/sales_data_app Overview While installing Flask-Migrate, I noticed a mismatch between: The version of google-genai installed in my local development environment The version declared in requirements.txt The local environment had been using: google-genai 2.10.0 However, requirements.txt still specified: google-genai==2.4.0 When I ran: pip install -r requirements.txt pip followed the configuration file and replaced the newer local version with the older declared version. This article explains how I discovered the issue, synchronized the environments, and verified the application behavior with automated tests. 1. The Problem and Its Background I was preparing to introduce Flask-Migrate. During that work, I ran: pip install -r requirements.txt The installation log contained the following lines: Attempting uninstall: google-genai Found existing installation: google-genai 2.10.0 Uninstalling google-genai-2.10.0: That message caught my attention. After checking the environ

Google is expanding Gemini on two fronts at once: faster, lower-cost models for software and enterprise workflows, and a new robotics family designed for embodied, cross-robot control. The releases include Gemini 3.6 Flash , Gemini 3.5 Flash-Lite , Gemini 3.5 Flash Cyber, and Gemini Robotics 2 with related embodied-reasoning and on-device variants. The clearest immediate enterprise story is the widening choice of models for agentic work. In its official Gemini Flash announcement , Google positions 3.6 Flash as a general workhorse for coding, knowledge work, and multimodal tasks, while 3.5 Flash-Lite is aimed at workloads where response speed and cost efficiency are decisive. The robotics update extends the same broader push beyond software agents into systems that must reason about and act in physical environments. A broader Gemini stack for agentic workloads Gemini 3.6 Flash is generally available through Google's developer, enterprise, and consumer channels. Google says it improves on 3.5 Flash for coding, knowledge-work, and multimodal tasks, while producing around 17% fewer output tokens than 3.5 Flash. That token-efficiency claim matters because output tokens are a material part of both latency and inference spending in multi-step agent workflows. Google lists pricing for Gemini 3.6 Flash at $1.50 per 1 million input tokens and $7.50 per 1 million output tokens . The company describes the model as offering a lower cost per task, a metric that depends not only on token prices but also on how many tokens a task requires to complete. The supplied release information does not provide a price for 3.5 Flash-Lite, so it should not be inferred from 3.6 Flash pricing. Gemini 3.5 Flash-Lite occupies a different role. Google calls it its fastest and most cost-effective subfamily, with a stated output speed of 350 output tokens per second . It is intended for high-throughput agentic workflows where an organization may value quick model responses and high request volume ove

Google Gemini offers an AI trip planner that combines travel research, itinerary generation and Google service integrations in one conversational workflow. The capability can surface real-time flight and hotel options, build itineraries around a traveler’s interests and adjust plans as needs change. Although Google is continuing to promote the feature, its official materials position it as an established part of the Gemini ecosystem rather than a newly launched product. The practical appeal is straightforward: trip planning often requires moving among airfare searches, hotel listings, maps, saved locations and notes. Gemini is designed to bring several of those steps together. On Google’s official Gemini AI trip planner page , the company describes prompts such as planning a four-day Tokyo visit around particular interests, then using Gemini to organize a tailored schedule by neighborhood. What Gemini’s travel planner can do Gemini’s travel functionality is framed as a consumer assistant for the research and planning stages of a trip. Users can describe a destination, trip length, interests or preferred travel style in natural language. Gemini can then help turn that input into an itinerary while drawing on relevant Google travel and mapping services. The official descriptions identify several connected capabilities: Real-time flight options through Google Flights. Real-time hotel options through Google Hotels. Customized itineraries organized around a traveler’s requested interests and locations. Plan adjustments during the trip , rather than a fixed itinerary created only before departure. Maps integration for navigation and points of interest along a route. Google’s Gemini Apps support material also confirms that the apps can help plan trips and retrieve live flight information. Maps integration matters because it extends the experience beyond trip inspiration: a user can move from deciding what to do to navigating to places and discovering points of interest whi

“What on earth is Google doing?” Misinformation fears spur walk-back of AI tool.

Google Photos has launched Video Remix , an AI-powered editing feature that applies stylized templates to users' existing video clips. Powered by Gemini Omni , the tool is designed to turn a video into a more cinematic or artistic version through a one-tap workflow inside Google Photos. The feature matters because it brings generative video styling into a consumer photo library and editing workflow rather than requiring users to begin in a dedicated video-generation product. According to Google's official Video Remix announcement , templates can add cinematic relighting, replace backgrounds, and apply artistic treatments including watercolor, raw sketchbook, and oil painting. What Google Photos Video Remix changes Video Remix is built around easy-to-use templates rather than a conventional timeline editor. A user starts with their own clip, chooses a template in the Google Photos Create workflow, and receives a stylized result. That positions the feature as a fast option for personal memories, social posts, and short marketing assets where a full editing process may be disproportionate to the desired output. Google says Video Remix is beginning to roll out to eligible Google AI Plus, Pro, and Ultra subscribers in select countries. The supplied materials identify the subscription tiers, but do not provide feature-specific pricing or a complete country-by-country availability list. Access may therefore differ by market and rollout stage. Area Google Photos Video Remix Broader Gemini Omni context Primary workflow Applies templates to a user's existing video clips in Google Photos Supports wider video generation and editing workflows Documented examples Cinematic relighting, background changes, watercolor, raw sketchbook, and oil painting Style-driven video transformations, including claymation-style demonstrations reported by third parties Access described in supplied research Rolling out to eligible AI Plus, Pro, and Ultra subscribers in select countries Google docume

Google has expanded Gemini Omni into Google Vids for end-to-end AI video generation and editing. The update lets users create clips from text and image references, then make targeted changes to existing footage through a step-by-step conversation. Rather than rebuilding a video after each revision, users can describe an adjustment, supply additional media where useful and refine the result in place. The central development is Omni's use of multimodal and real-world understanding in a Vids workflow. According to Google DeepMind's Gemini Omni overview , the model can work from arbitrary media, including images, text, video and audio, and apply reference-to-video capabilities grounded in world knowledge and physics-like reasoning. In Google Vids, that foundation is intended to make generated and edited scenes more coherent in composition, context and visual behavior. For teams that already use Vids to communicate ideas, training material or internal updates, the change moves AI assistance beyond first-draft generation. It introduces a conversational editing layer that can alter a chosen part of a video while preserving the broader scene and workflow. What Gemini Omni changes in Google Vids Gemini Omni supports both video creation and revision. A creator can begin with a prompt or image reference to generate a clip, or bring in existing footage and specify what should change. Google describes examples such as changing color grading or lighting, replacing backgrounds and removing background elements. This distinction matters because prompt-to-video and video editing have different practical constraints. Generating a new clip can be useful when no footage exists. Editing existing material is more relevant when a team wants to retain an established subject, scene or message while changing selected details. Omni's reference handling is designed to connect those modes rather than treating each request as an isolated output. Workflow How Gemini Omni is used in Vids Supported

Gemini Robotics 2 includes three models, but only one is publicly available right now.

The latest version of Google DeepMind's AI model includes a significant jump into “physical AGI.” But plopping AI into the real world comes with risks.

Analysis of 15 million real AI interactions finds most tasks at most jobs are unaffected.

This tutorial builds a starter "Hello World" style agent using Kotlin and the native Kotlin version of the Agent Development Kit (ADK). The full sample project is available on GitHub: xbill9 / adk-hello-world-kotlin Kotlin ADK and MCP Hello World This project is a runnable Kotlin Agent Development Kit (ADK) demo. A Kotlin LlmAgent uses Gemini to decide when to call a greet tool discovered from a local Kotlin Model Context Protocol (MCP) server. The project has two Gradle modules: agent : the Kotlin ADK agent, Gemini model configuration, MCP toolset, and interactive ReplRunner ; server : the Ktor MCP server that exposes greet . Technology Stack Kotlin: 2.3.0 Kotlin ADK SDK: com.google.adk:google-adk-kotlin-core (v0.6.0) MCP Kotlin SDK: io.modelcontextprotocol:kotlin-sdk-jvm (v0.8.1) Ktor Framework: 3.0.0 (Netty, SSE, ContentNegotiation, CORS) JDK: Java 25 Build System: Gradle 9.2.1 (Kotlin DSL) Prerequisites Java 25 A Gemini Developer API key The Gradle wrapper is included. Configure Gemini Create the local environment file: cp .env.example .env Set GOOGLE_API_KEY in .env , then load it: source ./set_env.sh The file is ignored by Git. Run the Demo Start the Kotlin MCP server in one… View on GitHub What Is Kotlin? Kotlin is a modern, statically typed programming language created by JetBrains. It runs on the Java Virtual Machine (JVM), works alongside existing Java libraries, and is widely used for Android, backend, and multiplatform development. Static typing is especially useful when building agents. Agent configuration, tool schemas, and tool results can all be checked by the compiler before a prompt reaches the model. Installing Java This sample uses Java 25 . If Java is not installed, SDKMAN! is a convenient way to install and switch between JDK versions on Linux and macOS: Home | SDKMAN! the Software Development Kit Manager SDKMAN! is a tool for managing parallel versions of multiple Software Development Kits on most Unix based systems. sdkman.io After installing

TL;DR: nb2lite-skill-agy wraps Google's gemini-3.1-flash-lite-image model (NB2Lite) in a FastMCP server and packages it as an Antigravity CLI skill. You type "generate an image of a cyberpunk kitchen" into Antigravity, and it just... does it. Then you say "add a neon RAMEN sign" and it edits the same image without re-prompting the whole scene. Oh, and the cover image of this article? Generated by the thing the article is about — dogfooding all the way down. More on that at the end. Background: why another image tool? Most image-generation workflows are stateless . You send a prompt, you get pixels back, and the model immediately forgets everything. Want to tweak the result? You re-describe the entire scene and pray the character, lighting, and composition survive the round trip. (Narrator: they don't.) Google's NB2Lite — the friendly nickname for gemini-3.1-flash-lite-image — takes a different approach. It's a high-efficiency image model with sub-2-second generations, solid text rendering in 25+ languages, and — the headline feature — support for the stateful Interactions API , which lets you iterate on an image across multiple turns while the model keeps the visual context server-side. This repo glues that capability directly into Google Antigravity CLI , so your coding agent can generate and iteratively refine images as a natural part of a pair-programming session. It ships as two things in one repo: A Model Context Protocol (MCP) server ( nb2lite-agent , a single-file FastMCP app in server.py ) exposing four tools. A Skill definition ( nb2lite-image ) that teaches Antigravity when and how to use those tools well. The Interactions API: images with a memory The Interactions API is Gemini's stateful endpoint. The core loop looks like this: You call client.interactions.create(...) with a prompt and store=True . The response includes an interaction_id — a handle to the turn's visual context, persisted on Google's servers. On the next call, you pass previous_interactio

TL;DR: omni-skill-agy wraps Google's gemini-omni-flash-preview model (Omni Flash) in a tiny FastMCP server and packages it as an Antigravity CLI skill. You type "generate a video of a fox running through snow" into Antigravity, and it just... does it. Then you say "make it nighttime with snowfall" and it edits the same video without re-prompting the whole scene. It can also animate a still image, interpolate between two keyframes, restyle a video you already have — and when you're happy, upload the result to YouTube. Without leaving your terminal. Background: why another video tool? Most video-generation workflows are stateless . You send a prompt, you get frames back, and the model immediately forgets everything. Want to tweak the result? You re-describe the entire scene and pray the character, lighting, and camera work survive the round trip. (Narrator: they don't.) Google's Omni Flash — gemini-omni-flash-preview — takes a different approach. It's the video-generation model in Google's Gemini "Omni" line: built for fast, high-fidelity clips, and — the headline feature — wired into the stateful Interactions API , which lets you iterate on a video across multiple turns while the model keeps the visual context server-side. What Omni Flash actually does The "Omni" part isn't branding fluff — the model accepts genuinely mixed multimodal input. A single request's input can be a plain string, or a list of typed parts: text parts, base64-encoded image parts, and document parts pointing at a video you've uploaded via the Gemini File API. The model composes whatever you hand it into one clip. That single mechanism covers five distinct ways to make a video: Text → video. A prompt in, an .mp4 out — landscape 16:9 or portrait 9:16 , chosen at generation time. One image + a motion prompt → animation. A still comes to life ("the group smiles and waves at the camera"). Two images + a transition prompt → keyframe interpolation. The model invents the in-between footage from frame A

Gemini had over 750 million monthly users in February.