标签：#gemini

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.

TL;DR: nb2lite-skill-kiro wraps Google's gemini-3.1-flash-lite-image model (NB2Lite) in a tiny FastMCP server and packages it as a Kiro skill. You type "generate an image of a cyberpunk kitchen" into Kiro, 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 into Kiro , so your coding agent can generate and iteratively refine images as a natural part of a 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 exactly four tools. A Kiro skill ( nb2lite-image ) that teaches Kiro 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_interaction_id , and the model edits the existing canvas — preserving ch

Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about its AI strategy.

There are new 3.6 and 3.5 models today, but Google is already training Gemini 4.

After five years of shipping features at Tittri, the team gave me a piece of feedback that stung a little: I was good, but I only ever built what I was asked to build. And we delivered, end to end, every week. From dashboards to multi-step workflows to third party integrations, all of it to make dense workflows understandable. Somewhere along the way I'd become all about business critical web apps, where the frontend is not just visual, it's how people execute operations reliably. But it just wasn't enough for me. So in January 2026 I stopped waiting to be asked, and pitched an AI integration into a UK-based fintech client's product, one that handles real business loans. When I finally presented the idea to the team and the client, after enough brainstorming and planning and identifying the best way for the brokers to benefit from it, expectations rose. No prior AI integration experience, no matching tutorials, real users, real data, real money. Then I had to actually build it. What is the client and why AI The client is one of the UK's pre-eminent business finance brokers and comparison services that combines advanced technology with a team of finance experts to help small and medium-sized enterprises (SMEs) and their advisors find, compare, and apply for the most appropriate and affordable funding options from across the entire market. The client's B2B product is a commercial finance brokerage SaaS, where brokers manage deals, calls, emails, documents, lenders, AIPs (Agreement In Principle), credit/KYC, corporate structures, properties...etc. The brokers use the SaaS to run the whole lifecycle of a deal, and before AI every single step of it was manual. A typical deal goes like this: An enquiry comes in by call or email, something like "my client needs £400k to buy out a GP partner". The broker gets on a discovery call that can run 30–90 minutes, writes up the notes afterwards, creates the deal, and then starts gathering everything a lender will want to see. Finan

This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . What I Built Preparing for technical interviews can be overwhelming. I wanted to build a tool that doesn't just give generic questions, but actually analyzes my specific resume to challenge my unique skill set. This led me to build a multi-agent system using the ADK. This multi-agent system, will take user resume and extracts their profile for generating Interview Questions specialized to the candidate profile. The agents communicate in a sequential loop: The Profiler extracts data -> The Interviewer generates questions -> The Judge validates them. If the Judge rejects a question, the Interviewer re-drafts it, ensuring only high-quality, resume-relevant questions make it to the user. Cloud Run Embed Your Agents Profiler Receives a resume PDF GCS path, downloads it in-memory, parses the text content, and builds a summary of skills. Interviewer Reads the candidate summary and drafts 3 technical interview questions designed to test the boundaries of their experience. Analyzes the drafted questions. Passes the iteration if they are resume-specific; rejects/fails them if they are too generic. Key Learnings This project was a fantastic weekend challenge. Working through the Google Codelab gave me a solid grasp of agent-based architectures, specifically implementing Agent, LoopAgent, and SequentialAgent to create a robust workflow. A few key technical takeaways included: Managing Statelessness : Learning to handle agent sessions in a Cloud Run environment was a great lesson in explicit session lifecycle management. Cloud Integration : Integrating Google Cloud Storage for file handling taught me how to bridge in-memory document processing with persistent cloud storage efficiently. Deployment Architecture : Mastering the transition from local development to a containerized, production-ready Cloud Run deployment provided deep insights into modern backend orchestration. Check the Code from

Google said users can soon access their notebooks through AI Mode in Search.

Hachette, Cengage, Elsevier, and other publishers allege that Google trained its AI on copyrighted works without the necessary permissions.

This is a submission for Weekend Challenge: Passion Edition What I Built I built Passion...

In the world of project management and team collaboration, the holy grail is reducing friction. Previously, we looked at how to make Trello cards talk back The Power of Gemini inside Trello and how to bring Gemini into your workspace Gemini in your Slack workspace . But what if you wanted a highly intelligent, stateful team assistant living directly in Slack that could answer complex, open-ended questions about your Trello boards? Questions like: "Which cards did I edit last week?" "Show me all comments made across my active boards in the last 7 days." "What is the current status of the card XYZ?" Answering these questions requires more than simple semantic search; it requires a tool that can dynamically write retrieval scripts, parse complex multi-board JSON payloads, filter dates, and compile elegant reports. In this article, we'll explore how to build exactly that using Google’s Antigravity Managed Agent (the "Agy" agent) , integrated into Slack's native Agent View , utilizing a secure, stateful, and sandboxed remote execution environment. 🚀 Prerequisites Before starting, make sure you have: A Google AI API Key with Gemini / Antigravity access A Trello Account (with an API Key and Token for read-only board queries) A Slack Workspace (with App Admin privileges to create a Socket Mode app) Node.js (v24+) and pnpm (v11+) installed 🔒 Security & Environment Controls: The Agy Sandbox Philosophy At the heart of this setup is Google's Antigravity (Agy) Managed Agent . Instead of running in a transient stateless environment, the Agy agent operates inside a persistent, secure, and remote Linux sandbox equipped with standard execution engines (Python, Node.js, bash, etc.). When a Slack user asks a question, the Agy agent dynamically writes a script, runs it in its isolated sandbox, inspects the Trello API output, self-corrects if any errors occur, and presents a formatted response. In this article, we focus on Trello, but the same principles can be applied to any other syst

Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace?

Google added two new models to the Gemini API today: Nano Banana 2 Lite (image generation) and Gemini Omni Flash (video generation + editing). Neither is the Gemini 3.5 Pro release people have been waiting for, so it's easy to miss. Here's what's actually in them. TL;DR Nano Banana 2 Lite: gemini-3.1-flash-lite-image = text-to-image in ~4s, $0.034/1K images Gemini Omni Flash: gemini-omni-flash-preview = video gen + conversational editing, $0.10/sec Both are built to be chained: generate an image fast, then animate it into video Neither model is positioned as a quality upgrade = both are cost/speed plays Nano Banana 2 Lite Model ID: gemini-3.1-flash-lite-image Text-to-image output in about 4 seconds $0.034 per 1K-resolution image Positioned as the direct replacement for the original Nano Banana ( gemini-2.5-flash-image ) - if you're on that model, this is a drop-in upgrade Available in Google AI Studio, Gemini API, Gemini Enterprise Agent Platform, and consumer surfaces (Search AI Mode, Gemini app, Photos, NotebookLM, Flow, Google Ads) Gemini Omni Flash Model ID: gemini-omni-flash-preview Public preview in Google AI Studio and the Gemini API Conversational editing - refine a generated video using plain-language instructions instead of re-prompting from zero Multimodal referencing - combine text, image, and video inputs to keep a scene consistent $0.10 per second of video output (same rate as Veo 3.1 Fast) Known limitations right now Generations capped at 10 seconds No audio reference uploads yet No scene extension yet Video references under 3 seconds are accepted by the API schema but not correctly processed yet Character consistency across scene changes/pans still has rough edges Google says longer durations are coming. The part worth paying attention to: chaining them Generate an image with Nano Banana 2 Lite (fast, cheap) Pass that image as a reference into Omni Flash Omni Flash animates it into a video Both models are optimized for throughput and cost, not for to

In his keynote on Wednesday, Benoit Schillings, vice president of Technology at Google DeepMind and...

Building an AI agent is fun. Fixing its production latency when it's juggling live data, RAG, and text-to-speech? Not so fun. In the latest episode of the AI Agent Clinic, we sat down with developer Sami Maghnaoui to debug PlaybackIQ, a football / soccer agent he built to provide pre and post match analysis with text to voice, and minute-by-minute match insights with interactive UI. The app was awesome, but under heavy "match day" data loads, the wait times were killing the UX. Here’s how we fixed it: The Bottleneck: We implemented OpenTelemetry on the Agent Platform to trace exactly where the LLM calls and data retrieval were hanging up. The Scale: We shifted the deployment to Cloud Run to properly handle concurrent traffic. The Result: We managed to slash the agent's latency by 80%. If you're dealing with sluggish LLM response times in your own apps and want to see what a production-grade fix looks like, we recorded the whole teardown and rebuild. 🎥 Watch the teardown here: [ https://youtu.be/G7olcqETSn8 ] (Let me know in the comments what your go-to stack is for tracing LLM latency!)

Google's 24/7 agentic assistant, Gemini Spark, comes to Mac alongside other improvements, like real-time tracking and support for more apps.

Google is updating its image generator to make it faster and cheaper, making it a more useful tool for creators looking to make AI content.

Yesterday, we kicked off our physical newspaper, The Daily Context, at the AI Engineer World’s Fair...

Google is expanding Gemini’s personalized AI image generation to eligible free users in the U.S., allowing the chatbot to create images based on your interests and data from connected Google apps.