标签：#gemini

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.

We (at DEV and MLH) are covering AI Engineer's World Fair by printing a physical newspaper called...

Dealing with vendor invoices usually means someone is sitting there manually opening PDFs and typing...

Building an HSK Speaking Test AI: Real-time Tone Grading with Gemini I built a free Mandarin speaking assessment tool that grades tone + grammar in real time. Here's the engineering behind it. The Problem HSK (Chinese proficiency test) has a speaking component (HSKK), but most learners can't self-assess their level. Online tutors are expensive. Generic AI conversation tools don't grade tones. So I built ToneTutor: a 3-minute spoken-HSK test that estimates your speaking level and identifies weak points. The Tech Stack Frontend: Web Audio API (record user voice → PCM → LINEAR16) React + TypeScript (real-time transcript display) Backend: FastAPI (Python) on Google Cloud Run Gemini 2.5 Flash (real-time conversation + transcript grading) Firestore (user sessions + results) The Challenge: Web Audio API records as WebM. Gemini expects LINEAR16 (WAV). iOS Safari doesn't support WebM. So: Transcode WebM → PCM in browser (Web Audio context) Send raw PCM bytes to backend Backend wraps PCM in WAV header → sends to Gemini Speech-to-Text Gemini analyzes transcript + provides HSK level estimate The Grading Loop python async def grade_session(transcript: str): prompt = """ Rate this Mandarin response on HSK 1-6 scale. Assess: tone accuracy, grammar, vocabulary range. Provide: level estimate + weak points. """ response = await gemini.generate_content(prompt, stream=True) return parse_hsk_level(response) Results - 3-min test - Real-time feedback - Shareable HSK score card - Free (limited sessions) Open source coming soon. Built because I'm a native speaker + voice actor frustrated with generic tools. Try it: tonetutor.tefusiang.com (free for 3 sessions) Curious about the speech-to-text pipeline or tone grading logic? Ask below.

This post is my submission for DEV Education Track: Build Apps with Google AI Studio . What I Built For this project, I built DevBrand AI, an AI-powered web application that helps developers create a complete personal branding kit in just a few clicks. Instead of manually writing bios, portfolio headlines, README introductions, or designing graphics, users simply provide their GitHub username, role, tech stack, experience, and preferred design theme. The application then generates everything automatically. Prompt Used I used Google AI Studio's Build apps with Gemini feature with a prompt similar to this: Build a modern React + TypeScript application called DevBrand AI that generates a complete developer branding kit. Use Gemini to generate professional bios, portfolio headlines, GitHub README introductions, project ideas, mission statements, social media introductions, CTAs, and branding recommendations. Use Imagen to generate a modern 3D developer mascot, hero illustration, and portfolio banner. Create a responsive UI using Tailwind CSS with reusable React components, loading animations, copy buttons, and download functionality. Features 🤖 AI-generated developer bio 🎯 Personal tagline 💻 Portfolio headline 📄 GitHub README introduction 💡 Project ideas 🌈 Suggested branding colors 📢 Social media introduction 🚀 Portfolio call-to-action 🎨 AI-generated developer mascot 🖼️ Hero illustration 🌐 Portfolio banner 📋 Copy buttons 📥 Download generated content 📱 Responsive modern interface Demo Screenshots Live Demo App: https://devbrand-ai-706459620449.asia-southeast1.run.app My Experience This project was my first time using the new Build apps with Gemini experience in Google AI Studio, and it was surprisingly fast to go from an idea to a working application. What impressed me most was how the AI generated a well-structured React + TypeScript project instead of just producing a single file. The generated components, services, and overall architecture made the project easy to und

This post is my submission for DEV Education Track: Build Apps with Google AI Studio . What I Built I set out to build the same app as the one mentioned in the Tutorial. Please create an app that generates a unique new Magic the Gathering card, using Imagen for the visuals, and Gemini to create the text descriptions and stats for the card. Apply the "Sophisticated Dark" design theme to the app. Spammed Fix Errors Non-Stop After this other than the Manual Entry option. Demo My Experience You can't trust Gemini Flash even for the Task provided in the Tutorial Standalone at least and well I spammed Fix Errors and they removed the Auto-Fixing of Errors because of idk an infinite loop or something but well the Error Fixing Experience was quite Meh considering I haven't delved into Vue and React in that level yet so I just 'Vibe Coded' and I found out with this experience that Vibe-Coding is UnCool. I think I would do the other course after properly understanding concepts behind it unlike the way I jumped in this One.

I had a long Google AI Studio (Gemini) session that I wanted to keep. I selected the conversation in the browser, copied it, and pasted it into a text file. File size: a few hundred KB. "OK, that's safe." Later, I exported the same session as JSON. File size: a few MB. More than half of the data had silently disappeared. What was missing I checked what the manual copy had dropped. The system prompt The instruction I had originally given the model — the system prompt — was completely gone. Manual copy captures only the user/assistant turns visible in the conversation pane. The instruction context that shaped the entire session does not get copied. The tail of long responses When a Gemini response is long, the browser shows a "Show more" button. If you copy without expanding it, the response gets cut mid-sentence. Out of 8 sessions I checked, 3 had responses truncated this way. Newlines inside code blocks Newlines inside code blocks got mangled in several places. Responses containing JSON or YAML had indentation that no longer parsed. The reasoning trace For some models, the model's reasoning trace is stored separately from the visible response. Manual copy doesn't capture it at all. How to export as JSON Google AI Studio has a session export feature. In the session view, click the ... menu at the top right Select "Export" Choose JSON format and download The JSON contains the full data, including the system prompt. Measured: manual copy vs. JSON export I compared 8 sessions. Session Manual copy JSON Loss rate A tens of KB ~150 KB ~70% B ~90 KB ~200 KB 50-60% C ~30 KB ~100 KB 60-70% D ~50 KB ~180 KB ~70% E ~60 KB ~240 KB ~70% F ~20 KB ~70 KB ~60% G ~20 KB ~50 KB 50-60% H ~10 KB ~30 KB ~60% Total a few hundred KB ~1 MB 60-70% Average loss rate, 60-70%. The manual copy was, on every session, missing most of what was in the actual session state. Why I didn't notice If you open the manually-copied file, the conversation reads fine. As long as the start and end connect, a m

46.4%. That number — ChatGPT's June 2026 market share — ends a streak that held since November 2022. For the first time since the product launched, OpenAI holds less than half the AI assistant market. Gemini is at 27.7%. Claude is at 10.3%. The monopoly phase of AI assistants is over. The data comes from a June 2026 market report tracking monthly active users across major AI assistants. ChatGPT still leads with 1.11 billion monthly users — a number that would define the entire category in any other software market. But Gemini has 662 million, up 129 million in five months. Claude sits at 245 million, nearly four times its December 2025 count of 60.2 million. The trajectory is the story, not the absolute numbers. Why the 50% Threshold Actually Matters Below 50% doesn't mean decline. ChatGPT's absolute user count keeps growing. What the threshold signals is the end of single-platform dominance — the condition where building for "AI users" meant building for ChatGPT users. That assumption no longer holds in mid-2026. For context: search engine market share stayed above 90% for Google for nearly a decade after competitors entered. Social network market share for Facebook stayed above 70% for years after Instagram and Twitter had genuine scale. The pace of AI assistant fragmentation is meaningfully faster than those precedents. Three products above 10% share in under two years of real competition is an unusually fast split. What fragmentation means practically: the community knowledge base — YouTube tutorials, Reddit threads, prompt libraries — that once pointed almost exclusively at ChatGPT now covers three platforms with genuine depth. That changes how you can expect your users to arrive at your AI-integrated product, and what they already know about AI when they get there. Gemini's 662 Million Users Are Not What They Look Like Gemini's surge from under 500 million to 662 million monthly users in five months is impressive on paper. The driver is less impressive: Google

Origin: Halfway through a chat, where is that meme? Every heavy chat user has a bunch of memes saved on their phone and computer, but when they actually need one—halfway through a conversation, wanting to send a "Thanks, let's keep in touch" or "I'm just bad"—they can never find it. The filename is IMG_4821.jpg , albums are not categorized, and searching is impossible. I first came across a great open-source project ShiQu1218/MemeTalk , which uses Python + Streamlit + SQLite to build a local meme semantic search system. It scans your local meme folder, creates an index using OCR and vector embeddings, and then performs multi-way retrieval. It's fully functional but research-oriented and requires running Streamlit in a browser. What I wanted was something closer to an "everyday handy tool": A native Mac App, a search box, where I type what I'm looking for, relevant memes pop up, and a single click copies it directly to the clipboard. Thus MemeFinder was born. This article documents its development process from scratch to "menu bar resident + global hotkey

June 24, 2026. That is the shutdown date for every Imagen model on Firebase AI Logic — imagen-3.0-generate-002 , imagen-4.0-generate-001 , imagen-4.0-ultra-generate-001 , imagen-4.0-fast-generate-001 . All of them. If you have been putting off this migration, you have run out of runway. The replacement is Google's Gemini Image models — internally called "Nano Banana," publicly named gemini-2.5-flash-image . The migration is mostly a one-function rename and a response structure update, around 90 minutes of work for most codebases. The catch: one Imagen capability, mask-based editing, has no replacement at all. That separate deadline hits June 30. What Goes Dark and When Firebase AI Logic's migration documentation confirms these shutdown dates: imagen-3.0-generate-002 — June 24, 2026 imagen-4.0-generate-001 — June 24, 2026 imagen-4.0-ultra-generate-001 — June 24, 2026 imagen-4.0-fast-generate-001 — June 24, 2026 imagen-3.0-capability-001 (mask editing: inpainting, outpainting, object removal) — June 30, 2026 Vertex AI runs on a slightly different clock — Google recommends migrating before June 30, with a hard shutdown date of August 17 for Vertex AI users on legacy Imagen endpoints. Firebase AI Logic is the shorter deadline. Don't assume extra time if your app uses the Firebase SDK. The Core Migration: Python Three things change simultaneously: the method name, the model identifier, and the response structure. All three break if you miss any one of them. Before (Imagen): import google.generativeai as genai client = genai . Client ( api_key = " YOUR_KEY " ) response = client . models . generate_images ( model = " imagen-4.0-generate-001 " , prompt = " A red fox running through snow " , config = { " number_of_images " : 1 , " output_mime_type " : " image/jpeg " } ) image_bytes = response . generated_images [ 0 ]. image . image_bytes After (Gemini Image): import google.generativeai as genai client = genai . Client ( api_key = " YOUR_KEY " ) response = client . models . g