标签：#gemini

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

Most carbon footprint apps do the same thing: Quiz → "Your footprint is 120 kg CO₂/week" → Generic tips → User never returns. That's not a coaching experience. That's a guilt trip with no follow-through. For PromptWars Virtual — Challenge 3 (Carbon Footprint Awareness & Reduction), I built CarbonCompass with a different premise: Not just measure. Guide. Live demo: https://prompt-wars-virtual-hackathon-8u1kxxwh1-mithunvisveshs-projects.vercel.app/ The Problem with Existing Carbon Tools I started by looking at what already exists — Capture, Klima, JouleBug. Each of them calculates a footprint accurately. But they all fail at the same step: the recommendation layer. "Install solar panels." "Buy an EV." "Go vegan." These are structurally correct but useless for a hostel student in Chennai who travels by bus and eats at the mess. They're recommendations designed for a demographic that already has money and flexibility. CarbonCompass is built around two real Indian users: Aditi — a college student in Chennai. Bus commute, hostel mess food, shared room electricity. Her biggest carbon lever is food waste, not transport. Rohan — a tech professional in Bengaluru. Petrol car + scooter commute, air-conditioned 2BHK, frequent food delivery. His biggest lever is home energy, not diet. The same app, two users with different lifestyles receive coaching tailored to their highest-impact opportunities. That's the core product promise. The Architectural Decision That Made Everything Work Before writing a single line of code, I ran this prompt in Google Antigravity's Plan Mode: You are a senior product architect. Before coding: Generate user personas Design a SINGLE shared calculation module that the Dashboard, Impact Simulator, and AI Coach all call with the same inputs Create the data schema Propose page architecture Flag risks for a one-week build Do not write code yet. Create an Implementation Plan. The agent produced a full Implementation Plan artifact — a structured document I cou

This is a submission for the June Solstice Game Jam TL;DR Save the Sun is a kids' deduction game set on the eve of the June solstice: you race Sköll—the wolf who wants to eat the sun—to Sól's one true rune before he catches her and the longest day never dawns. Gemini does two jobs and the engine referees both: it reads the player's questions—typed, or spoken aloud and transcribed—as the Oracle, and it plays the wolf as Sköll. The engine owns the secret and never hands it to Gemini. Everything here is checkable: play a round · watch the demo · anchildress1/save-the-sun . What I Built Blame a board game 📞 The idea started with Dream Phone , a 90s deduction game I played as a kid—you dial pretend phone numbers and narrow down which boy has a secret crush on you. The catch: it needed 2-4 players and fell flat with two. So I rebuilt it as a two-player game à la Guess Who and gave the second seat to Sköll, an AI opponent to race. That became Save the Sun , a deduction race for players aged 8 to 12 against Sköll, the Norse wolf who wants to eat the sun. The story of Sól and Sköll comes straight out of Norse mythology and is one of my all-time favorites. Sól drives the sun-chariot across the sky, and Sköll chases her—every day, all day, forever—until Ragnarök, when he finally catches her and the sun goes out. The game drops you into the night before the solstice with the wolf a stride behind: get the true offering to Sól before he reaches her, or the dawn never comes. Teaching AI to lose 🧩 The hard part of a kids' deduction game is making the AI beatable without handing it the answer. The opponent never sees the secret: a deterministic engine holds it and referees every move, and Gemini only ever plays on top. Sköll's side was easy—he answers in structured JSON—but a loose human question has to be read into something the engine can resolve first, and that reading is the only job I gave the Oracle. Twenty-four runes, one short night 🌙 The round itself is small on purpose. Th

Gemini 3.5 Pro goes general-availability in late June 2026 with a 2-million-token context window and a Deep Think reasoning mode that positions it against the most capable frontier models currently live — at a moment when the field is unusually thin. Claude Fable 5 was disabled globally on June 12 under a U.S. export control directive. GPT-5.6 remains a release candidate in Codex backend logs under the codename kindle-alpha . As of June 19, 2026, Gemini 3.5 Pro is the next major frontier model with a confirmed launch window, and it’s already live for select enterprise customers on Vertex AI. This is what’s confirmed, what’s still unknown, and what developers should do before GA drops. The Timing Isn’t an Accident Google announced Gemini 3.5 Pro at I/O on May 19 with a June general-availability target. At the time, that framing put it in direct competition with Claude Fable 5 (released June 9 before the shutdown) and the anticipated GPT-5.6. That competitive calculus shifted on June 12 when Anthropic disabled Fable 5 for all customers worldwide following an export control order. Claude Opus 4.8 is still live — it hits 88.6% on SWE-Bench and is a legitimate coding workhorse — but its 200K context ceiling blocks the entire category of codebase-scale and multi-document workloads that Fable 5 had been handling at 200K. The gap Gemini 3.5 Pro steps into isn’t hypothetical. Teams that built agent pipelines around Fable 5’s coding accuracy have been on Opus 4.8 stopgaps or migrating to GPT-5.5 since June 12. Neither alternative offers 2M context. Neither has a Deep Think mode native to the same model. Gemini 3.5 Pro is arriving into the most favorable competitive opening Google has had at the frontier in 18 months. The 2M Token Context: Where the Ceiling Disappears Gemini 3.5 Flash shipped with a 1M-token context window, doubling Gemini 3.1 Pro’s 500K limit. Pro doubles Flash again. At 2 million tokens, a single API call can hold: A 2,000-file TypeScript monorepo at 200 lin

As announced in this blog post on June 18, 2026, Gemini CLI and Gemini Code Assist IDE extensions...

Google's demo reel for Gemini Omni looks effortless: ask for a video, then keep talking to it until the shot is right. The question for developers is whether that conversational loop holds up outside a stage demo — and what it actually changes versus the Veo workflow it replaces. What Does Omni Add That Veo Couldn't? Omni's core addition is state. Veo produced one-shot renders — each prompt generated a fresh clip with no memory of the last. Gemini Omni holds context across turns, so changing the camera angle on turn three preserves the characters and lighting established on turn one without restarting the scene . Announced at Google I/O on May 19, 2026, the first shipped model, Gemini Omni Flash, replaces Veo as the video-generation surface in the Gemini app . Product director Nicole Brichtova framed it as "the next step towards combining the intelligence of Gemini with the rendering capabilities of our media models" — DeepMind's informal pitch is a "Nano Banana for video," extending conversational image editing to motion footage. Two claims deserve a skeptical read. Google advertises "intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics," but those physics behaviors currently rest on Google demos and creator footage, with no third-party benchmarks published at launch . And on raw output, independent reviewers put Omni's generation quality on par with Veo 3.1 rather than clearly above it . The differentiation is the iterative editing loop and Gemini-grounded reasoning — not a new render engine. Before Starting: Paid Membership, Region, Age Omni access is gated behind a paid Google AI plan and a few hard eligibility rules, so confirm these before you open a prompt. Gemini Omni Flash unlocks in the Gemini app and Google Flow for Google AI Plus, Pro, and Ultra subscribers, with Plus starting at $7.99/month . If you want to test it for free, generation is available at no cost on YouTube Shorts and the YouTube Create App at launch . Two cons

Most AI tools give Australian small businesses American advice. An Aussie tradie running Xero does not need to hear about QuickBooks. A cafe owner with three casual staff has Fair Work Act obligations that no generic "automate your business" tool will surface. I built AppZ AU Business Advisor to fix this -- a free tool powered by Gemini 2.0 Flash that generates personalised automation blueprints with real Australian business context. This post covers the technical decisions, the prompt engineering approach, and why the AU-specific scaffold makes all the difference. The Problem with Generic AI Business Advice When you ask a general AI "how should I automate my business?", the training data skews heavily American. You get advice about QuickBooks, not Xero. About W-9 forms, not BAS lodgement. About 401k, not superannuation. For an Australian sole trader approaching the $75k GST registration threshold, this is not just unhelpful -- it is actively misleading. The compliance obligations are different. The software ecosystem is different. The pain points are different. The Prompt Scaffold Approach Instead of injecting "you are talking to an Australian business" as a keyword, I built a reasoning scaffold -- a structured context block the model uses as a knowledge foundation: AUSTRALIAN BUSINESS CONTEXT: - GST: 10%, mandatory registration at $75k annual turnover - BAS: lodged quarterly (or monthly for large businesses) to the ATO - Superannuation: 11.5% employer contribution, paid per payroll from July 2026 - ATO tools: STP Phase 2 mandatory for all employers - Dominant accounting platforms: Xero, MYOB, Reckon (not QuickBooks) - Fair Work Act: award rates, leave entitlements, payslip requirements - Key software by vertical: ServiceM8 (trades), Deputy (hospitality), Cliniko (health) This is not a keyword list -- it is a reasoning foundation. When a tradesperson mentions "invoicing problems", the model now reasons about Xero integrations, GST-inclusive invoicing, and BAS categ

Google's new smart speaker is more about Gemini than audio quality.

Pentagon also claims 1.5 million personnel are using generative AI tools.

The chatbot still remains the most popular AI assistant worldwide with over 1.1 billion monthly users, followed by Gemini with 662 million and Claude with 245 million.

This post is my submission for DEV Education Track: Build Apps with Google AI Studio . What I Built I built MascotCraft Studio , an app that generates a cute mascot character for a coding/tutorial brand using Imagen for the visuals and Gemini for the name and personality bio. Here's the prompt I used: "Please create an app that generates a cute mascot character for a coding/tutorial brand, using Imagen for the visuals and Gemini to create a name and short personality description for the mascot. The user should be able to type in a few style keywords (like 'friendly owl', 'cool robot', 'cheerful fox') and get a unique mascot image along with its name and bio." Gemini went well beyond the basic ask — it added a "Character Designer" with quick preset ideas (Wise Python Owl, Cyberpunk JS Fox, Debugging Robo Kitty, and more), color palette options, multiple visual rendering styles (3D Chibi Toy, Minimal Vector, 16-Bit Retro Pixel, Circular Badge), and even a "Studio Gallery Showcase" using localStorage to save and revisit previously generated mascots. Demo 🔗 Live app: https://cute-coding-mascot-generator-924052444918.us-east1.run.app Using the "3D Chibi Toy" style with keywords for a friendly coding octopus, the app generated Octo-Byte — "Asynchronous learning, multi-threaded fun!" A cheerful deep-sea developer who discovered that having eight arms makes multitasking a breeze, whose tech specialty is multi-threaded asynchronous architecture, and whose favorite pastimes include typing on four mechanical keyboards at once. The artwork came out as a glossy 3D chibi-style purple octopus wearing glasses, sitting in front of a tiny code editor. My Experience Watching Gemini's "Thinking" process work through the build was the most interesting part — it planned out the UI sections, color palettes, and visual styles, then added bonus features I never asked for, like the gallery save feature. The whole thing went from a single paragraph prompt to a fully deployed, live web app in

The fraudsters allegedly targeted hundreds of thousands of people with Gemini-coded scams sites.