标签：#gemini

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.

Grab a refreshing slice of cold watermelon (because it’s summer in Tokyo and I love it!), and let me...

Voice translations preserve speaker's tone, pacing, pitch—with SynthID watermarks for security.

Imagine speaking in English, and having listeners from all over the world hear you translated into...

This article was originally published on aicoderscope.com TL;DR : Gemini 3.5 Flash went GA on May 19, 2026 and costs 50% less than Claude Sonnet 4.6 on input tokens ($1.50 vs $3.00/M). It generates code at ~284 tokens per second — roughly 4.7× faster than Sonnet 4.6. Cursor already lists it natively; Cline needs one extra config step. The trap: Flash's default thinking level is "medium," which is slower and pricier than "low," the setting Google specifically tuned for coding and tool-use loops. Gemini 3.5 Flash Claude Sonnet 4.6 DeepSeek V4-Flash Best for Fast agent loops, context-heavy analysis Complex refactors, instruction fidelity Cost-capped high-volume tasks Input / Output per 1M tokens $1.50 / $9.00 $3.00 / $15.00 $0.14 / $0.28 Context window 1M tokens 200K tokens 1M tokens Terminal-Bench 2.1 76.2% — — Output speed ~284 t/s ~60 t/s — Max output per request 65,536 tokens 64K tokens 64K tokens The catch Output at $9/M erodes savings on code-gen 15× pricier output than Flash No vision, MIT-licensed Honest take : Use Gemini 3.5 Flash with Cline for multi-step agent tasks where round-trip latency compounds and context windows run large. Stay on Claude Sonnet 4.6 when you need a hard refactor to land perfectly on the first try — Sonnet's 79.6% SWE-bench Verified score still leads Flash's on correctness benchmarks. The cost math that does and doesn't work Gemini 3.5 Flash charges $1.50 per million input tokens and $9.00 per million output tokens. Against Claude Sonnet 4.6 at $3.00/$15.00, the input side is a genuine 2× saving. The output side is almost the same story: $9 vs $15 is 40% cheaper per generated token. Run the numbers on a typical Cline coding session: 8 tool calls, reading 12 files (roughly 20,000 context tokens), generating 500 lines of code output (~7,000 output tokens). Sonnet 4.6: (20K × $3 + 7K × $15) / 1,000,000 = $0.165/session Gemini 3.5 Flash: (20K × $1.50 + 7K × $9) / 1,000,000 = $0.093/session That's 44% cheaper per session. At 50 sessions a m

If you’ve tried building AI applications, you often face a familiar engineering wall. It goes like...

Microsoft shut down dozens of GitHub code repositories for Azure and AI coding tools after a reported hack.

NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now.

The Model Comparison Problem Most AI model comparisons are useless for developers making real decisions. They benchmark on academic datasets that don't reflect production workloads. They test frontier capabilities that matter for 5% of use cases. They ignore latency, cost, rate limits, and API reliability — which are the things that actually determine whether a model works in your application. This comparison is different. It's focused on what matters when you're building something: how Grok and Gemini perform on the types of tasks developers actually encounter, what each model's API experience is like, and where the genuine tradeoffs lie. I'm deliberately not including benchmark scores. If you want MMLU numbers, there are plenty of leaderboards for that. This is about production utility. What Each Model Actually Is Grok (xAI) Grok is xAI's model family. The current production models are Grok-3 and Grok-3 Mini, with Grok-3 being the flagship. Grok has a large context window (128K tokens standard, with extended context available), real-time access to X (Twitter) data as a differentiating feature, and strong performance on reasoning-heavy tasks. The xAI API follows a familiar REST pattern and is broadly compatible with OpenAI SDK conventions, which makes migration straightforward. Grok's notable characteristics: Strong at structured reasoning and multi-step problem decomposition Real-time web access via the API (useful for tasks needing current information) Relatively generous rate limits compared to some competitors Less restrictive on certain content categories than some other models Gemini (Google DeepMind) Gemini is Google's model family, currently anchored by Gemini 1.5 Pro and Gemini 2.0 Flash. The defining feature of Gemini is its context window — Gemini 1.5 Pro supports up to 1 million tokens in production, which is genuinely useful for certain document-heavy use cases. Gemini also has the tightest integration with Google's ecosystem (Workspace, Cloud, Search)

Keeping up with trending technical topics and new tools on developer forums can be time-consuming. To save time, I wanted to automate the process of finding popular articles, reading the comments to understand community sentiment, and drafting a summary. While I could write a standard Python script to scrape the dev.to API, simple scripts tend to be brittle. If an article doesn't have comments yet, a basic script will likely crash unless you write extensive error-handling logic. Instead of a rigid script, I built an Agent —a program that can dynamically reason about errors and adjust its approach. If one task fails, it can figure out the next best step. In this tutorial, I'll show you how to build a Trend-Spotting Agent using Python, the Google Agent Development Kit (ADK) , and Gemini 2.5 Flash. What We're Building We are going to write a Python application that acts as an autonomous agent. We'll give it three abilities: Search the dev.to API for rising technical articles based on specific tags. Dynamically fetch the top comments of those articles to read real community sentiment. Automatically draft a newsletter-style article on your DEV.to account summarizing its findings. Prerequisites Python 3.9+ installed on your machine. Google ADK . (Check out the Google ADK Docs if you need help installing). A DEV API Key . Grab this from your DEV.to account settings under "Extensions" and throw it in a .env file. Step 1: Giving the Agent its "Hands" (API Tools) Large Language Models (LLMs) are incredibly smart, but out of the box, they can't actually do anything on your computer. The coolest part about Google ADK is that we can write standard Python functions, hand them to the LLM as "tools", and let the AI decide how and when to use them. Let's write our API functions. Tool 1: Finding Rising Articles Here is our function to fetch rising articles. Pay close attention to the docstring ( """Fetches the top...""" ). We aren't writing this for other developers; the ADK actually