标签：#google

Apple chose Google Cloud to run Private Cloud Compute outside its own data centers for the first time, using NVIDIA Blackwell GPUs, Intel TDX, and Google's Titan chip. Apple maintains an independent append-only hardware ledger and dual-vendor attestation roots. AWS and Azure are not part of the collaboration. By Steef-Jan Wiggers

When I checked Google Search Console after a month, only 2 of my 8 sites were indexed. The other 6 had zero pages in Google's eyes. No penalty, no error banner. Just silence. The bug My build script generated the sitemap by mapping over page objects. Somewhere a URL field was an object, not a string. So the sitemap shipped lines like: <url><loc> https://example.com/[object Object] </loc></url> Google fetched the sitemap, saw garbage URLs, and quietly skipped the whole file. No crawl, no index. How I caught it GSC > Sitemaps > it said "Success" but "Discovered pages: 0". That mismatch is the tell. I opened the raw sitemap.xml in the browser and searched for [object . There it was. Root cause: url: page.url where page.url was itself { path, params } , not a string. The fix // before loc : page . url // -> [object Object] // after loc : `https://livephotokit.com ${ page . path } ` Redeployed, resubmitted the sitemap, and requested indexing on the core pages. Pages started landing in the index within a couple of days. Takeaway A "Success" status on your sitemap does not mean Google read your URLs. Always open the raw XML and eyeball it. One bad [object Object] can silently sink an entire site. I'm building LivePhotoKit and a handful of other small tools solo with AI. Sharing the real bugs as I hit them.

Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.

Smart speakers have spent the past few years searching for a compelling second act. Beyond music, timers, and controlling your lights, they've struggled to justify taking up space on the kitchen counter. AI promised to change that. Amazon debuted its new hardware powered by a revamped Alexa last fall, and now it's finally Google's turn. […]

Adjust your options for things like account recovery, ad personalization, and which parts of your Google profile are shared publicly.

Tenor still connects to Google apps, but other platforms must look elsewhere for GIFs.

Google's NotebookLM is adding a new way to catch up on your notes: TikTok-style AI videos. The new feature is rolling out to Google AI Ultra and Pro subscribers, allowing NotebookLM to generate 60-second vertical AI clips based on the sources you upload to the app. The example shared by Google details Australia's unsuccessful war […]

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.

They may not look as good, but Nano Banana 2 Lite images only take a few seconds to create.

The GIF-picking interfaces in some of your favorite online platforms might look different going forward, as Google prepares to shut down the Tenor API today. While the Tenor website, along with its searchable GIF library, will remain live, platforms like X, Discord, Bluesky, and WhatsApp that previously integrated the API are now having to migrate […]

You can use Gemini to quickly search your Gmail inbox with natural language.

Cost is everything. In just about every agentic conversation, the three things that come up for enterprises implementing AI workloads are: Cost Observability Security and as AI continues to throw everyone for a loop when it comes to cost management (e.g - Uber running out of the yearly token budget in one quarter), the ability to shrink resource (like hardware) usage will be crucial moving forward. In this blog post, you will learn how to cust costs by 90% using Agent Susbtrate in comparison to Agents running in k8s Deployments/Pods. The Cost Comparison Agents need a place to run. The "place to run" needs to be a platform that's easily managed, orchestrated, and has the ability to cluster resources. Resources like CPU, GPU, and memory need to be able to scale and expand. Without this, it's a matter of manually managing servers that Agents are running on and clients to interact with said server. That's why so many organizations choose Kubernetes to run Agentic. When running Agents per Pod, however, that can get costly very quick in terms of hardware (GPU, CPU, memory) and performance (can your cluster scale up and down quickly based on resource needs when it comes to Agents coming up and going down per use?). The tests in this blog post show: Always-on Agents running in k8s. Actors running in Workers via Agent Substrate And the comparison will be 50 always-on Pods in comparison to 50 Actors across 5-7 Workers (Pods). If there are 50 Agents running per Pod and 50 Agents running per Worker with 5-10 Actors per Pod, you can already imagine the hardware resource savings that can be accomplished. Right now, the majority of organizations start off with the "one Agent per Pod" approach as that's the fastest way to show value and get up and running. For the future, however, Agents in Actors via Agent Substrate will be how organizations deploy when they care about efficiency, optimization, and managing cost. Let's dive in from a hands-on perspective. Prerequisites To follow a

SCOTUS falls short of deeming geofence warrants unconstitutional, though.

Google expands personalized intelligence to Gemini app image creation

The EU wants Google to share search data with competitors and open up AI on Android, but Google alleges major privacy risks.

The Supreme Court's decision to limit geofence warrants is a win for privacy advocates, who called their use unconstitutional but sought an outright ban.

Anatomy of the Trust Gap Before we can talk about the solution, we need to talk honestly about how...

Abstract Executing autonomous AI agent payloads in Google Workspace via the Apps Script...

If you send SMS one-time codes, there's a decent chance you're paying scammers to phone-spam themselves on your dime. It even has a name: SMS pumping . And it's not a rounding error — Elon Musk claimed Twitter was losing ~$60M/year to fake 2FA traffic before they killed SMS 2FA for free accounts. Here's how the scam works, why SMS 2FA is structurally expensive, and why flipping the direction — mobile-originated (MO) 2FA , taken to its logical end over iMessage — fixes both the cost and the fraud at once. What is SMS pumping? SMS pumping (also called AIT — Artificially Inflated Traffic , or SMS toll fraud ) is a scheme where bad actors abuse a form that sends SMS one-time codes. They pump thousands of phone numbers — usually premium ranges they secretly control with a telecom — into your "send me a code" endpoint. You pay for every one of those messages. A cut of that termination fee flows back to the fraudsters via the carrier. The "users" never log in. They were never users. The entire point was to make your verification endpoint dial a meter that pays them. The structure that makes this possible is simple: you, the company, send (and pay for) the message. Every code is revenue for someone in the delivery chain — so there's a direct financial incentive to trigger as many as possible. Why SMS 2FA is expensive even without fraud Even with zero abuse, application-to-person ( A2P SMS ) is a bad cost curve: You pay per message. Volume spikes — a launch, a bot attack, an international audience — turn into surprise bills. International is brutal. Cross-border A2P carries steep carrier surcharges that vary wildly by destination. Carrier fees and registration overhead. In the US you're funneled through A2P 10DLC registration, brand vetting, and per-segment fees before you send a single legit code. So your 2FA line item is pay-per-event , unpredictable , and exploitable . Three bad properties for something that's supposed to be boring infrastructure. The Twitter/X case This

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