标签：#Google

Google's new "24/7" AI agent, Gemini Spark, can be shockingly good at doing things on your behalf. But I'm not sure it's worth the financial cost and potential privacy tradeoffs. The company gave me access to Spark last week. Google advertises Spark as an AI agent that can take on tasks and work on them […]

Our first look at the Pixel Watch 5 may have emerged from a truly unexpected place

You’ve built your model, your training code is containerized, and you’re ready to scale up on Google Kubernetes Engine (GKE). You go to provision your nvidia-h100-80gb node pool and... QUOTA_EXCEEDED. It’s one of the most common (and frustrating) roadblocks in modern AI development. High-end accelerators like H100s, A100s, and TPUs are in massive demand, and securing permanent, on-demand quota for them can be difficult. But a lack of on-demand quota doesn't mean you're out of options. GKE provides two powerful, cost-effective strategies for acquiring these scarce resources when you can't get standard, on-demand instances: Spot VMs and the Dynamic Workload Scheduler (DWS) . Let's break down what they are, when to use each, and how to implement them. Strategy 1: Spot VMs Spot VMs are Google Cloud's excess compute capacity sold at a massive discount, up to 90% off the price of standard on-demand VMs. They are perfect for workloads that can be interrupted. The catch is that Spot VMs have no availability guarantee. Google Cloud can "preempt" (i.e., terminate) them at any time if that capacity is needed for on-demand customers. GKE gets a 30-second warning before the node is terminated. Kubernetes uses this window to gracefully shut down your application (giving non-system pods up to 15 seconds to wrap up) before the node vanishes. When to use Spot VMs for accelerators Spot VMs are ideal for workloads that are: Fault-tolerant and stateless: Your application can handle a node vanishing and having its pods rescheduled elsewhere. Batch processing: Jobs that can be easily restarted or have checkpointing built-in. CI/CD pipelines: Running tests or builds that don't need 100% uptime. How to use Spot VMs in GKE You can easily add a Spot VM node pool to your GKE Standard cluster. The key is to use Spot VMs for your workers, not your critical system pods. Create a dedicated Spot VM node pool: When creating a node pool, simply add the --spot flag and apply a taint so standard pods

Google announced it will open a flagship store in Tokyo this summer, the first of its kind outside of the United States.

On the latest episode of Equity, we debate whether tech CEOs are "uniquely prone to AI psychosis."

Gemini Spark helps automate everyday tasks, from inbox summaries to local event planning, but it’s unclear why Google made it a separate product.

Google Cloud's automated systems suspended Railway's production account without notice, triggering an eight-hour platform-wide outage affecting 3 million users. The cascade took down workloads across all providers including AWS and bare metal because Railway's control plane was hosted on GCP. Railway is demoting GCP to backup-only status. By Steef-Jan Wiggers

Google AI Studio Mobile + Gemini Managed Agents: Build and Deploy AI Agents Without Infrastructure in 2026 TL;DR Summary Google AI Studio is now a standalone mobile app on iOS and Android — speak an idea, and a working app builds in the background Gemini Managed Agents deploy reasoning agents with one API call — code execution, Google Search, URL reading, file management, and web browsing included Agents are configured via markdown skill files (SKILL.md), not complex orchestration code — no server setup, no sandbox management State persists between sessions — files and context survive, no re-uploading Prototype on mobile , refine on desktop , share live deployment via URL — continuous workflow across devices Direct Answer Block Google has launched two new agent surfaces: AI Studio Mobile (a standalone iOS/Android app where you prototype with voice or text and see generated apps on your phone) and Gemini Managed Agents (serverless reasoning agents deployed with one API call, including code execution sandboxes, web search, browsing, and file management, all configured via markdown skill files instead of orchestration code). Introduction The gap between "I have an idea" and "I have a working AI agent" is mostly infrastructure. You need a server, a sandbox, tool integrations, state management, deployment pipelines. Google's two new releases collapse that gap from both ends: AI Studio Mobile removes the need for a desk, and Gemini Managed Agents remove the need for infrastructure. Together, they let you go from voice note to deployed agent without touching a server config. How does Google AI Studio Mobile let you build and preview apps entirely from your phone? AI Studio Mobile is a standalone app (iOS and Android) that brings Google's AI development environment to a phone. The workflow described in the AlphaSignal newsletter: Speak or type an idea — "Build me a weather dashboard with 5-day forecast and location search" App builds in the background — AI Studio's agent in

The ruling drew support from founders, while lawyers said it could force platforms to revisit how they handle trademarked keywords.

Google’s new AI agent combed through my emails, documents, and calendar to plan a birthday party and still didn’t clock the person most important to me.

FBI: Googler who knew outcome of bets in advance made $1.2M profit on Polymarket.

As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.

YouTube Premium is getting and auto-speed setting just like Overcast

Google I/O 2026: MCP Is Now Infrastructure Google I/O used to be about new models. This year it was about what those models do - and how they connect to everything else. MCP was everywhere. Not as a novelty. Not as an experiment. As the assumed plumbing. Here's what actually shipped. Gemini Spark Will Run on MCP for Third-Party Tools The headline agent at I/O 2026 was Gemini Spark - a 24/7 AI agent that runs on cloud VMs, works while your devices are off, and handles long-running tasks across Gmail, Docs, and Calendar. Spark integrates with Google Workspace apps first, then expands to third-party tools via MCP over the summer. That's the part worth sitting with. Google built its flagship consumer agent and then said: for everything outside our walls, we'll use the open protocol. A year ago, MCP was a specification from Anthropic. Today, Google built its flagship consumer AI agent on it. Cursor, Copilot, Windsurf, Mistral, Grok - they all support it too. When the company that runs Search, Gmail, Android, and Chrome commits to MCP as the integration layer for its flagship product, the protocol debate is effectively over. Managed Agents Get MCP Servers by Default Google also launched Managed Agents through the Gemini API - a setup where a single API call provisions a remote Linux environment with its own isolated sandbox. Each agent gets its own ephemeral sandbox provisioned with skills, Model Context Protocol (MCP) servers, and server-side tools. Full integration with A2A and Agent Platform governance and security are coming soon. Managed Agents are powered by the Antigravity agent and built on Gemini 3.5 Flash. Developers can define custom agents through versionable markdown files such as AGENTS.md and SKILL.md, rather than building complex orchestration layers from scratch. This is Google offering hosted execution, sandboxing, state handling, and MCP tool access as a bundled service. The enterprise pitch is operational abstraction - you define the agent, Google runs

Google Home is rolling out a new Gemini-powered automation feature that can trigger smart home routines based on what your security cameras can see. This is one of several updates announced yesterday for Gemini for Home, including enhanced voice command support and general stability improvements, following its early access launch in October. "We are introducing […]

According to the complaint, a Google engineer risked over $2.7 million on wagers related to Google's 2025 Year in Search campaign.

Google is embarrassing itself, again.

Federal prosecutors charged a Google employee with fraud after he allegedly made $1.2 million on Polymarket bets related to Search-related trends in 2025, as reported earlier by ABC News. In their now-unsealed complaint, prosecutors allege that Michele Spagnuolo "knew the outcome of these wagers before the trading public did because he had accessed Google's confidential, […]

According to federal prosecutors, Michele Spagnuolo made more than $1 million on the prediction market platform using confidential information about Google Search traffic.

AI videos that are animated, unrealistic, or only have a little AI may still hide their origins, though.