标签：#Google

The latest version of Google DeepMind's AI model includes a significant jump into “physical AGI.” But plopping AI into the real world comes with risks.

Google Earth users can "visualize the past" via AI-created pictures.

Google's new API relies on parents to set age ranges in Family Link.

Google is expanding its Play Age Signals API, giving Android developers a privacy-preserving way to tailor experiences based on users’ age ranges.

When The Atlantic published a searchable dataset of works used to train AI, Kirk Wallace Johnson, like a lot of artists, looked for his name out of curiosity. And, like a lot of artists, he found it. Essentially, his books, like The Feather Thief and The Fishermen and the Dragon - nonfiction tomes that he […]

Deciding what's real on the Internet won't be easy in the future.

Most guides about Google Forms and your website answer a question you did not ask. Search for how to replace a Google Form with your own HTML and you get three kinds of answer. Embed the iframe but style the container. Use a service that hides Google's branding. Or the clever one: build your own HTML form and point it at Google's endpoint, so responses still land in your existing spreadsheet. All three keep Google Forms in the loop. If that is what you want, they work, and I will show you the third one because it is genuinely useful when you need it. But if you actually want the Google Form gone, replaced by markup you own, here is how that works and what it costs you. One-line summary: Google Forms does one thing your static site can't, accept a POST; swap that for a form endpoint and you get your markup back, at the cost of owning spam and losing free-unlimited. Why the iframe is the problem The embed is an iframe. That means: You cannot restyle it. Your fonts and colours stop at the border. It does not resize with its content, so a long form becomes a scroll area inside your page. It looks like Google on your site, because it is. You inherit its accessibility behaviour and can do nothing about it. None of that matters for an internal survey or a sports club sign-up sheet. It matters a lot on a business site, where a Google-branded iframe reads as a stopgap someone never got round to replacing. The clever workaround, and where it breaks You can POST your own HTML form straight at a Google Form's response endpoint. Open your form, inspect the page, dig the field IDs out of the markup, and build a form whose input names match: <form action= "https://docs.google.com/forms/d/e/YOUR_FORM_ID/formResponse" method= "POST" > <input name= "entry.1234567890" type= "email" required > <textarea name= "entry.9876543210" required ></textarea> <button type= "submit" > Send </button> </form> Responses land in the same spreadsheet. No new service. For a throwaway internal page, thi

A thousand workers from OpenAI, Anthropic, Google, Meta and more have signed the letter.

Analysis of 15 million real AI interactions finds most tasks at most jobs are unaffected.

Employees of OpenAI and Anthropic, as well as Google, Meta, Thinking Machines, Microsoft, Mistral, and other leading AI labs, have written a statement to the US government supporting a potential slowdown of sorts for frontier AI development - or at least a speed-up of global coordinated governance efforts. "Al could help create a dramatically better […]

It's earnings season, and investors got an unpleasant surprise from Google: an increase on its spending estimate, to as much as $205 billion - from the last quarter's projection of up to $190 billion. Even the lower end of Google's new projected range - $195 billion - is much more than the company had previously […]

This article provides a step-by-step guide to building and testing a cross-cloud currency agent. A coordinator built with Strands Agents and hosted on Amazon Bedrock AgentCore Runtime (in AWS us-east-1 ) discovers and delegates to a Google ADK agent (on GCP Cloud Run in us-central1 ) over A2A v1.0 , cross-checks results against an MCP exchange-rate tool , and measures what independent cross-cloud verification costs in latency, reliability, and overhead. What is This Project Trying to Do? Most Agent-to-Agent (A2A) protocol demos stop at "look, the HTTP 200 OK request succeeded." That is a smoke test, not an interoperability benchmark. This project goes further: an Amazon Bedrock AgentCore-hosted Strands Agents coordinator discovers and delegates to a Google ADK agent running on GCP Cloud Run, comparing the results against a local MCP stdio exchange-rate tool backed by live Frankfurter daily reference rates. We also compare the performance, developer experience, and wire compatibility directly against our previous benchmark run hosted on Microsoft Foundry in Azure ( gpt-5-mini ), giving us a true cross-cloud benchmark across AWS, Azure, and GCP. The questions we answer with hard empirical data rather than vibes: Can an AgentCore-hosted Strands agent discover and invoke a Google ADK agent through an A2A agent card with no framework-specific glue? What latency and token overhead does remote-agent verification add? Does independently verifying an MCP tool result over A2A improve correctness or failure recovery enough to justify that overhead? How does AWS Bedrock AgentCore Runtime compare like-for-like with Microsoft Foundry on Azure? Reduce, Re-Use, Re-Cycle! This builds directly on the currency agent from the previous articles in this series: Getting Started with MCP, ADK and A2A | Google Codelabs GitHub - jackwotherspoon/currency-agent That agent — built with Google ADK, Gemini 2.5 Flash, and a FastMCP exchange-rate server backed by the free Frankfurter API — serves a

Keeping phones out of landfills and doing useful things is a good thing.

The screwup shows how tricky it can be to stop web crawlers from making ostensibly private conversations with AI chatbots entirely too public.

Here's what the rumor mill tells us Google might be preparing for its next Pixel launch.

Google’s AI Overviews now appear in 43% of searches, underscoring how quickly AI-generated answers are becoming the default way people discover information online.

Nota: ✋ This post was originally published on my blog wiki-cloud.co Introduction Artificial intelligence is evolving at an unprecedented pace and is transforming how people and businesses interact with technology. Over the past few years, much of the focus has been on generative AI models, which can create text, images, code, audio, and other types of content from natural language instructions. These capabilities have marked a significant and transformative shift in how we perform many tasks, allowing AI to move from a specialized technology to an accessible tool for millions of users. However, we are entering a new stage. Artificial intelligence models are no longer limited to simply answering questions or generating content. They can now be autonomous, understand objectives, analyze context, decide what steps to take, use tools, consult different sources of information, connect with APIs, execute actions, and collaborate with other specialized agents to complete more complex tasks. This evolution is giving rise to what is known as agentic artificial intelligence, an approach in which AI systems can act with a greater level of autonomy and actively participate in business, technical, and operational processes. Instead of simply offering a recommendation, an agent can search for information, validate data, coordinate different activities, and execute a sequence of actions aimed at achieving a specific goal. Within this new scenario appears Google Agent Development Kit , also known as Google ADK , is an open-source framework developed and designed by Google to facilitate the creation, evaluation, and deployment of artificial intelligence agents. ADK provides developers with a structure for defining agent behavior, connecting them to language models and external tools, managing sessions and memory, coordinating multi-agent systems, and evaluating their performance before deploying them to production. Thanks to this code-based approach, Google ADK allows you to build e

If you want to tweak your Android Auto settings, the software has a developer menu you can check out.

I went looking for a simple answer to a simple question: How do you give an agent access to Google Calendar? Not a demo. Not a screenshot. A real agent, running unattended, with enough access to be useful and enough guardrails that it won’t turn into a security incident. While researching OpenClaw setups, I found a thread on r/openclaw where someone asked what looked like a tiny question: what do I need to add Google Calendar to OpenClaw? One reply said: "Look into gog cli." That answer is way more revealing than it looks. Because the hard part usually isn’t Google Calendar itself. The hard part is everything hidden behind the phrase "connect Google" . And if you’re building agents in n8n, Make, Zapier, OpenClaw, or a custom OpenAI-compatible loop, auth is only half the problem anyway. Once the workflow runs 24/7, you also need to think about retries, quota limits, caching, and how many LLM calls the thing is quietly making in the background. That’s where a lot of teams hit the same wall: the integration works, but the operational shape of it is bad. Security is fuzzy. Request volume is noisy. And AI costs get weird fast if every poll and retry triggers more model calls. The demo version is lying to you If you’ve used something like n8n Cloud, you’ve seen the polished version: Click Google Calendar Sign in Approve access Done That flow is real inside a managed product. But the minute you leave the managed garden — self-hosted n8n, OpenClaw, a custom MCP server, a Python worker on Ubuntu, or your own app using the OpenAI SDK against an OpenAI-compatible endpoint — you inherit the boring parts. Now "connect Google" actually means: create a Google Cloud project configure the OAuth consent screen choose the right OAuth client type enable the Google Calendar API pick the right scopes store credentials safely handle refresh tokens deal with quota errors later That’s not setup trivia. That’s infrastructure. One user in that same OpenClaw discussion realized it immediately:

If your automation just stopped mid-run, you have hit a quota limit. Here is exactly which one and how to fix it — free, no upgrade required, works at any order volume. Quick answer — the numbers that matter in 2026: Both consumer (free) and Google Workspace accounts get a 6-minute maximum execution time per script run — the old 30-minute Workspace limit no longer applies. Consumer accounts are capped at 90 minutes of trigger runtime per day; Workspace accounts get 6 hours of trigger runtime per day. UrlFetch calls are capped at 20,000 per day on consumer accounts (100,000 on Workspace). These are the hard limits that cannot be increased — they are why Autocrat, Sheets automations, and document workflows fail at scale. If you have ever seen “Service invoked too many times”, “Exceeded maximum execution time”, or “Could not obtain lock”, you already know the symptom. This guide explains the exact numbers behind those errors, when they appear by account type, and what actually works when order or document volume gets serious. What Are Google Apps Script Quotas? Google Apps Script quotas are hard limits on how much work a script can do. They exist to protect shared infrastructure — stopping one workflow from consuming resources that affect thousands of other users. Quotas appear across four layers, and they all apply simultaneously: User-level quotas — tied to the Google account running the script. Consumer and Workspace accounts have different ceilings. Project-level quotas — tied to the Apps Script project itself. Concurrent executions are capped here. Service quotas — Gmail, Sheets, Drive, UrlFetch, and other services each have their own daily limit. Execution quotas — limits on how long a single run can take and how much total runtime is consumed in a day. The critical point is that these limits stack independently. A workflow can be fine on execution time but fail on service call rate — which is why the same script can work perfectly for a small operation and break