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Google has a new suite of creative design tools for Workspace users called Google Pics, which aims to make editing and generating "professional-grade" AI images less cumbersome for businesses. Built around Gemini and the Nano Banana generative AI model, Google Pics is designed to give more granular control over prompt-based image making and manipulation, allowing […]

Early Androids were the tech equivalent of alternative rock. iPhones were far too mainstream for some of us, and Android felt like home.

Android can help you keep your phone safe from malicious apps, scam calls, invasions of privacy, and much more. You just need the right settings.

Google raised the price of its 4K streaming box to $149, up $50 from its original $99 price. The new price is currently live at the Google Store and Best Buy, but Amazon appears to still be offering the original price. The price hike comes just a couple of weeks after Google launched its new […]

Every company knows when it revoked access. None knows when access stopped. I built this for the All Things Agentic Hackathon , and I wrote this post for the purposes of entering that hackathon. Code: github.com/NexuChat/parallax The chore I was actually trying to kill I maintain a web application with two roles, two languages, one of them right-to-left, a dark theme, and three viewport sizes. Every release, I would open it as the owner, click through, sign out, sign in as a member, click through again, switch to Arabic, reload, shrink the window, reload — and try to remember what a page had looked like ten minutes earlier. The worst defects never survived that process, because they are not visible in any single session. A member opening a page they should have been denied sees nothing wrong. Nothing on the page says "you should not be here." The information is not in their session at all. It is in the difference between their session and the owner's. So I stopped testing sessions and started comparing them. Seven witnesses, one axis apart Parallax opens seven isolated browser contexts at the same instant against the same application. One is a baseline — owner, English, light, desktop. The other six each change exactly one axis from it: privilege, locale, theme, viewport. The full product of those axes is thirty-six combinations. Seven one-axis derivations is not just cheaper; it is the only version that can attribute a cause. When the Arabic witness disagrees with the baseline and locale is the only thing that changed, locale is the reason. With thirty-six combinations you get a bigger table and less knowledge. Each axis carries a contract about what must change and what must not: Axis Contract A finding is Privilege access must differ sameness — an escalation Locale access constant, layout mirrors access drift, or geometry that did not mirror Theme access constant, layout does not move any positional shift Viewport access constant, reflow allowed access drift That

I wrote this post for my entry to the All Things Agentic Hackathon. TLDR: I built a five-agent design team on Gemini (Including Gemini Flash 3.7 and Gemma 4) that takes a brief and a folder of photographs and returns finished, editable pages. The interesting engineering was not the prompts. It was deciding wh ere the run's progress lives. Code: github.com/minhthanhdang/vibes-ai . What it does Vibes AI is a design co-pilot. Upload photographs, describe what the thing is for, and it designs the pages: real crops, generated backgrounds, type in any Google Fonts family, all written as geometry that can be dragged afterwards. There are five agents. An orchestrator holds the other four as tools, so every hop is request and response, and the user reads one reply instead of a transcript of agents talking to each other. A property analyzer reads each upload in six design dimensions. An image editor cuts. An image generator draws the picture the gallery does not have. A design assistant does the actual designing. The part I want to write about is the unattended run. One form (purpose, page count, palette, vibe, size) and then no further human input until the pages are done. One long request was the wrong shape Designing six pages is minutes of model calls, not milliseconds. My first instinct was one request that loops over the pages and returns when it is finished. That shape gives nothing back. No honest progress, no Stop button that means anything, and a failure at page four throws away pages one to three. So a page became the unit of work. One job designs one page. The job is a row in an AgentRun table, a worker claims it under a lease, and when it settles it enqueues the next page inside the same transaction that marks the current one done: const chained = await db . $transaction ( async ( tx ) => { const won = await tx . agentRun . updateMany ({ where : { id : run . id , status : RunStatus . RUNNING , startedAt : run . claimedAt }, data : { status : RunStatus . SUCCEEDED

Several Garmin watches offer a built-in LED flashlight, but even if you have another brand, you likely have a decent light on your wrist.

Google rolled out the Lake America change on Saturday, while the official US government maps are still waiting.

Sometimes the volume control on a Bluetooth device behaves unpredictably. This quick fix in your Android phone's settings is a good troubleshooting step.

If you use Android Auto, you might be interested to know that there's a feature available on some phones that helps keep phone calls minimal while you drive.

Long-lived GCP service account keys are secrets that must be managed forever, are hard to rotate, and are easy to leak. Scaling Workload Identity Federation to 120+ production projects shows why it changes how machine identity is approached entirely: keys are secrets to manage, federated identities are trust relationships configured once, gated by attribute conditions. By Shijin Nair

If the sound you're getting through Android Auto is disappointing, there are several components and settings you should check.

If your algorithm is out of whack recently, there are things you can do to get it back on track with content you like.

After Donald Trump’s executive order demanding the name change, Google is the first major online maps provider to flip the switch.

It'll still show up as Lake Ontario for Maps users in Canada.

Android Auto has several essential features enabled by default, but you may be surprised at how much it can really do.

Smart TVs store lots of data, and the amount they have to work with is often quite low. There are ways to maximize your storage space, though.

I wrote this for the All Things Agentic Hackathon. Every incident-response demo you have seen ends the same way: something breaks, the agent fixes it, everyone applauds. I want to show you the opposite. Here is my agent, at 95% confidence, having correctly diagnosed a bad deployment, deciding not to roll it back. That refusal is the whole project. The question underneath At 3am an alert fires. An engineer wakes up, reads several hundred log lines, correlates them against recent deploys, and rolls something back. Most of it is mechanical. It is an obvious target for automation. But "automate it with an LLM" does not dissolve the problem, it relocates it. The new question is: how much would you let an agent change in production without asking you first? Give it too little and it is a chatbot that writes summaries. Give it too much and one confidently wrong diagnosis takes down your service at 3am with nobody watching. I named the project Sonjomon — Bengali for restraint. The autonomy ladder An agent should not have one blanket permission level. How far it may act alone is a function of two things: how confident it is, and how much damage the proposed action does if that confidence turns out to be wrong. tier = f(confidence, blast_radius) OBSERVE record findings, take no action SUGGEST recommend to a human, do not execute APPROVE stage the action, execute on explicit approval ACT execute now, then verify independently A restart is medium risk — reversible in seconds. A rollback is high risk — it shifts production traffic, and a needless rollback during a real outage extends it. Deleting data is critical, and no confidence level unlocks it. Six conditions can only ever push the tier down, never up: the blast-radius ceiling, thin evidence, a similar action that just failed, a third attempt at the same fix, a stale incident, and a global dry-run switch. Nothing pushes it up. A wrong action is far more expensive than a missed one. Three things the model does not control It

Chances are there's a connection issue between your phone and car if Android auto isn't working.

I created this article for the purpose of entering the All Things Agentic Hackathon. TL;DR — An ADK output_key writes into the session of the agent that declares it. In-process that session is shared, so it looks like state flows. Across a RemoteA2aAgent hop it is the worker's session, and it never comes back. Nothing raises. Nothing warns. Every local run and every CI job exercises the working topology, so the failure is invisible to an offline test suite by construction — including at 100% coverage. The system that passed Bastion is a three-agent access-governance fleet built with Google ADK and A2A. An Orchestrator owns investigation state, an Access Auditor reads production IAM through a read-only identity, and a model-free Escalation Agent delivers validated count-only reviews. The local graph passed its configured core statement and branch coverage gate. Every branch, every seam. Then the same graph was split across deployed A2A workers, and an assumption that looked natural in-process became false. The boundary we had not modeled In-process, the previous step's result is simply there : # The Auditor declares output_key; the Orchestrator reads it back. report = ctx . session . state . get ( AUDIT_FINDINGS_KEY ) Deploy the same sequence and only the construction changes. The graph is identical: RemoteA2aAgent ( name = " access_auditor " , agent_card = card_url ( auditor , " access_auditor " ), description = " Reads the live IAM policy and flags anomalies. Read-only. " , httpx_client = private_a2a_client ( auditor ), a2a_request_meta_provider = _forward_investigation , ) output_key still writes. It writes into the worker's session, which never crosses back. The deployed Orchestrator saw an empty state key while every local run and every test saw a populated one. Observed 2026-08-22: the Auditor completed a full sub-trail, and the next step then refused with "returned no structured report." No exception at the boundary. No warning at construction. The run still r