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Google is announcing a big change to the Google Images homepage in honor of the platform's 25th anniversary this week. Instead of a mostly blank page with a search bar, the homepage will soon show you a bunch of images that it thinks you might like before you even start searching. The company says the […]

Google is expected to officially reveal the Pixel Watch 5 on August 12.

“We have no choice but to address the challenges created by these massive facilities,” New York governor Kathy Hochul said. The executive order will pause construction for one year.

Google and industry partners announced Agentic Resource Discovery (ARD) Specification, an open standard for publishing, discovering, and verifying AI tools, APIs, and agents. ARD introduces a discovery layer built on catalogs and registries, enabling dynamic capability discovery while leveraging existing protocols such as MCP and OpenAPI for execution and emphasizing trust and interoperability. By Leela Kumili

A new leak may have just spoiled the Pixel Watch 5 and its finishes ahead of Google's launch event next month. Leaked press renders provided to The Tide Chart by OnLeaks appear to show the upcoming watch with four case finishes: black (Dark Anthracite), polished silver (Natural Silver), yellow gold (Warm Gold), and a duskier […]

Many enterprise AI governance discussions start with frameworks. Frameworks are useful. They help organizations define principles, roles, controls and accountability. But when an enterprise starts using generative AI in real workflows, the practical governance problem often appears somewhere much more specific: the AI access path. That is the moment when an employee, application, copilot, agent or API workflow sends a request to an AI model. At that point, governance becomes operational. The practical governance questions Before an AI request reaches a model, an enterprise may need to answer several concrete questions: Who is sending the request? What business use case is involved? What data is being sent? Which AI model is being used? Is the model approved for this use case? Should sensitive data be masked or blocked? Was the access decision recorded? Can the activity be reviewed later? Can AI usage and token cost be explained by user, department, model and use case? These questions are not only policy questions. They are architecture questions. If the enterprise cannot answer them at the access path, AI governance may remain too far away from the real system behavior. Why the access path matters Many organizations already have AI policies. But policies are often written before or after the actual AI interaction. The access path is where policy meets execution. For example, a team may approve the use of generative AI for internal productivity. But the organization still needs to understand: whether customer data is being included in prompts; whether employees are using approved or unapproved models; whether sensitive content is being sent to external services; whether different departments are using AI in very different ways; whether audit evidence exists when an incident or review happens. This is why AI governance should not only be treated as a document, committee or training program. It also needs a technical control point. A simple access governance pattern A

Link https://leetcode.com/problems/subsets/description/ Problem Given an integer array nums of unique elements, return all possible subsets (the power set). The solution set must not contain duplicate subsets. Return the solution in any order. Example Example 1: Input: nums = [1,2,3] Output: [[],[1],[2],[1,2],[3],[1,3],[2,3],[1,2,3]] Example 2: Input: nums = [0] Output: [[],[0]] Solution First, create a variable subsets, initialized to [[]], as the return value. Loop through nums, and for each element, create new subsets by appending that element to each existing subset. Then, append these new subsets to subsets. Sample code class Solution : def subsets ( self , nums : List [ int ]) -> List [ List [ int ]]: """ 0: [[]] 1: [[]]+[1] -> [[], [1]] 2: [[],[1]] + [2],[1,2] -> [[], [1], [2], [1, 2]] 3: [[], [1], [2], [1, 2]] + [3], [1, 3], [2, 3], [1,2,3] -> [[], [1], [1, 2], [3], [1, 3], [2, 3], [1, 2, 3]] """ subsets = [[]] for num in nums : new_subsets = [ subset + [ num ] for subset in subsets ] subsets += new_subsets return subsets

Demis Hassabis thinks the world needs an AI watchdog with the power to hit the brakes if frontier models become too dangerous. Writing in a blog post, the Google DeepMind CEO and cofounder said the US should lead the initiative, arguing that the country is the best place to set global standards "given its economic […]

There’s more to Google’s messaging app than you might realize.

FIFA’s network was vulnerable to anyone with even minimal access.

Google released the Genkit Agents API in preview for TypeScript and Go. The open-source framework packages message history, tool loops, streaming, and state persistence behind a single chat() interface. Detached turns let agents work after clients disconnect. Interruptible tools provide human-in-the-loop control with anti-forgery validation on resume. By Steef-Jan Wiggers

7 MongoDB Query Mistakes That Return the Wrong Results VisuaLeaf VisuaLeaf VisuaLeaf Follow Jul 14 7 MongoDB Query Mistakes That Return the Wrong Results # mongodb # coding # software # database 3 reactions Add Comment 5 min read

Google has struck a deal to purchase a major solar project's entire electrical output to offset its fossil fuel emissions.

MongoDB queries look simple. You type a field, give it a value, hit run, and you get your data back. But just because a query runs without throwing an error doesn't mean it worked right. Sometimes you get a blank screen. Sometimes you get way too many records. Other times, the data looks fine at first glance, but it doesn't actually match what you asked for. Most of these slip-ups happen for one basic reason: the query structure doesn't match the way the data actually sits in the database. To show you what we mean, we’ll use a clinic database with a collection called visits . Here is what a typical document looks like: JSON { "_id": "6871b6f9c3f1d1a4c2a10001", "status": "completed", "visitDate": "2026-07-01T09:30:00.000Z", "patient": { "name": "Anna Keller", "age": 34 }, "doctor": { "name": "Dr. James Carter", "specialty": "Cardiology" }, "symptoms": ["cough", "fever"], "prescriptions": [ { "name": "Ibuprofen", "active": false }, { "name": "Paracetamol", "active": true } ], "invoice": { "paid": true, "method": "card", "total": 250 } } You can run these examples right in the VisuaLeaf MongoDB Shell . Using visual tools makes a big difference because you can see exactly what MongoDB is returning in real time. 1. Forgetting the Curly Braces This is just a quick typo, but it breaks things right away. The Mistake: db . visits . find ( status : " completed " ) The Correct Query The find() tool always expects an object. Even if you are only looking for one specific thing, you still need to wrap that condition in curly braces {} . 2. Treating $or Like a Regular Object This one trips a lot of people up because the broken version looks like it should work. The Mistake: db.visits.find({ $or: { status: "completed", "invoice.paid": false } }) What is wrong: $or expects an array of conditions, but this query gives it one object. The error will usually be something like: MongoServerError: $or must be an array The Correct Query The first query is wrong because $or needs an array, n

In response to a public records request, HUD has withheld documents about DOGE’s use of AI—in part by citing a privilege that doesn’t exist.

AI makes the first 80% of development feel fast, but hides architectural complexity until it's too late. To prevent system instability, engineering leaders must shift from raw throughput to systemic comprehension. By unifying spec-anchored SDD, TDD, and automated fitness functions into a repo-bound "Context Store," teams can ensure AI agents and human reviewers evolve code safely. By Stella Berhe, Stephan Bragner, Vikram Maran, Anand Jayaraman

Bring on the magenta and peach.

This year's Google Pixel 11 lineup might come in a bunch of funky colors. A series of now-deleted Amazon listings spotted by 9to5Google show what appear to be placeholders for Google's upcoming Pixel 11 in hot pink Fuchsia (Hibiscus), vibrant green Moss (Pistachio), and Midnight (Obsidian) black. We've seen two sets of names for the […]

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example,…

The states allege that the deal would harm movie theaters, basic cable distributors, and audiences.