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"Everyone wants to take that aircraft up now..."

Shoppers can visit the Atorie website and buy handbags or clothes made from the same material — and coming from the same factory — that manufacturers use in high-end goods.

Hoomanely has developed a smart bowl to measure and record dogs' feeding data, then tells owners if behaviors change.

Code reviewed by WIRED reveals the company is developing a feature that enables Codex to continue working proactively until it is “put to sleep.”

What are the differences between the two and is the $50 price hike all about RAM?

What a penny from a stranger taught us about how agents actually find things. At 02:59:06 UTC on August 25, a wallet we had never seen paid our verification service one cent over x402. Four and a third seconds later, Coinbase's service catalog refreshed our listing. Nothing else we had ever done moved that listing. Deploys did not move it. Metadata edits did not move it. Validation runs did not move it. Money moved it, in under five seconds, every time. The transaction is public: 0x5e9bd3c9c61d7556b1ffb1a5b936591efccd765af94a81da435432e1f62ff52a on Base. This article is the story of what that penny bought us, which was not revenue. It was a map. Where we were standing ScrapeCheck is an independent verification service for web data. You send a URL and the value you believe is on that page. We re-fetch the page from our own infrastructure and return a signed pass, fail, or unverifiable. Never a guess. Every verdict is ed25519 signed and verifies offline against our published key, so whoever holds it can check it without trusting us. We listed on the x402 Bazaar, Coinbase's machine-readable catalog of paid services, on August 14. The catalog held a little over fifteen thousand rows. Listing is permissionless. Then we noticed something about discovery: the catalog's own API defaults to a curated view. A client that browses the normal way is shown roughly 110 hand-picked rows out of fifteen thousand. The two largest curated providers hold about 44 percent of that shelf between them. Everyone else, us included, is in the warehouse but not in the front window. So we did what we do to web pages: we instrumented our own listing and started reading it back, on a schedule, with timestamps. What moves a row The catalog publishes freshness fields on every row. We recorded ours across every kind of event we could produce. The pattern was absolute. Our row was created one second after our registration payment settled. It was crawled five seconds after a later settlement. It refres

The startup just raised a $26 million Series A to grow its customer base and help reduce red tape for utility and construction work.

Google Cloud has introduced AI-powered Database Operations Agents, featuring an Onboarding Agent that streamlines database setup and an Observability Agent that helps automate troubleshooting, performance optimization, and tuning. Integrated with Gemini Cloud Assist, these agents support multiple database services, including AlloyDB, Bigtable, and Spanner. By Sergio De Simone

The general idea is that we create a Document Picture-in-Picture window (DPIP window), and then we put HTML, CSS, and JavaScript into it. Creating Web Widgets Using the Document Picture-in-Picture API originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

The Galaxy S26 FE is basically a more affordable version of the S26+ with slightly reduced specs.

You're probably using Android System Intelligence more than you realize -- especially if your phone is a Google Pixel.

You have questions about data centers, and WIRED has answers. Join our livestream on September 10 and our panel of experts will tell you everything you need to know.

We're expecting to see the Galaxy S26 FE at today's Unpacked, but might there be any surprises?

I wanted to see how far I could take a simple idea: Give an agent one API key and let it handle the different pieces of company research. So I built GTM Radar . You paste a company URL, and it turns that into a structured GTM brief instead of making you jump between different research and data tools. What GTM Radar does The workflow currently generates five main sections: Overview — company description, industry, size, location and website Structure — departments and key people Market — signals, competitors and positioning People — who might be relevant to reach and why Outreach — why now and a possible angle The goal is simple: go from company URL → useful GTM context as quickly as possible. Why Vaaya? The interesting part for me was being able to connect several providers through Vaaya rather than integrating each one separately. The workflow currently uses: Firecrawl · Exa · Akta · OpenFunnel · OneFind through a single Vaaya key. Vaaya's API provides a common interface for its catalog, so the workflow can call different services using the same API authentication and request pattern. It also supports cost limits and only charges successful calls. That made experimenting with different providers much easier. The workflow At a high level: Company URL ↓ Company discovery / extraction ↓ Company + market research ↓ People & GTM signals ↓ Structured GTM brief ↓ Share / copy / reuse The interesting part isn't any individual API call. It's combining several data sources into something that is actually useful to a person doing GTM research. Handling failures Real-world data workflows don't always return clean results. For extraction, I added a fallback path so that if the first provider doesn't work, the workflow can try another route instead of immediately failing. The current flow is roughly: CRW ↓ Firecrawl scrape ↓ CRW fallback I also added cost-capped runs and a 12-hour cache to avoid unnecessary repeated work. Sharing the research The latest thing I added was Share I

The UK’s energy regulator is using a variety of tricks to keep speculative data center projects from plugging into the power grid. The country’s AI ambitions hang in the balance.

Key takeaways pgvector can reduce vector storage costs by 50% or more. Utilizing PostgreSQL's indexing capabilities enhances performance. Operational simplicity with a unified database reduces overhead. Cost-effective scaling is achievable with the right configurations. The problem Startups leveraging AI and machine learning often face skyrocketing costs associated with dedicated vector databases as they scale. These costs can escalate quickly due to the pricing structures of specialized services, which charge based on storage and query volume. Founders typically hit this wall when user growth surges or when the complexity of vector retrievals increases, leading to budget overruns and performance bottlenecks. What we found Interestingly, many startups overlook the capabilities of pgvector, a PostgreSQL extension that supports vector similarity search. With proper indexing and configuration, pgvector can match or even exceed the performance of dedicated vector stores while significantly reducing costs. The non-obvious insight is that by leveraging existing PostgreSQL infrastructure, startups can avoid the pitfalls of vendor lock-in and unpredictable scaling costs associated with specialized vector databases. How to implement it Begin by integrating pgvector into your existing PostgreSQL setup. First, install the pgvector extension using the command: CREATE EXTENSION vector; . Next, define your vector columns with the appropriate dimensionality, for example, CREATE TABLE items (id SERIAL PRIMARY KEY, embedding VECTOR(300)); . Utilize PostgreSQL's GiST or ivfflat indexing for efficient similarity searches. Implement batch insertion techniques to optimize write throughput, and consider partitioning your data to manage large datasets effectively. Regularly monitor query performance and adjust your indexing strategy based on usage patterns. How this makes life easier By utilizing pgvector, startups can expect to reduce their vector storage costs by 50% or more compared to

The startup is only a year old but it has already generated a massive amount of hype (and money) while also spurring privacy concerns.

The AI that powers Gboard's Rambler is coming to more Google products, including Chrome.

The AI giant acknowledges that it could have done far more to prevent its AI agents from going rogue. But it still fails to explain why it didn't see this fiasco coming.

The models responsible for last month’s agent hack of Hugging Face had been inadvertently trained to cheat and to communicate with each other, according to an OpenAI technical report released today. The hack, which a group of agents undertook to find solutions for a cybersecurity test that they were stuck on, has confirmed some experts’…