Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. Google's new AI tool just proved Signal encryption really works - here's how With the aim of securing other vital systems, Google DeepMind plans to open-source its AlphaProof Code. Radhika Rajkumar is a senior editor at ZDNET based in New York City. Researchers formally verified that a portion of Signal's encryption code works. AlphaProof Code is a step toward stronger security verification.

Mali Osmelak never imagined that selling clothes on Vinted would end with a court claim to recover almost £6,000. She started trading on the platform to earn an income while managing Crohn's disease and caring for her four-year-old daughter. But when she became a professional seller in March, she was unable to complete Vinted's verification process, leaving thousands of pounds inaccessible in her Vinted wallet. After several weeks of trying to resolve the issue, Mali took legal action. Vinted says sellers may be asked to complete verification checks to comply with legal and regulatory requirements and 98% of transactions are completed smoothly without needing help from its support team.

If video killed the radio star, then AI slop returned the favor. At least, that is what Josh Cowell, founder of camera maker CAIM1, will tell you. Short for Counter Artificial Intelligence Machine 1, the startup is working to reestablish our collective faith in the validity of images through its latest prototype: a camera that embeds cryptographic proof of a photo's authenticity directly into the image. As Cowell described in a blog post by the startup's silicon chip provider, Tropic Square, the traditional photograph is dead, and designing a secure camera to replace it is an exercise in healthy paranoia. When you take a photo using its prototype, CAIM1's internal chip creates a unique silicon fingerprint that certifies when, where, and with which camera the photo was taken.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Why are people attacking food delivery robots? Apple's first touchscreen MacBook comes at a price Fake party invite scam: Can you trust a friend's reply? Identity thieves don't need your password anymore; here are 6 new account takeover threats Fox News AI Newsletter: Don't want AI on your phone? Tech executive warns sweeping bans could threaten'biggest economic opportunity of our generation' Jim Clemente delves into the chance that Savannah Guthrie's fame played role in mom's kidnapping Veteran electrician says data center boom is creating a'life-changing' path to six-figure pay'Gutfeld!': AI offers different answers to race preference question FBI is taking an'offensive and defensive approach' to cybercrime: Director Kash Patel'The Five': Would you read your partner's ChatGPT messages?

The'Pain in the Ass' Math That Could Save Vibe Coding From Itself A new startup called & Truth is joining the race to automate formal verification, a painstaking process that uses math to prove software works as intended. Stanford researcher Chuyue Sun and her fellow computer scientists developed an AI tool that completed an engineering project in less than half a day for under $500, compared to the five months it took three highly skilled experts to do the same work manually. The dramatic reduction in time and cost, which Sun reported in a recent paper, is among the early results that are awakening an obscure field of coding and mathematics known as formal verification. For decades, developers have envisioned using math to prove that their software is bug-free. This process is long and laborious, requiring engineers to write precise statements about the goals of their software and convert their code into formulas that can be validated.

I tested the best dating apps of 2026, including Hinge, Tinder, and Hily. Here’s what I'd use for relationships, hookups, and free chats.

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Getting Technical with Mashable Mashable Selects Gift Ideas For Everyone On Your List Say More Versus Trending Now In My Bag Mashable @Home Top creators, ranked Creator Playbook All Series Spotify's QR code checks could complicate your shared subscription -- and make listening more expensive. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan. She also runs Fan Mail, a weekly pop-culture newsletter.

To keep scammers at bay, dating apps are embracing biometric scanning tools and “verified human” badges. These moves are sparking concerns about surveillance and privacy.

When you purchase through links in our articles, we may earn a small commission. The actual teen protections are more complicated. I'm not a parent, but I'm looking at the Meta settlement wondering if our kids are any safer. If you haven't heard already, Meta agreed last Wednesday to settle a lawsuit over Facebook and Instagram's affect on children. Brought by 47 states, Washington D.C., and several U.S. territories, the suit alleged that Meta purposely made its platforms addictive to kids, ultimately causing negative mental health outcomes like anxiety, depression, and even suicide.

Florent Delgrange won the Best Blue Sky Paper Award at AAMAS 2026 for his work Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments . We caught up with him to find out more about his vision for agent learning. What is the topic of your Blue Sky Ideas paper and why is it an interesting area for study? My Blue Sky Ideas paper asks a simple but difficult question: how can an autonomous agent keep learning as its world changes without quietly losing the guarantees that made its behavior trustworthy? Reinforcement learning and formal methods address complementary parts of this problem.