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Keep your iPhone or Qi2 Android phone topped up with one of the best portable chargers.

For the last couple weeks, I've been in an extremely lucky position: I've been spending a lot of time playing games on Valve's Steam Machine. We gave the Steam Machine a 6, and I don't disagree with my colleague Sean Hollister's review. But even though I already own a PS5 and an Xbox Series X, […]

Talk to an AI Agent with a real face and voice, in real time Discussion | Link

Whether you’re going off-grid or safeguarding against blackouts, these beefy, WIRED-tested batteries can keep the lights on.

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React is a library, Next.js is a framework — here's what that actually means for your project, and how to choose based on SEO, scale, and team.

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency. Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models...

It’s hard to say if the pricey LG Micro RGB Evo television is worth shelling out for compared to its cheaper competitors, but you’ll probably enjoy its color-popping display.

For more than a decade, one question has loomed over the race to build autonomous vehicles: Are cameras alone enough to safely replace human drivers, or do truly driverless cars need additional, overlapping sensors like lidar and radar to navigate the world reliably? Tesla has bet billions of dollars that artificial intelligence and cameras are […]

A new book claims that Mystery, who teaches awkward men how to hit on women, had sex and smoked weed with an AI chatbot named Miss Shira Always.

Scammers are hijacking government websites to upload ads for “leaked” OnlyFans content. Thousands of copyright complaints from adult creators are helping people avoid malicious links.

OpenAI Academy and the Walton Family Foundation are bringing hands-on AI Skills Jams to help K–12 educators build practical AI skills for the classroom.

Burst water mains. Evacuated hospitals. In a closed-door simulation, insurers played out their response to a mass disruption by China’s Volt Typhoon hackers—and found a nightmare scenario.

Cryptographic Watermarking for LLM Outputs with resk-mark Links: PyPI: https://pypi.org/project/reskmark GitHub: https://github.com/Resk-Security/resk-mark Web: https://resk.fr __ __ ________ _____/ /__ ____ ___ ____ ______/ /__ / ___/ _ \/ ___/ //_/_____/ __ `__ \/ __ `/ ___/ //_/ / / / __(__ ) ,< /_____/ / / / / / /_/ / / / ,< /_/ \___/____/_/|_| /_/ /_/ /_/\__,_/_/ /_/|_| The Provenance Problem Every company deploying LLMs in production faces the same question: once a model generates text, how do you prove it came from your system? Prompts like "say you are an AI" are trivially removable. Post-hoc detectors are unreliable and adversarial. And once text leaves your system — forwarded, copied, pasted into a ticket — you have zero visibility. resk-mark solves this by embedding a cryptographic watermark directly into the token generation process. The output reads naturally, but carries a verifiable signature that survives rewording and truncation. How It Works resk-mark hooks into the language model's sampling process. Before generation, the caller provides a secret key. During sampling, the library biases the probability distribution toward tokens that encode that key's signature: from reskmark import WatermarkEncoder , verify encoder = WatermarkEncoder ( secret_key = " your-key-here " ) model = AutoModelForCausalLM . from_pretrained ( " mistralai/Mistral-7B " ) # Wrap the generate call output = encoder . generate ( model , " Explain the concept of zero-knowledge proofs. " , max_length = 200 , ) print ( output ) # "Zero-knowledge proofs are a cryptographic method where..." # Reads naturally - watermark is invisible # Later - verify provenance is_authentic , confidence = verify ( output , public_key = " corresponding-pub-key " ) print ( f " Authentic: { is_authentic } , confidence: { confidence : . 2 f } " ) Key Properties Invisible — the watermark does not change the meaning, grammar, or fluency of the output Robust — survives copy, paste, truncation, and light rewo