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hubie writes:

When it comes to recording, tracking, and storing data, the concept of "permanence" can be a lot trickier than you'd expect:

More and more, our data—private photos, work documents, confidential information, and so on—are migrating to digital clouds and platforms. In a more recent and perhaps controversial turn of events, some gaming firms are pushing for digital downloads in lieu of physical CDs.

One recurring argument against digital options has been that it's not as "permanent" as their physical counterparts. After all, aren't physical storage systems safer from the whims of a tech firm wiping your cloud storage—or, even worse, forcing you to choose a subscription plan?

But CDs break. Books burn. USBs go defunct. Personally, I—a stickler for physical media—have a very bad track record of breaking my game chips and scratching my movie discs. There are real challenges in dealing with physical storage systems, too, and big players like Microsoft are investing significant resources to overcome the inevitable decay caused by, well, the passage of time.

[...] The following responses may have been lightly edited for length and clarity.

Mohiuddin Ahmed, Computer scientist, Adelaide University, Australia.

Data storage is a contextual matter. Given the transient nature of everything around us, permanent data storage is some sort of a myth at this stage. That said, multiple copies of data and moving them to a new storage every couple of years can be thought of as a practical solution toward permanent data storage.

Francesca Musiani, Research director, Center for Internet and Society at the National Center for Scientific Research, France.

I find the idea of "permanent" data storage quite elusive, not only in our digital "era." Every medium we have ever invented eventually fails, from stone inscriptions that erode to paper burning, CDs' "disc rot" and solid-state drives that gradually lose stored charge. Further, even if a storage medium could survive for centuries, the devices and software needed to read it may not; a perfectly preserved floppy disk is of little use if no one can build or maintain a compatible drive. In this sense, data loss is a techno-historical problem, I would say: preserving information requires preserving the means of interpreting it.

[...]

Jack Cushman, Director, Library Innovation Lab at Harvard Law School; creator of the Public Data Project.

I know of one data storage form that can be permanent: a library. Or rather, a world full of libraries. Libraries aren't perfect storage devices (nothing is), but they are fiercely committed to self-repair. A good library is a collection of people, technologies, and practices that pass an obligation—to remember the things we must remember—from the last generation to the next.

Libraries, rather than better hard drives, are what solve correlated failure, cataloged in the LOCKSS (Lots Of Copies Keep Stuff Safe) threat model. A correlated failure is when you make really great, durable copies of the data you care about, and then they all get hit by an asteroid. Shouldn't have had them all in one physical location. Or a hacker marks them all for deletion (they were all behind one sysadmin), or a government orders them all destroyed (all in one regulatory regime), or they all rot at the same time (all from one flawed batch), or the money to store them dries up (all on one funding source).

On a long enough timeline any copy will break. You need multiple copies with different vulnerabilities so they don't all break at the same time. Then you need to repair the broken ones. A worldwide network of libraries does the repair work, whether the objects are 30 medieval Magna Carta manuscripts or 300,000 government datasets.

[...]

owl writes:

https://ludens.cl/photo/spectra/spectra.html

The biggest lie in color photography is that you can accurately represent the colors of objects by simply recording the amount of red, green and blue in them. This technique - the only one in current mainstream use - gives good results only when the spectral sensitivity curves of the camera precisely match those of the human eye, and when the spectrum of the light used to make the photo is perfectly smooth, and no different kind of light will ever be used!

It's clear that the first of these requirements is hard enough to meet, and that the second one is, simply and plainly, never true.

And that's why photographers are always battling to get the right colors - and never do get them!

Not only in photography is this matter an important one. In daily life it is, too. Lots of electronic technicians hate that stupid problem of not being able to correctly read resistors. It happens that old-style resistors, and some other parts too, are labelled with color bands or dots, instead of numbers. Under some lighting conditions it can be hard to tell a red from an orange, or a green from a blue. This leads to the wrong resistors being installed in equipment, and thus more troubleshooting work.

Original Submission

hubie writes:

Firefox says it will keep supporting uBlock Origin even as Microsoft Edge and other Chromium browsers phase it out under Manifest V3:

Firefox recently announced via Bluesky post: "Our support for uBlock Origin isn't going anywhere." The moment comes in response to news that Microsoft Edge is soon going to lock out uBlock Origin and other ad-blocking extensions that run on Manifest V2 architecture.

Once Microsoft Edge moves to Manifest V3, ad-blocking extensions won't have access to the functions needed to properly identify and block ads that occur while browsing websites and watching videos.

Microsoft's move isn't surprising, as Edge is based on Chromium, the open-source browser engine that powers most web browsers today, including Opera, Brave, Vivaldi, and Samsung Browser. Google initiated the migration from Manifest V2 to V3 in Chrome/Chromium, and Microsoft Edge is now following Google's lead.

But Firefox is one of the few web browsers remaining that isn't based on Chromium, and it's now the only major browser to still support uBlock Origin. Neither Safari nor DuckDuckGo—the two other major non-Chromium browsers out there—support uBlock Origin.

For die-hard uBlock Origin fans, Firefox appears to be the only browser left without compromises. With any other browser, you'll need to settle for uBlock Origin Lite (with fewer features and less ad-blocking success) or whatever built-in ad-blocking feature comes with the browser.

Original Submission

Arthur T Knackerbracket writes:

Cloudflare built an AI agent workspace for its employees. Now it's open source:

Cloudflare has open-sourced its Cloudflare OS platform, which it first developed as an internal workspace for employees to build apps using AI agents—including people who are not software developers or engineers. The company also touts a security framework designed to reduce the risk of employee vibe-coding sessions creating serious security flaws or leading to data breaches.

The tech company spent several months building and internally testing Cloudflare OS, which allows employees to describe workflows in natural language so that an AI agent can code them into applications. In an August 5 blog post announcing the open source version's availability on GitHub, the company claims thousands of Cloudflare employees use the platform on a daily basis to "create documents and slides, automate repeatable tasks, and build small apps to visualize data and help them do their work."

"This is a full-on personal app vibe coding platform, in which the sandbox is so secure that you can pretty much go wild—the AI cannot introduce a significant security bug," said Kenton Varda, principal engineer at Cloudflare, in a post on the social media platform X. "We believe a company's security team can feel comfortable giving non-technical users permission to vibe code and then sleep soundly at night."

The security model relies on creating fine-grained app instances so that a document editor app would run each document as a separate instance in a separate sandbox, Varda explained. The Cloudflare OS platform manages who has permission to access each instance, and each individual runs their own copy of the code that they can freely modify.

[...] Cloudflare OS can work with just about any AI model and allows organizations to select the most suitable model for the job at hand. "Not every user needs access to the max thinking mode of the latest frontier lab model," said Sam Rhea, chief information officer at Cloudflare, in a separate blog post. "And we do not need team members spending $20 to summarize their email inbox every hour."

looorg writes:

Owners of Petlibro pet feeders have "service issue" that prevents feeding of the pets. If you can't even bother to feed your own pet? Why do you have them? Also if we can't even depend on the machines to feed pets how are we depending on them to do other more important things. But why do you need cloud access to set a schedule to feed a pet? Or that is to say to open a tray so the pet in question can access the food. As it was when it was not opening it was mocking or taunting the pet with food it could not access.

Pets should now be on their normal feeding schedule again ... Perhaps the service issue was just there to put some of the furries on a diet.

Petlibro says it's working to resolve a 'service issue' that's causing its smart feeders not to dispense food on schedule.

Smart pet-feeder company Petlibro suffered an outage on Tuesday, giving users a harsh reminder of the risks of relying on smart devices.

https://www.theverge.com/tech/979295/petlibro-outage-smart-pet-feeders
https://arstechnica.com/gadgets/2026/08/pet-owners-say-smart-pet-feeder-outage-led-to-furry-ones-going-unfed/

Original Submission

JoeMerchant writes:

Meta's recent open-weight release of Muse Glimmer 30B has re-ignited the debate over whether locally hosted models can replace subscription-based cloud APIs like Claude 3.5 Sonnet, GPT-4o, and Gemini 2.0 Pro. But what does it actually take in terms of silicon, power draw, and real-world performance to run Meta's latest agentic model at best capacity?

To run Muse Glimmer 30B at "best capacity"—meaning unquantized (FP16) or high-precision 8-bit (FP8/Q8_0) with its full 131,072-token context window loaded into VRAM alongside the DFlash speculative decoder—hardware requirements land between 48 GB and 64 GB of VRAM. A dual NVIDIA RTX 5090 workstation (~64 GB VRAM) requires an initial hardware investment of roughly $4,500–$6,000, while an Apple Mac Studio (128 GB Unified Memory) costs around $3,800–$4,800.

The operational energy divergence between PC rigs and Apple Silicon is substantial:

• Dual-NVIDIA Workstation (750W Peak): Under heavy 8-hour daily agent workloads, power costs average ~$35/month ($420/year). Continuous 24/7 background operation increases power costs to ~$86/month ($1,036/year).
• Apple Silicon Mac Studio (130W Peak): Under the same 8-hour daily workload, power costs average ~$5.90/month ($71/year), or ~$15/month for continuous 24/7 agent loops—yielding an 80%+ energy reduction over PC GPU setups.

In terms of capability, Muse Glimmer 30B holds its ground surprisingly well against mega-parameter cloud models:

*SPOILER* (click to show) *SPOILER* (click to hide)

                        Benchmark / Workload
                        Muse Glimmer 30B (Local)
                        Claude 3.5 Sonnet (Cloud)
                        GPT-4o / o3-mini (Cloud)

                        SWE-bench Verified (Coding)
                        76.0
                        49.0
                        70.5 (o3-mini)

                        MCP Atlas (Tool Use)
                        75.5
                        81.2
                        80.0

                        DeepSearch QA (Research)
                        74.6
                        82.0
                        84.1

                        AIME 2026 (Math/Logic)
                        94.7
                        78.3
                        91.0

The Verdict: While frontier cloud models like Claude 3.5 Sonnet and Gemini 2.0 Pro maintain a clear lead in complex architectural design and massive context capacity (up to 2M tokens), Muse Glimmer 30B dominates in privacy, zero token costs, and low-latency local agent execution. For software developers and privacy-conscious users running 24/7 personal assistants, local hardware offers a compelling alternative to continuous cloud API fees.

Model weights and GGUF quantizations are currently available via Hugging Face under the Apache 2.0 license.

------

I'd call 3.5 Sonnet a bit of a strawman, over a year old, but still an impressive target to match locally, privately, at those prices.

Original Submission

Arthur T Knackerbracket writes:

AI-assisted research only used 20 prompts to find an exploit to hack hundreds of millions of people:

Critical 'Zoomsday' Flaw Enables Total Device Takeover During Zoom Calls — AI-Assisted Research Only Used 20 Prompts To Find An Exploit To Hack Hundreds Of Millions Of People.

The team claims it cooked the exploit with merely 20 prompts to an AI agent. The exploitable area is substantial, as recent estimates pin Zoom's monthly active users at around 220 million and an estimated 56% of the global conferencing market share.

With the exploit, the attacker was able to get full remote code execution, meaning they could effectively control the user's computer and their data — invisibly, to boot. Zoom isn't an application that runs with administrator privileges, so kernel-level rootkits are off the menu, but once you have the user's data, it's not like you need much else. Plus, it's easy to gain exploit persistence any number of other ways.

A.Security says a small team developed this exploit with a mere 20 prompts to an AI agent — pointing out how easy it was to come up with a nation-state-class vulnerability with meager resources. While the majority of AI-assisted vulnerability research focuses on open-source software or applications with published communications protocols or file formats, Zoom is fully proprietary, and it was still easily cracked open.

The firm further noted that "the model requiring elite teams, months of effort, and weapons-grade budgets has collapsed," and that "the barrier that kept these weapons scarce has collapsed, and it will not come back" — basically repeating what every security researcher has been yelling from the top of their lungs for the past year or so.

The vulnerability itself is, rather unsurprisingly, a buffer overrun: the program fails to check that an input is the right size, so you can push more data than it expects and overwrite part of the following memory with code that will be executed.

First, the scientists decompiled the Android package and asked an AI agent to rank the potential attack surfaces to relatively little success. They then turned their attention to the communications protocol. They found that the code library handling annotations received each object (rectangles, text, etc.) in serialized form, with count fields telling the recipient how much data to read next.

Crucially, they found that the code handling these reads didn't have a boundary check for maximum size, meaning one could simply lie about it and send a chunk of data that's too large and padded with exploit code at the end, as Norman Stansfield would say, bin-go!

Original Submission

An Anonymous Coward writes:

https://science.nasa.gov/missions/chandra/nasa-telescopes-create-colorful-craft-from-nearby-nebula/:

Like a collage made of layered sheets of colored cellophane, a vibrant new image layers observations of a famous star-forming nebula from NASA space telescopes. The resulting cosmic "craft" reveals new details about the star formation region known as 30 Doradus, or the Tarantula Nebula.

Located in the Large Magellanic Cloud, a small neighbor galaxy to the Milky Way about 160,000 light-years from Earth, the Tarantula has thousands of young stars embedded in a vibrant honeycomb-like structure of gas and dust.

NASA Tarantula image

The new composite image contains X-rays from NASA's Chandra X-ray Observatory, which has repeatedly observed the Tarantula Nebula over the course of its mission, in the layer that appears in blue. The X-ray data reveals gas blown away by winds from the surfaces of young, massive stars and heated to millions of degrees by shock waves, like sonic booms from supersonic jets.

The red represents infrared data from NASA's James Webb Space Telescope showing thousands of young stars, plus swaths of cool dust that will provide the ingredients to form new stars and planets. Optical data in the green layer from NASA's Hubble Space Telescope uncovers hydrogen gas that is warmer than seen with Webb, as well as some individual stars through the nebula.

The composite image shows the full Hubble and Webb images of this region, as well as a large section of the Chandra image, all recently published in a research paper in the Astrophysical Journal. In some regions the blue Chandra layer stands alone, and in others it combines with either the red Webb data or the green Hubble data. In the middle region all three images overlap to provide a holistic view in red, orange, yellow, green, and blue.

Arizona State University is introducing a degree in Content Creation. This is not an April fool's joke or a hoax.

The degree will teach the craft of influencing and online content creation which will include lessons on creating powerful content across video, podcasts, analytics, and social media strategy.

Online comments in response to this announceent have included "F**k. This. Timeline." and "Just imagine explaining to someone that your homework is 'go viral'".

Classes start this October.

Original Submission

Arthur T Knackerbracket writes:

Anthropic and OpenAI are racing to scale up while reducing dependence on Nvidia:

Anthropic is hiring a "custom silicon team" to design chips on which to run its models, the company has revealed.

Yesterday, Business Insider noticed a job listing for a senior engineer with experience shipping semiconductor designs. (You can see listings for a silicon engineer and a technical program manager, silicon on Anthropic's job board right now.) A spokesperson for Anthropic then confirmed the plans to both Business Insider and TechCrunch.

The spokesperson clarified that Anthropic will still take a "multi-chip approach," with plans to use hardware from other companies alongside its own designs as it continues to scale up.

This is confirmation of a rumor that has been circulating for a little bit; The Information previously reported that Anthropic was considering working with Samsung as a hardware manufacturing partner.

Anthropic is not alone in walking this path. Its competitor, OpenAI, recently announced a new custom chip called Jalapeño designed for large language model inference in data centers. OpenAI partnered with Broadcom to develop the chip. Google has been running its models on its own hardware for a while; Meta has also designed and deployed its own chips, and Mistral is reportedly looking into doing the same.

There are a few reasons AI providers are doing this. First, much of the industry is heavily reliant on Nvidia for the hardware the companies' models run on, and Nvidia's continued leverage there is a potential strategic vulnerability, especially as AI companies operate in an environment where compute infrastructure is highly competitive as demand continues to outstrip current capacity.

Second, designing chips for specific models and vice versa could lead to better performance. So, for example, if OpenAI can reap the rewards of that vertical integration, you can bet Anthropic and other frontier model providers will want that advantage as well.

To that point, Anthropic says its teams will co-design new hardware and models side by side. It has co-designed certain hardware with partners before, but the plan is now to bring more silicon expertise inside Anthropic itself.

Anthropic may also hope this could help its frontier models get some extra competitive edge as software developers and other users begin exploring running cheaper, smaller, or open-weight models on their own hardware or on edge devices.

However, since Anthropic is still in the process of hiring key team members, it will be some time before either the company or its users see any benefits.

Original Submission

Arthur T Knackerbracket writes:

https://www.engadget.com/2236104/cbp-officers-are-misusing-surveillance-tech/

US Customs and Border Protection (CBP) agents have reportedly misused electronic databases to spy on family members, try to get dates and even provide intelligence to suspected drug traffickers, according to freedom of information (FOIA) files seen by Wired. Officers allegedly abused databases that can draw from sources like license plate readers, facial recognition and smartphone searches to violate the privacy of numerous individuals.

The FOIA files, ranging from 2009 to 20222, show a number of troubling incidents. A CBP officer is alleged to have used a government database to contact a flight attendant, and another was accused of using data from trusted-traveler applications to ask people out. One employee provided border-crossing data to someone involved in a divorce, and another abused internal policies by tracking coworkers cellphones with ad-tech-derived location data.

Of 300 incidents tracked by Wired, 138 were referred to CBP management and 78 assigned to criminal investigators, while 43 others weren't investigated. Many of the claims were handled internally or put under the category of minor misconduct, but 21 were withheld due to potential law-enforcement proceedings that suggest criminal misconduct were involved.

The report is noteworthy because the DHS has more access to private citizens' data than ever before and has built one of the largest surveillance systems in the world, Wired noted. Its sources include records on immigration arrests, border screening records, naturalization applications and the SENTRI trusted-traveler program. The agency also has access to Palantir's ICM and FALCON systems, along with the DHS Mobile Fortify facial-recognition app deployed on agents phones.

CBP told Wired that it takes misconduct allegations seriously and works to "uphold the rule of law and hold ourselves accountable... [and takes] appropriate investigatory, corrective, and disciplinary action."

Original Submission

Arthur T Knackerbracket writes:

https://arstechnica.com/tech-policy/2026/08/claudes-new-scarlet-letter-watermark-is-invisible-for-now/

Anthropic has revealed that it will soon watermark content that is processed (not just generated!) by any of its models. In a support article, Anthropic explained that it was rolling out machine-readable watermarks to comply with the European Union's AI Act, which requires all AI system providers to watermark AI-generated or manipulated audio, image, text, and video outputs. The law applies to any AI model released after August 2 and provides a grace period until December 2026 for providers to update previously released models.

Anthropic confirmed that moving forward, all new models offered globally—not just in the EU—will mark AI-generated content "from day one." Text outputs will "carry embedded watermarks," invisible to the user, and other "generated files will include digitally signed provenance metadata where supported," Anthropic said.

Notably, Anthropic is deploying a "nuke it from orbit" approach, applying the watermarks to all processed content where supported, even though the EU does not require it for cases where an AI system performs "an assistive function for standard editing" (the guidance's own example is grammar correction), or where it doesn't "substantially alter" the user's text or its meaning.

A watermark applied at the model level can't tell wholesale generation from a comma fix, so Claude may end up stamping exactly the content the law was written to leave alone. How thoroughly it truly watermarks will not be known until Anthropic releases a detection tool that can be tested. The company said that it plans to eventually share details about how to detect marks in order to offer technical support that the EU's law requires.

Anthropic also noted that the watermarks won't work on "some platforms or features" that don't support them. For non-text content, Anthropic will use the C2PA metadata approach to record provenance.

The approach described by the EU and implemented by Anthropic is unfortunately trivially easy for bad actors to bypass, while potentially punishing users who trust the system to accurately label their outputs. Text watermarks work by biasing the model's word choices in a pattern spread across the entire document, only detectable in aggregate by the right tool. The catch is that "invisible" can also mean the model occasionally trades the best word for a slightly worse one, just to keep the signal intact.

Anthropic noted that those marks "will travel with the text when it's copied and pasted elsewhere, and may persist through some editing." But if watermarked text is pasted into another chatbot system that edits the text, the watermark could be destroyed. With image and video content, screenshotting/recording or using any decent metadata editing tool will suffice to remove this information, too. And once Anthropic tells the world how to identify these watermarks, building a system to remove them would be trivial.

Further, the potential for misinterpretation seems high; the watermark is not particularly informative. Anthropic explained that a "detected mark provides a signal that content was processed by Claude, but is not fully conclusive." The only real message the mark sends is that "the content may have been processed by Claude," Anthropic said, and the mark may even appear on content that was not generated by Claude.

On top of this, you have the general public, who may not grasp the difference between processed text and wholly generated text. If the system watermarks human-authored text simply because it was edited in a workflow that touches Claude, suddenly it carries the same denotation as wholly generated text does. And all of this in a system where the "lack of a detected mark doesn't mean the content wasn't AI-generated or processed," Anthropic said.

To its credit, Anthropic acknowledges that it may be marking some content that the AI Act does not require to be labeled: "People often use Claude to proofread, translate, summarize, or convert files. The output can carry a Claude mark even if the underlying ideas, text, or data originated from another source." So, despite being explicitly exempted by the law, Claude will mark any editing work done on original writing.

owl writes:

https://www.techdirt.com/2026/08/12/ninth-circuit-rewrites-section-230-to-remove-the-part-that-actually-mattered/

For all the talk of "reforming" or "repealing" Section 230 in Congress, the fact is that the courts over the past three or four years have effectively chipped away so steadily at the law that it's lost a significant chunk of its usefulness. The latest comes from the Ninth Circuit, which ruled earlier this week that Section 230 is not, in fact, an immunity from lawsuit, but merely a defense against liability. This may sound like a procedural technicality — and, indeed, the coverage of this case from the likes of Reuters covers it as a boring procedural story — but it's a huge deal.

To get there, the panel had to rewrite the history of Section 230 and wave off a whole stack of its own prior rulings as either sloppy word choice or mere dicta.

To understand why this ruling is such a big deal, you first have to understand Section 230's true benefit: it would get bogus cases tossed at the earliest moment. This is the entire key to why Section 230 is important. The point of Section 230 is to put the liability on the party actually violating the law — which would be the creator of the content, and not the intermediary tool they use to host/distribute that content. But the mechanism used to protect speech is that it gets the cases against intermediaries dismissed very early (aggrieved parties can still sue the actual speakers).

Original Submission

looorg writes:

The future is for everyone and it will all just be great and awesome. No problems. That is our AI fueled future according to Zuckerberg.

On Monday, Mark Zuckerberg published a 6,500-word manifesto about personal AI, largely about the possibilities for the "personal superintelligence" systems Meta AI is building. The ideas in the post aren't totally new. A version of the essay ran in The Wall Street Journal two weeks ago, and he's talked about them on Meta earnings calls before. But this is probably the most detailed version he's shared.

What I'm wondering, I might have missed it, is if he wrote it himself or he had his AI write it for him.

https://www.meta.com/thefutureisforeveryone/
https://www.politico.com/news/2026/08/10/mark-zuckerberg-ai-power-01030904
https://www.theguardian.com/technology/2026/aug/10/mark-zuckerberg-superintelligent-ai-essay-meta
https://techcrunch.com/2026/08/10/mark-zuckerbergs-ai-manifesto-is-exactly-why-people-dont-like-ai/
https://www.theverge.com/ai-artificial-intelligence/977623/mark-zuckerberg-ai-manifesto-dim-vision

Original Submission

AnonTechie writes:

Mini human brains are being grown in labs all over the world. Soon, they could outthink neural networks.

I'm going to let you in on a secret. Every cell in your body has the potential to get smarter. I don't mean this metaphorically, or in a "body keeps the score" kind of way. I mean that if lab-coated biologists took a sample of your skin and very carefully manipulated the cells inside it, they could actually make a brain. They do it all the time.

Recent developments in organoid intelligence, specifically the cultivation of "mini human brains" in laboratories, signal a potential paradigm shift in computing and artificial intelligence research. This burgeoning field posits a biological alternative to conventional silicon-based AI, with implications for enhanced cognitive capabilities and novel approaches to information processing.

The Wired article highlights the global proliferation of research into organoid intelligence, focusing on the development of "mini human brains" designed to potentially surpass neural networks in computational capacity. These biological systems, grown from stem cells, offer a new frontier for understanding and replicating complex cognitive functions. Unlike digital artificial intelligence, which operates on algorithms and datasets within electronic circuits, organoid intelligence leverages the inherent parallel processing and adaptive learning capabilities of biological neurons. This approach could unlock efficiencies and problem-solving modalities currently unattainable by even the most advanced conventional AI systems. The ability to model aspects of human cognition organically presents both profound scientific opportunities and significant ethical considerations that will necessitate careful navigation as the technology matures.

[Source]: WIRED (Subscriber Only)

[Covered By]: LinkedIn

Original Submission