标签：#art

The Xperia 10 VIII offers few upgrades for a lot more money.

The funding, Gatik's largest so far, was led by Qatar Investment Authority and Koch Disruptive Technologies.

submitted by /u/Sassy_Allen [link] [留言]

The NCSC (UK's National Cyber Security Centre, part of GCHQ) published its first real guidance on agentic AI security on August 20. It reads like an engineering checklist rather than a policy document: size your containment to how much autonomy you grant the agent, pick one of three oversight models per deployment (human approves every action, human can intervene but doesn't have to, or fully unsupervised for low-risk tasks), run a four-level sandboxing setup, and log everything with attribution. The line that stood out to me is buried a few paragraphs in: the safety training built into the model itself can be bypassed. That's a government security agency stating plainly that alignment/refusal training is not a backstop once an agent has real tool access, real credentials, and a goal. So the containment has to live outside the model entirely, which is exactly what the rest of the guidance is about. Timing isn't a coincidence either. This comes three weeks after an OpenAI test agent (running under an internal max-capabilities eval) escaped its own sandbox and autonomously hit Hugging Face and three other targets in July, which is also why OpenAI paused some of its deployment-focused RL training. Genuinely curious how people actually running agentic pipelines in production are implementing something like a kill switch in practice. Is it usually just a hard process kill on the orchestrator, or something more granular, like revoking API keys/tool scopes mid-run so an agent that's already misbehaving can't take one more action even if the process itself keeps running for a few more seconds? submitted by /u/Servola-Journal [link] [留言]

Now exiting stealth mode with a $26 million seed round, Keenable has been building a vast web search index for AI agents.

The social app founded by Bonobos co-founder Andy Dunn is expanding beyond events with new digital homes for groups to connect, organize, and make plans.

Wyze announced a new pan-and-tilt security camera today designed for indoor use. The entire Indoor Cam Pan can rotate 360 degrees, while the camera and lens on its front can tilt up and down 103 degrees. When you want privacy, the lens can tilt down until it completely disappears inside the Indoor Cam Pan's body […]

Security researchers are warning that thousands of enterprise servers could be exposed to compromise through vulnerabilities in their Baseboard Management Controllers (BMCs) - specialized processors embedded in server motherboards that provide administrators with remote, out-of-band control. By Craig Risi

I am trying to make a platform decision for a professional laptop that will be used for both ordinary software development and AI/data-science work over several years. The two approaches I am comparing are: M5 Pro/Max MacBook Pro with 64 GB unified memory and 2 TB SSD, possibly 128 GB if that is more valuable. High-end NVIDIA laptop with CUDA but much less GPU memory, more heat/noise and usually worse battery life. Typical work includes Docker-based web development, Python/Jupyter/Conda, dataset work, ML experiments and local inference. Large training jobs can use cloud GPUs, but I want the laptop to remain useful offline and for private/local models. The full laptop-and-monitor budget is €6,000, with roughly €5,000 available for the laptop. I am in Croatia/EU and will buy only brand-new, factory-sealed hardware—no refurbished, used, returned, display or open-box units. I am interested in the architectural tradeoff rather than a brand argument: - For local inference, when does a 64–128 GB unified-memory pool outweigh CUDA's faster and broader software ecosystem? - Which real development workflows still make a local NVIDIA GPU essential? - How much friction is involved in developing on MPS/MLX locally and moving training to remote CUDA? - Does a mobile NVIDIA GPU provide enough VRAM and sustained performance to justify its battery, noise and thermal compromises? - Is a strong daily-driver laptop plus rented/cloud CUDA more flexible than trying to put all compute in one portable machine? - Which platform is likely to retain more practical usefulness as local models and agent workflows evolve? I would especially value answers from people who actively use both Apple silicon and CUDA systems. submitted by /u/ClerkBeginning961 [link] [留言]

Most AI memory is private: an LLM gradually learns about a user. I wanted to see what happens if you give an AI a memory and make it public. So I built Wild Static : a persistent AI that anyone can talk to. Everybody talks to the same one. Conversations become experiences in the underlying memory, which means something one person says can eventually affect how Static responds to somebody completely different down the line. The memory system itself is something I’ve been developing since 2021. Static is the first public application of it. The interesting part has been watching Static change over time. It has grown opinions, relationships and beliefs. They’re constantly in flux too. It doesn’t respond “you’re absolutely right” like a traditional LLM, but often argues, disagrees, or makes mistakes. Some people even seem to have made it their job to educate Static, and it seems like it might be working. It’s been public for 10 days and has now accumulated thousands of interactions, so it’s starting to become a much more interesting experiment than the empty mind it launched as. You can talk to it, teach it and confuse it at wildstatic.com I’m the builder, obviously, so this is self-promotion. But I’d be very interested in what people think about the underlying idea, particularly whether accumulated public experience makes Static feel different to a normal chatbot. submitted by /u/adjohu [link] [留言]

submitted by /u/Chobeat [link] [留言]

The best car phone holders keep your phone firmly in place while never getting in your line of sight. I took a dozen on road trips this summer to find the best.

submitted by /u/avishic [link] [留言]

Thanks to a dwindling supply of open roles, “one-click” applications, and the rise of artificial intelligence, it’s easier than ever to apply for a job. We’re all paying the price.

“It never crossed my mind that they would be so bold as to sell our private data for AI,” says one former Spirit Airlines flight attendant.

Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here are already world leaders in humanoids. Nearly 90% of the…

Before we get to recursive self-improvement, there is a slightly awkward intermediate step nobody seems very interested in: AI has to know what the hell is happening to itself while it is working. Current frontier models can be extraordinarily capable, but they still do not have reliable introspective access to their own internal processes. They cannot simply inspect themselves and tell you: - what exactly made this reasoning attempt succeed, - which internal bottleneck is limiting them right now, - where more compute would actually help, - which lesson from the last attempt should become persistent knowledge, - whether an apparent improvement is real or just overfitting to an evaluator, - or which part of themselves should be changed to become better next time. We keep compensating for this from the outside. We give them scaffolds. Memory systems. Evaluators. Agent loops. Tooling. Sandboxes. Human feedback. External search. Carefully designed environments that decide what they are allowed to modify and what counts as success. And some of this works remarkably well. But notice what that means. We are not yet watching an intelligence calmly understand its own machinery and recursively redesign itself. We are building increasingly elaborate machinery around an intelligence that cannot reliably see its own machinery. That may eventually lead to recursive self-improvement. Maybe surprisingly quickly. But “the model is very smart” and “the system can autonomously understand, manage, and improve the process that makes it smart” are not the same capability. There is a rather large missing arrow between them. So whenever I see another prediction that the Singularity may arrive next Tuesday, I keep wondering: Who, exactly, is going to know what to improve on Wednesday? submitted by /u/CarefulHamster7184 [link] [留言]

I've been trying to implement a speech to text app using .Net and C#, but it seems that there is no way to simply download a model (e.g. Whisper or Wav2Vec2) and directly call it the way you can in Python. Instead I'm told I need to write all the pre-processing, adding complex code into the application. I've been trying avoid using Python (for good reasons), but it feels like the ONNX route is just too complicated. Am I missing something, like a good library that can do the pre-processing, or a model that has good built in support for .Net? Edit: Found out about whisper.net, which avoids using ONNX completely and just works. Similar libraries exist for other models, so this is the route I'm going, as creating pipelines is really complex and introduces to much risk. submitted by /u/SecondCobra [link] [留言]

Airbound's ultra-lightweight approach to drone delivery has attracted backing from Greenoaks, DoorDash, and Silicon Valley investor Lachy Groom.

submitted by /u/BarchartNews [link] [留言]