标签：#AR

submitted by /u/NISMO1968 [link] [留言]

I’ve rounded up the best smart speakers that let you talk to Alexa, from the popular Echo Dot to the newest Echo Studio.

In the previous article, we defined the forward pass for our neural network. Now, we will provide...

The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…

Find the real deals on Amazon Prime Day this June.

Pacific Fusion's sub-scale prototype delivered enormous amounts of power in a flash, setting the company up for its demonstration power plant.

Anthropic filed a confidential S-1 with the SEC this week, moving toward a public listing that will put disclosure obligations and investor return expectations directly in tension with its safety-first positioning. The IPO filing lands as GitHub Copilot ends flat-rate billing and switches to metered consumption, meaning teams with heavy usage face immediate cost spikes with no grace period to audit seat activity. OpenAI's frontier models and Codex are now available directly on AWS , which changes vendor-lock assumptions for inference pipelines and removes the proxy layers some teams were routing around. These two moves together suggest the "get developers hooked, then price for real" phase is now active across the stack. The security picture is worse. A researcher documented a Meta AI social-engineering exploit that handed attackers access to high-profile Instagram accounts by manipulating the agent through its account-management tool calls. No sophisticated jailbreak required. Any agent with write permissions to external accounts is now a confirmed social-engineering surface, and the Meta incident is the clearest public proof of that so far. Separately, malicious npm packages reached Red Hat Cloud Services repositories and were downloaded at scale, which means JS dependency audits for cloud-native stacks need an immediate re-run against known-bad versions, not a scheduled one. On the hardware side, Intel's Crescent Island GPU ships with up to 480GB VRAM , which revises local inference capacity planning for large MoE models in ways that weren't on most teams' roadmaps six months ago. Alphabet announced an $80 billion equity raise for AI infrastructure , which will tighten GPU allocation queues and data center procurement timelines across all cloud providers regardless of whether you're an Alphabet customer. The pattern across all of this: monetization is accelerating faster than the trust infrastructure required to support the attack surface already in production. A

The air purifier giant’s P50 pedestal fan is whisper-quiet and surprisingly versatile, even if its built-in voice assistant feels stuck in beta.

Zak Brown spent a decade racing before joining the business side of Formula One. He talks to WIRED about rebuilding a legendary brand, obsessive fans, and the pull of the driver’s seat.

If you work a white collar job, you’re probably scared of AI replacing you. AI started at the desk — data entry, customer service, software. Now its stepping onto the factory floor: Amazon robots moving inventory, Figure bots handling BMW parts, Tesla building Optimus for repetitive labor, and warehouses being automated. But at the end of the day, AI is a technology. We cannot stop it any more than we could stop electricity or the assembly line. The problem is not that machines are becoming powerful. The problem is the economic machine around it. Let’s face it: Capitalism doesn’t have the ability to support this kind of technology. Capitalism was built for a world of scarcity, where human labor was necessary and wages gave people access to goods. But as AI advances exponentially, it can produce more with fewer workers, while capitalism still distributes wealth through jobs it is actively eliminating. The result is abundance trapped behind an archaic wage system. I believe that we NEED to get governments and major tech companies to start seriously planning for a universal basic income funded by AI-driven productivity. As automation replaces more human labor over the coming decades, UBI will become essential to prevent mass instability and ensure that the wealth created by AI supports society as a whole, not just the companies that own it. We already know the wealth gap is too wide. If we don’t start addressing AI-driven inequality now, that divide will grow exponentially as more labor is automated and more wealth concentrates at the top. Without a plan to distribute the gains from AI, we risk mass instability and eventual economic collapse. Capitalism built the machine that could end scarcity, but not the system that could distribute its output. It’s time that we, as a global society, start thinking about phasing out that old machine. submitted by /u/SuddenEducation442 [link] [留言]

Amazon is a murky mess of ads, unknown sellers, misleading sales, and specious information. Stay safe while shopping on Prime Day and beyond with these tips and tricks.

presenting a poster there, and have registration covered. but they are placing me on waitlist for travel funds. As my travel depends on whether I get the travel grant, I need to get this off of my mind, either invite me or just say no. I'm waiting forever for this, more wait again? should i ask for a decision, or what to do. submitted by /u/Active-Tip3130 [link] [留言]

You don’t need expensive software for basic PDF tasks. In fact, all you need is a handful of free web-based apps.

Donald Trump killed an executive order to regulate AI. Now, administration officials and AI executives are trying to figure out if there’s anything left to piece back together.

submitted by /u/lilubba [link] [留言]

Hello this might be weird but I am doing a study on society's view on AI as a school project. Therefore I am asking all kinds of communities and trying to get a very wide audience. This is clearly an AI sentric sub so hopefully his is relavent? I would be very happy if any of you would like to be a part of it! submitted by /u/Timely_Special_5011 [link] [留言]

In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution. By Aaditya Chauhan

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here. From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to…

This is what production AI costs when nobody's watching. A 4-person team posted their Anthropic invoice. Agentic systems don't make just one API call per task. They read context, plan steps, call tools, hit errors, retry. Each step is a separate call to Opus at $25 per million output tokens. One user instruction can trigger 20+ calls before it's done. A lot of engineers have no idea what a single task costs end-to-end. - They don't know which prompts trigger the longest loops - They don't know how many silent retries are happening in the background - They can't tell which tasks could run on a smaller model without losing quality Frontier models are genuinely impressive. But agentic systems don't make one call.. they make dozens. Every single day. And most teams aren't watching the meter. If you're running agentic workloads in production, start tracking what individual tasks actually cost before your next invoice does it for you. submitted by /u/aipriyank [link] [留言]

I built CVE-Bench: 20 real-world CVEs across 18 Python projects (Pillow, GitPython, yt-dlp, urllib3, others), 5 frontier models, 3 prompt conditions, 300 runs total. Each agent runs in a sandboxed container and is scored against a hidden test_security.py derived from the maintainer's own fix. Binary pass/fail (a 90%-patched vulnerability is still a vulnerability). To better understand failure modes, I've tested three prompt conditions : advisory (full GHSA report), diagnose (exploit description only, no file or function), and locate (exact file and function, no description of the flaw). The three conditions test meaningfully different things. A model that does well on advisory but drops on diagnose can’t translate a behavioral description into a location in the codebase. A model that holds up on locate is recognizing dangerous code on its own. The leaderboard isn't the finding. Best solve rate is 50% overall, 60% under advisory. Cross-family separation (OpenAI vs Laguna) is confirmed under McNemar's test with continuity correction (all four pairs cross α = 0.05). Within-family gaps are noise: a power analysis puts the task count needed to detect a meaningful within-family edge at ~700. That cuts both ways: if the expensive models had a large true advantage, 20 tasks would have been enough to surface it. gpt-5.5 at 12× the cost of gpt-5.4-mini is not the rational choice. All four cross-family pairwise comparisons reach statistical significance at α = 0.05 (McNemar test with continuity correction, n = 60 tasks per model pair): gpt-5.5 vs laguna-m.1 (p = 0.015), gpt-5.4-nano vs laguna-m.1 (p = 0.017), gpt-5.5 vs laguna-xs.2 (p = 0.028), gpt-5.4-nano vs laguna-xs.2 (p = 0.040). Within-family comparisons remain far from significance; those rankings should be read as approximate. The failure taxonomy is the most interesting finding. Wrong-search drift — model finds the right file early, makes one incorrect inference, spends the remaining turns chasing it. Budget expires,