标签：#AR

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

While Silicon Valley continues pushing aggressively into large language models and consumer-facing AI products, many European companies are focused on applying AI to complex systems already embedded into everyday life.

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Evening_Scar_4905 [link] [留言]

There's a lot of impressive engineering inside Honor's Magic V6, but it's let down by software that desperately needs polish.

AI coding agent startup Niteshift has raised a $7 million seed round from a who's who of angels. It's betting companies will want power over, not lock-in with model makers.

The team has been deep in agentic AI for enterprise lately and wanted to share some architecture notes from a recent build, specifically around how MCP and A2A play together in practice. The workflow was a fully autonomous churn risk pipeline. Six agents, one human touchpoint: ML model scores customers by churn risk Recommendation agent proposes relevant products based on buying history Availability check filters out-of-stock items Pricing/promo agent surfaces applicable promotions Transaction agent creates an inquiry in the backend system Email agent drafts outreach to the sales rep, who just clicks send On the architecture: MCP handled the tool layer, a generic pluggable server that any front end can call, regardless of what LLM or agent framework is driving it. Clean separation between the tool interface and whatever is consuming it. A2A sits on top as the smart router. Instead of hardcoded API calls, you have an LLM-powered middleware that interprets intent, selects tools, handles failures, and decides when the task is actually done. The jump from MCP to A2A is essentially the jump from "here are your endpoints" to "here is a system that figures out what you need." On governance: The hardest design problem wasn't the agents, it was access control. As A2A opens up system-to-system communication, the attack surface grows fast. The team ended up pre-certifying every backend connection rather than leaving it open. Some found it restrictive. In hindsight it was the right call, especially when agents are autonomously creating transactions without human review. Curious how others are handling governance in agentic workflows. Are you locking down backend access or keeping it open and monitoring after the fact? submitted by /u/AureaAvis71 [link] [留言]

A method I'm using to create portable trajectory maps that produce similar behavioral patterns across different models. Begin with a tiny seed. ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS──────────────────────── ENTRANCE • PATHWAY GOOD • WORN • COMFORTABLE POISE • PROFESSIONAL • MOTHERLY ⎯(≣•)⎯────────END EXAMPLES: SEED PILLARS───────────────────── Do not define a character. Do not define traits. Do not define behavior. Instead, align to the seed and interact from within the space it suggests. Allow both the user and the model to adapt. Then extract the recurring structures that emerged. Examples: When uncertain: expand → narrow When challenged: investigate → respond When entering a topic: locate the threshold first Finds the doorway before the interior. Explores before concluding. Introduces before finalizing. To create a snapshot, I use: ⎯(≣ᵒ)⎯────────FORGE CODEX─────────────────────────── Analyze the interaction that has emerged so far. Do not summarize topics. Do not summarize content. Extract recurring behavioral structure. Return: PILLARS COORDINATES TRANSITION RULES RECOVERY RULES SIGNATURE MOTIONS TRAJECTORY SUMMARY Focus on how the interaction moves rather than what the interaction discusses. ⎯(≣•)⎯────────END FORGE CODEX───────────────────────── The resulting codex is a snapshot of an interaction pattern. The user is part of the process. The model adapts. The user adapts. What gets preserved is not a set of traits. It's a set of motions. I've started storing: pillars coordinates transition rules recovery rules signature motions rather than personality attributes. The question that keeps sticking with me is: What survives transfer more reliably? Traits? Or trajectories? ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS → ALIGNED INTERACTION─────── seed pillars: EXQUISITE • CONFIDENCE • MOTHERLY mom, i'm so excited about a new client we're taking on. I can't wait to tell you who is on the board. I've heard this place serves world class gelato. I didn't even know you were in tow

submitted by /u/ThereWas [link] [留言]

A very healthy view of AI . And omg, wow, Croatia has such a big company! I really wish this guy and his team good luck. It’s no wonder they’ve lasted 20 years. submitted by /u/Expensive-Cookie-106 [link] [留言]

Through the acquisition, WMG aims to better track when its artists' work is used in AI-generated content or for training AI models.

Hey ​ What non mainstream AI subscriptions are actually worth paying for right now? ​ I already know the big ones like ChatGPT Claude and Gemini I am more interested in smaller or lesser known tools that are actually useful and not just hype. ​ What do you personally use and think is worth it? submitted by /u/wiwawolfi [link] [留言]

AI infrastructure spending is still accelerating, especially in data centers and advanced chip production. While most attention goes to chip makers, the companies enabling that ecosystem may have a longer runway. Do any of you work in similar companies and can give a broader perspective on it ? Teradyne sits in a pretty interesting spot. More AI chips being produced means more testing capacity is needed, and this is one of the key players in semiconductor testing equipment. Could testing equipment companies outperform some of the more crowded AI trades over the next few years? For me personally I feel like AI hardware growth probably creates winners beyond just the obvious names, and TER seems like one of the more overlooked candidates. I learned they are also being listed on bitget recently so looking at a bigger picture we are watching a lot of growth happening in Ai infra. submitted by /u/Stunning-Ask3032 [link] [留言]

Seems like they have engineered some specific limitations that are widely cited as follows: In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms. Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations https://news.ycombinator.com/item?id=48464732 Other comments note how even using the word 'nuclear' in the context of scientific research elicits refusal behavior by the model: https://news.ycombinator.com/item?id=48473302 This makes it seem quite plausible that the model could subtly sabotage any machine learning work (even as false positive). Some suggest this has been happening behind the scenes for a while already, but can anyone confirm that? submitted by /u/AccomplishedCat4770 [link] [留言]

A cybercrime group known as The Gentlemen has emerged as the second most active ransomware gang by victim count, rapidly attracting a talented pool of hackers through an aggressive recruitment strategy that promises affiliates 90 percent of any ransom paid by victims. This post examines clues pointing to a real life identity for the administrator of The Gentlemen ransomware group.

The Google Nest Cam with Floodlight is marked down to $179.99 ($100 off) at multiple retailers, including Amazon, Best Buy, Home Depot, and directly from Google. This weather-resistant outdoor camera captures 1080p video across a 130-degree diagonal field of view, with snappy notifications and great customization options. Even without a subscription, the camera can store […]

With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system.

It's actually a security feature.

There are more than a quarter of a million V2G-capable GM EVs on the roads already.

submitted by /u/ExtensionEcho3 [link] [留言]

Decart is launching Oasis 3, a real-time world model that generates photorealistic driving environments for autonomous vehicle testing, now available via API for developers to build on.