Honor put a motorized camera arm on a regular-looking phone and started selling it in China yesterday. The device, simply called the Robot Phone, costs 9,999 yuan for the 12GB and 512GB version, roughly $1,500, with a higher 16GB and 1TB model at 12,999 yuan. Pre-orders opened immediately after the Guangzhou event, and full sales begin August 18. For now the phone stays in China.

When first turned on, the phone appears to be your standard device. Its dimensions are normal to say the least, measuring 9.59 mm thick, weighing 248 grams, and having a sleek metal body with modest, curved corners. There’s a 6.31-inch LTPO OLED display up front, which can produce 6,800 nits and switch between 1 and 120 frames per second seamlessly. All of this is powered by a Snapdragon 8 Elite Gen 5 chip, which also includes a 7,060 mAh battery, rapid charging (120 watts wired, 50 watts wireless), and just IP54 water protection.

Sale

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• Google Pixel 10a is a durable, everyday phone with more[1]; snap brilliant photography on a simple, powerful camera, get 30+ hours out of a full…
• Unlocked Android phone gives you the flexibility to change carriers and choose your own data plan; it works with Google Fi, Verizon, T-Mobile, AT&T…
• Pixel 10a is sleek and durable, with a super smooth finish, scratch-resistant Corning Gorilla Glass 7i display, and IP68 water and dust protection[4]

When you press a button, or even just wave your hand, something interesting happens. A glass cover swings open, showing a small titanium gimbal protruding from the rear of the phone. Someone at Honor evidently spent a long time figuring out how to make this thing smaller, as the mechanism is 65% shorter than typical, yet it also contains over 100 precise pieces. The 2.6-gram titanium motor can spin at a staggering 360 degrees per second. Want the camera to remain in a fixed position? Or keep your first-person perspective? Perhaps you just want to spin it 90 or 180 degrees on a whim? Not a problem; the arm can do it all, and more. Once up and going, the camera maintains its level even as the rest of the phone jerks around.

The main sensor is also impressive, at 200 megapixels and powered by a 1/1.28-inch chip and f/1.6 lens. You get a matched 200 megapixel telephoto with 2.7 times optical zoom, as well as a 50 megapixel ultrawide lens capable of shooting at angles of up to 122 degrees. Honor has partnered with ARRI to enable the phone to take footage in 10-bit LogC3, using the official ARRI Wide Gamut and all that jazz. When you take a photo, it is processed by Honor’s H1 imaging technology, which is excellent at reducing noise and other issues. Do you want to record some serious high-speed footage? There’s no need to worry because 4K at 120 frames per second is equivalent to 1080p at 240 fps. The cherry on top is that you can import the clips directly into DaVinci Resolve with the ARRI LUTs already applied.

The software portion is where things get very interesting. When you press a few buttons or tap a couple of faces, the YOYO Robot Mode activates, autonomously following individuals and keeping the camera focused on them without your interaction. Double tap a face to lock in, even if they stroll away for a bit, and it will immediately reclaim them. Alternatively, use gesture controls to flip the arm up and start rolling.

Want to tell the camera what to do? Voice instructions are available on the menu, and you can instruct it to swap angles or turn for a specific number of seconds. Still, all of this debate about modes and capabilities misses the point: this phone can be used to create video that is nearly identical to the original. Take a solo stroll or go live on a treadmill, and the phone will track the movement with much less tremor than if you held it by hand. Group photographs are also a breeze, since the arm swings from side to side, capturing a few shots and quickly stitching them together to create a lovely, broad panorama.
[Source]

CYBER-CRIME

Custom tools harvested whatever over-permissioned guest accounts would surrender

Someone has spent more than a year rifling through Salesforce and ServiceNow portals around the world, harvesting data that organizations accidentally left open to anyone who came looking.

Researchers at Reco have named the operation “City-Forum” after a domain connected to its infrastructure. The domain has pointed to the attacker’s server since March 2025, although exactly when the campaign began is unclear. Reco says the activity is continuing and increasing in volume.

Reco isn’t naming the targets, but said it spotted the attacker poking around portals belonging to telecoms companies, banks and other financial services firms, enterprise software vendors, cybersecurity companies, and public sector bodies.

“In the last year, we’ve seen many threat actors that use Aura enumeration against over-permissioned Salesforce guest users. This actor is different,” said Nitay Bachrach, senior security researcher at Reco.

On Salesforce, the attacker targets Lightning Web Runtime (LWR) sites through the UI API’s GraphQL layer, an approach Reco says it has not found documented in public research or incorporated into publicly available attack tools. Over at ServiceNow, the same operator queries a native Service Portal search endpoint that has received little public attention.

The tooling also checks whether Salesforce sites permit self-registration, potentially offering a route from anonymous guest access to an authenticated external account with permission to see considerably more data. Reco said it saw these checks across most of the Salesforce targets it examined.

“The threat actor created their own toolset, based on research and techniques which are not well documented online,” Bachrach said. “They studied the services to map different common data leak vectors – this is an advanced actor.”

This isn’t casual poking around either. Reco said the busiest Salesforce target logged more than 560,000 events from the attacker’s IP during the campaign, almost all attempts to enumerate data available to guest users.

Reco linked the Salesforce and ServiceNow activity to the same server, which targeted multiple organizations around the world. More unusually, the attacker hasn’t bothered changing its infrastructure: the same IP address and domain have remained in use for at least 17 months, with related custom tooling doing the rounds across both platforms.

ServiceNow told us it is “aware of a security company’s blog post claiming certain configurations are creating security risk. As noted in the security company’s post, there are no allegations of a compromise of the ServiceNow environment. Nonetheless, we take third party reports seriously and are investigating accordingly. Our priority is to protect our customers, their data, and our systems.”

Salesforce has not yet responded to The Register‘s questions. 

Salesforce customers have already had one very public lesson in what can happen when guest access gets too generous. In March, ShinyHunters told The Register it had stolen data from around 100 high-profile companies and nearly 400 websites after going after over-permissioned Experience Cloud guest accounts.

City-Forum isn’t doing quite the same thing, and Reco isn’t blaming ShinyHunters. “We don’t know who this is, and we’re not ruling anyone in or out,” Bachrach said.

Reco says all the activity it observed was conducted without authentication, with the attacker collecting information that organizations had exposed through permissions, sharing rules, search sources, or other configuration choices.

“If the guest can read a record, so can anyone on the internet,” Bachrach warned. “That is not a platform vulnerability.”

Which is good news for Salesforce and ServiceNow, perhaps, but rather less comforting for anyone now wondering what their guest account has been showing the guests. ®

Presented by Capital One

At VB Transform 2026, Kel Vanee, MVP of machine learning engineering at Capital One, spoke with Sam Witteveen, Senior Technology Contributor at VentureBeat, about how the bank built a scalable multi-agent AI architecture around deeply customized open-weight models rather than relying on an off-the-shelf foundation model.

“At Capital One, we’re not just using AI, we’re building AI,” Vanee said.

The groundwork was laid years ago with Capital One’s early investments in data transformation and cloud adoption, which Vanee said were foundational to moving quickly when the current wave of AI arrived. That technical foundation enabled the company to make several deliberate architectural decisions, including building a centralized, enterprise-wide AI platform with built-in governance, deeply customizing open models with proprietary data, and constructing its own multi-agent orchestration harness.

Customizing open-weight models with proprietary data

Rather than relying solely on off-the-shelf frontier models, Capital One fine-tunes open-weight models using its rich, proprietary data.

“We view our data as a huge advantage and something that nobody else has, something that the general frontier models cannot provide. So we are taking that data and deeply customizing these models,” Vanee explained. He added that real-time data is absolutely critical to bring in fresh context during live customer or associate interactions.

Vanee also revealed an unexpected benefit of this approach: extensibility across the enterprise.

“As we customize those open-source models for one use case, we actually see benefits across our whole portfolio,” he noted. “We are training that model to be an expert at Capital One use cases, policy, and nomenclature. As we do that training, we see a general lift.”

Inside Capital One’s multi-agentic AI workflow

As an example of the approach, Vanee pointed to a customer-service workflow for bank fraud that handles millions of calls a year, where interactions range from roughly four minutes to as long as sixty minutes, and where an initial attempt at engaging a single large language model proved insufficient. With Capital One’s multi-agentic workflow (MACAW), interactions are routed through specialized agents with governance and guardrails built in.

“The MACAW workflow is made up of a number of different agents,” he said. “The first one is an understanding agent. Its purpose is to look at what the customer is saying and try to understand what their intention is.”

From there, a reasoning agent is given several specific instructions to generate a summary; a validation agent fact-checks the summary to ensure it is accurate; and an explaining agent turns the summary into a formatted document with all necessary details that is then shared with agents.

For the consumer banking use case, this workflow helps several hundred customer-service agents who specialize in complex fraud calls. The post-call summaries it generates help document long, back-and-forth interactions that agents previously had to reconstruct by hand.

Capital One’s multi-agentic architecture also underpins Chat Concierge, a customer-facing auto-shopping assistant, which further leverages a version of Meta’s open-weight Llama model that has been customized with Capital One’s proprietary data. It uses the same division of labor, with one agent conversing with the customer, one building an action plan from business rules, one evaluating accuracy, and one explaining and validating the result.

Optimizing latency and cost with an agentic research system

Beyond customer-facing solutions, Capital One is also leveraging agentic AI to automate rote tasks for its employees and help them focus on high-leverage aspects of their work. In one example, the company built an autonomous agentic optimization solution to tune backend hosting infrastructure.

Vanee explained that in the world of LLMs, where new optimizations are delivered every day, they aren’t all complementary. Combining two good optimizations can sometimes cause a performance regression.

“This agentic system will run through a search space that is designed by the researcher, handle all the mechanics of setting up that experiment and running the experiment, and then put a whole summarization of the results in front of the researcher,” Vanee said.

Vanee added that the system allows researchers to “find the series of optimizations and configurations that’s really going to give [them] the best latency possible.”

What’s next: model routing and proactive, event-driven AI

Looking ahead, one big trend Vanee sees is routing abstraction layers that a platform seeks to validate over multiple models, both for cost and accuracy.

“We actually think that you can get better accuracy than any individual model simply by routing across a broader set of available models, because different models are going to excel in different areas,” he said.

His second prediction was a shift toward systems that act without waiting to be asked, while also emphasizing that deploying such proactive agents would demand rigorous testing and monitoring.

“The thing I think is going to become bigger in the future is more proactive and event-driven AI,” Vanee said. Rather than waiting for a human prompt, AI would step in as soon as it detects conditions that warrant action.

“This is going to enable more monitoring and larger-scale monitoring, and it’ll empower us as we fight fraud and address these opportunities,” Vanee said. “So proactive AI is going to be a really important trend.”

Driving continuous AI innovation in financial services

Capital One’s approach underscores a broader truth for enterprise technology leaders: driving measurable value with AI requires moving beyond off-the-shelf software toward deeply customized, highly governed architectures. By combining fine-tuned open-weight models, a multi-agent orchestration harness, and proprietary data assets, the bank has established a repeatable blueprint for deploying scalable AI in financial services.

“All of those ingredients were absolutely critical to differentiating in this space and hitting the quality bars as well as the cost and latency thresholds we set for ourselves,” Vanee said.

As the company expands these capabilities across new use cases, its enterprise platform approach helps to ensure that technical breakthroughs translate into safer, faster, and more personalized experiences for its millions of customers.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Specialised engineers and consultants trained through OpenAI’s partner network will work directly with clients on solutions.

OpenAI – in its latest effort to garner enterprise support – has teamed up with IBM to provide organisations with its tools to scale AI deployment.

The deal will see the two companies create industry-specific, go-to-market initiatives targeted at financial services, government, telecommunications and retail sectors, and comes at a time when AI investments are surging multi-fold despite only a fraction of businesses seeing real returns.

“While enterprises are rapidly investing in AI, they are looking for practical ways to apply it across their core operations to deliver measurable business outcomes and create new commercial models,” said Andy Baldwin, the global senior vice-president at IBM Consulting.

“The challenge is not access to AI technologies – it’s integrating AI securely and at scale into complex enterprise environments and workflows.”

The partnership embeds OpenAI’s latest frontier models, including GPT-5.6 and products like Codex and ChatGPT Work, into IBM’s AI platform.

IBM said it will deploy units of specialised engineers and consultants trained through OpenAI’s partner network to work directly with clients to accelerate AI implementation across complex business workflows and highly regulated environments.

The technology giant is also launching a dedicated channel through which more of its consultants and engineers can be certified under the partner network.

The two also said they will expand their collaboration as part of OpenAI’s cybersecurity partnership programme Daybreak by combining the AI giant’s frontier capabilities with IBM’s multi-agent-powered service for delivering decision-making and intelligence.

With this, the collaborators aim to help clients manage both cyber and AI model risk, including application-layer vulnerabilities, governance gaps and operational risks that limits AI adoption, they said.

“The organisations pulling ahead with AI are the ones turning it into a trusted part of how their business operates,” said Denise Dresser, the chief revenue officer at OpenAI.

Earlier this year, IBM set aside more than $10bn for its lofty quantum plans that it hopes will deliver the world’s first large-scale, fault-tolerant quantum computer by 2029.

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