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Restaurant operators rarely have two days that look alike. New challenges emerge constantly, and keeping up means making dozens of decisions with limited time and incomplete information: Why did food costs spike at one location? Which limited-time offer actually drove profits? Why is one store consistently outperforming another? Finding those answers often means digging through multiple reports, exporting spreadsheets and piecing together data from different systems, a process that takes time and distracts restaurant operators from their key hospitality role. Surfacing data and pattern recognition are where AI excels. However, when restaurants use a generic AI model, it can’t dive into what your restaurant needs to know about its own operation, explains Matt Sundberg, SVP, Product at Craftable. That’s the impetus behind “Crafti,” the AI model from Craftable, a software platform that replaces disconnected spreadsheets and manual processes with automated, real-time visibility into food costs, vendor spend and operational margins. “Crafti’s conversational layer allows general managers to ask anything and receive a grounded answer, along with the path forward, without having to wait on a colleague or a support ticket,” Sundberg says. From Reports to Conversations Through conversational AI, GMs can interact with operational data naturally, uncovering insights that help them make better decisions across every aspect of the business. “Restaurant operators are surprised to find that Crafti doesn’t just give an answer, but actually drafts solutions,” Sundberg says. For example, if a GM asks it to describe next week’s order, Crafti will draft a purchase order against the actual vendor catalog, pricing and pack sizes. Or, after providing a cocktail name, photo or PDF, it will draft a costed recipe matched to actual inventory. Wondering how Crafti can level up your own operations? Here are some examples of actual questions operators have asked to spark ideas about how it
Facial recognition technology leads to 11 arrests at Manchester's Caribbean Carnival Tens of thousands attended the two-day event Last updated 14 hours ago Manchester's Caribbean Carnival has passed without any significant problems, according to Greater Manchester Police. Tens of thousands of people attended the two-day event, taking in Saturday's parade, live performances and other activities over the weekend. Officers used Live Facial Recognition, known as LFR, as part of their policing at this year's carnival. The technology scans faces and alerts officers when someone is wanted or subject to a court order, with all images deleted within 24 hours, according to Greater Manchester Police. LFR has previously been deployed at the Brit Awards, the MOBO Awards, football matches, the Manchester Marathon and in town centres. Eleven arrests over the weekend There were 34 alerts from the technology over the two days. Eleven of these led to arrests, for offences including breach of a sexual harm prevention order, fraud, failing to appear at court, and theft. The remaining 23 alerts included eleven registered sex offenders, whose conditions were checked by officers, and five people known to regularly carry knives. A missing child was also identified with the help of the technology, police said. Greater Manchester Police said no one was misidentified during any of the scans carried out over the weekend. More than one million scans since launch LFR has been used more than one million times since it was introduced in October last year, according to the force. In that time, only one person has been mistakenly identified, a white female at the Manchester Marathon, police said. Officers were on hand throughout the carnival to explain how the technology works and to answer questions from the public, Greater Manchester Police said. Assistant Chief Constable Rick Jackson, who was Gold Commander
Abstract Surface defect segmentation (SDS) is challenging in automated inspections due to the high variability of defects amid complex metallic textures. The defects show significant variations in size, shape, contrast, and spatial distribution. Existing segmentation methods struggle to jointly model fine boundary cues, omni-directional long-range dependencies, and multi-scale context in a single framework. To address these issues, we propose Edge-Guided Omni-Directional Attention Network (EGONet), a novel hierarchical architecture for metallic SDS. EGONet offers rich, multi-level intermediate features that capture both fine-grained details and broader semantic information. It also enhances multi-scale contextual understanding by integrating a Dense Atrous Spatial Pyramid Pooling (DASPP) module with four parallel dilated-convolution branches and a learnable residual blend for maintaining spatial resolution. A Sobel-guided Edge Attention Module (EAM) highlights high-frequency boundary cues at the shallowest feature level via auxiliary edge supervision. The proposed Omni-Directional Attention (ODA) decomposes spatial attention into four axes to model long-range dependencies across all orientations via a two-stage refinement. A Quad-Statistical Spatial Attention Module (SAM) leverages quadruple pooling to deliver finer spatial sensitivity than traditional dual-pooling methods. Moreover, the Efficient Channel Attention (ECA) module recalibrates channel responses to suppress background interference. Lastly, a Bilateral Feature Integration Block (BFIB) combines the dual attention pathways via complementary asymmetric gating and residual stabilization. Extensive ablation studies validate each component’s contribution toward improving overall detection performance. Experiments on two benchmark datasets demonstrate that EGONet achieves competitive performance against state-of-the-art methods, with leading results across multiple defect categories on MT-Defect and strong generalization on SD900. Subjects Introduction Surface defect detection (SDD) is critical for maintaining product quality and operational efficiency in modern manufacturing. Conventional inspection methods suffer from fundamental limitations in precision and adaptability to complex patterns. Human-based inspection is inherently slow and prone to inconsistent judgments1. Ultrasonic methods are constrained by material acoustic properties and require
Gay bars in San Francisco have discontinued the use of artificial intelligence-assisted face scanning technology after the community raised privacy concerns. Both Badlands and Toad Hall announced on Saturday they would stop using Patronscan technology, according to the San Francisco Chronicle. “Effective immediately, we are pausing the use of Patronscan while we review our ID verification and security practices,” Brian Aranda, director of operations at both bars, told the newspaper in a statement. “Our security teams will continue to thoroughly check IDs manually during this time. We appreciate everyone who has shared their concerns and remain committed to providing a safe and welcoming environment for all.” Related: Why are San Francisco gay bars scanning patrons’ faces? The Castro District bars sparked anger and scrutiny when the security measures were first deployed earlier this year, especially among LGBTQ+ patrons of establishments often seen as havens for those not publicly out. Controversy intensified after reporting by the San Francisco Gazetteer. The publication also reported on the use of AI-assisted recognition software at The Mix, where managers have defended the screening technology with local press. "It keeps my customers safe. We've had no bar fights, vandalism and theft is down," a general manager told a Bay Area NBC affiliate in June. The Mix has not made any announcements about the use of the technology since concerns were first raised. The technology is intended to flag fake IDs and identify individuals with a history of causing violence at the clubs. Signage has told patrons before they enter that the technology will be used, though many visitors told the Gazetteer they were unaware images of them would be taken and stored until they were in front of a camera themselves. Among privacy advocates, the use of Patronscan’s technology has raised concerns for years. The OneZero
Law enforcement agencies and governments have long used biometric modalities for accurate identification, enabling law enforcement activities and immigration and border controls. Our data can be collected and lawfully used through facial, iris or fingerprint recognition tools and, now, a new biometric modality has emerged in tattoo recognition technology, which can verify who someone is by the permanent visible marking on their body. It may not be part of an individual’s DNA or genetic makeup, however a tattoo modifies the body and can contribute to identifying unique features about someone to help authorities in investigations. We have introduced a panel at Identity Week America on NIST’s world-class evaluation of tattoo recognition, titled “Tatt-E: Benchmarking the state of the art in tattoo recognition”. Find the session on our agenda. Historically, authorities have searched tattoo image databases using text descriptions of tattoos to assist in their investigations. However, the effectiveness of this method has been limited by the subjectivity of text-based descriptions. Recent advances in technology have enabled developers to leverage artificial intelligence (AI) to create automated, image-based tattoo search capabilities. Compared to traditional approaches that rely on subjective text descriptions, image-based searching provides a more objective means of retrieval. To determine whether these systems are fit for purpose, decision-makers will need to know their capabilities and limitations. NIST is running an evaluation program to assess the accuracy of tattoo recognition algorithms. This evaluation will measure the capability of these algorithms to detect tattoos in an image and to perform automated matching of different images of the same tattoo from the same subject over time. This talk, presented by Mei Ngan, a scientist at NIST, will present the state-of-the-art accuracy in image-based tattoo recognition and discuss current capabilities and limitations of the technology. NIST evaluates tattoo recognition technology as a secondary biometric
"We Envision Growth Strategies Most Suited to Your Business" Revolution of AI integration in ultrasound imaging dates back to the late 1970s, when researchers at different institutions experimented with primary pattern recognition. This marked ultrasound's transition from interpretation to systematic analysis. Currently, edge AI helps in processing images on portable units for instant feedback in emergency settings. Moreover, handheld scanners currently incorporate beamforming chips matching cart-based systems. Organizations also collaborate with clinicians to validate the use of these solutions across diverse regions. AI in ultrasound imaging has grown from a primary development to a crucial diagnostic component, empowering healthcare providers globally. Firms are also adopting ethical standards, ensuring reasonable access and precision diagnostics, elevating patient outcomes. Fortune Business Insights reported that the industry for AI in ultrasound imaging is noticing a substantial growth with a CAGR of 26.61% and is expected to reach a revenue share of USD 3.43 billion in 2026 to USD 22.62 billion by 2034. GE HealthCare Technologies Inc. is a well-known brand providing AI in ultrasound imaging systems that improve scan guidance, workflow productivity, and clinical decision support. Headquartered in the U.S., its Voluson Expert Series and LOGIQ platforms use automation tools to speed assessments and strengthen image consistency. In January 2025, the company received FDA clearance for updated Voluson Expert systems, adding AI features for women’s health imaging. GE HealthCare continues to widen intelligent ultrasound tools for obstetrics, general imaging, and point-of-care use across demanding clinical settings. In February 2026, the company expanded its BARDA collaboration with around USD 35.0 million to improve AI powered ultrasound. Koninklijke Philips N.V., headquartered in the Netherlands, advances in AI in ultrasound imaging through platforms that automate measurements, support cardiac assessment, and extend access to clinical care. Its Lumify handheld ultrasound and EPIQ CVx systems incorporate AI features from
Abstract The tomato plant is considered one of the most important crops in the world, yet it is vulnerable to various diseases that affect crop quality and agricultural productivity. These challenges have driven the need for an efficient and intelligent plant disease detection system. With the development of computer vision and artificial intelligence, this proposed methodology based on deep learning for tomato leaf diseases has been presented. Two public datasets: Taiwan DS with nine classes and Tomato Leaf Diseases Detection Computer Vision Dataset (TLDDCV DS) with seven classes have been used to test this system. This system begins with plant image processing, which includes gamma correction and bilateral filtering, to enhance image quality and clarity while preserving key disease features. Then, a genetic metaheuristic algorithm was used to automatically select the most significant hyperparameters, further optimizing both processing time and accuracy. After that, the tomato leaf disease detection applies the You Only Look Once version 11 Nano (YOLOv11n) model. The YOLOv11n backbone is edited through a Data-efficient Image Transformer (DeiT) to improve the system’s capacity for learning global contextual information and long-range dependencies. Experimental results demonstrate that the proposed system outperforms existing methods. It achieved an average mAP@50 of 97.8%, mAP@50-95 of 93.4%, precision of 97.3%, recall of 93.8%, and F1-score of 95.5% on the Taiwan dataset. Additionally, it achieved an average mAP@50 of 87%, mAP@50-95 of 48%, precision of 83.9%, recall of 70.3%, and F1-score of 76.4% on the TLDDCV dataset. These results demonstrate the generalizability and effectiveness of the proposed system in real-world agricultural situations. Similar content being viewed by others Subjects Introduction Tomato is the most widely consumed and economically significant vegetable crop worldwide, second only to potato in global importance1,2. It is used as both a common dietary staple and a fruit-like ingredient, making it highly
Challenging GPUs, bypassing HBM, digging deep into Cerebras behind the largest chip in history Ten years ago, this was a company that no one had high hopes for. From the very first day of its establishment, it only wanted to build a type of chip that had never been made before. It tried to challenge Nvidia's dominant position, but repeatedly hit a wall in the early days of its founding and was on the verge of bankruptcy many times. Ten years to sharpen a sword. In May this year, Cerebras finally went public, with its market value once approaching 100 billion US dollars. CEO Andrew Feldman said: We are willing to cooperate with every hyperscaler to improve AI inference speed, except for Nvidia. We had the honor to interview Cerebras' early investors, the researchers who first proved that Cerebras' products could be deployed, and discussed with senior executives in charge of products inside Cerebras about the company's transformation in the past two years and its moves after IPO. After piecing together these clues, we were surprised to find that to understand the glory of Cerebras' listing, we need to go back 10 years. Go back to a whiteboard, a startup that was about to go bankrupt, a physics gamble that almost no one believed in, and an AI lab that owned the world's largest language model at that time, and how it became the most critical page in this story at an impossible point in time. The following is the story of Cerebras. 01 Baidu AI Lab Discovering the Scaling Law In 2014, Andrew Ng left the Stanford AI Lab and joined Baidu. Inspired by Google Brain, Baidu invested 300 million US dollars to establish an AI lab located in Silicon Valley. Their main goal was to make deep learning
Women and children could be disproportionately impacted by the use of such technology Attorney-general Michelle Rowland has penned a letter to privacy commissioner Carly Kind regarding possible privacy concerns flagged by the community over the use of smart glasses, which are embedded with cameras that can take photos and videos. Rowland requested that the Office of the Australian Information Commissioner (OAIC) prioritise looking into the privacy implications of such gadgets, noting their rising accessibility in the country. She indicated that women and children in particular could be disproportionately impacted by the use of the technology for harmful conduct like inappropriate recording, harassment and surveillance. “Unlike other forms of technology, smart glasses may be used more discreetly, making it harder to know when you are being recorded”, Rowland said in a statement. In 2024, the International Bar Association raised concerns regarding Meta and Ray-Ban's AI-powered smart glasses, which came out in late 2023. The body highlighted issues with data security. “How do you provide for the rights of the data subject? If you film someone without authorisation to what extent is that legal?” asked Larissa Galimberti, then-chair of the IBA’s cybersecurity subcommittee. Callum Sinclair, who leads the technology and commercial firm at Scotland firm Burness Paull, noted that smart glasses wearers could record people and obtain personal data without consent. Rowland said the government considered the OAIC’s expertise and oversight to be key to tracking developments in this field. “The OAIC regularly monitors the effects of new technologies on privacy – and the government has every faith that they will identify any new privacy risks and mitigation measures”, the attorney-general said. “The government is continuing to work on the next phase of privacy reforms to ensure our privacy laws are fit for purpose in the digital age”. The OAIC recently updated
09 Aug FLOCK EXPERT WITNESS AND TESTIMONY CONSULTANT: PRIVACY, SECURITY, IDENTITY, ETC. A Flock Safety expert witness and testimony consultant is a person with knowledge relevant to the company’s technology, automated license plate recognition (ALPR/LPR), vehicle-location data, digital evidence, law-enforcement use of license-plate-reader systems, privacy and surveillance technology, or another issue involving related matters or evidence. Like you’d hear from top Flock Safety expert witness testifying consulting pros, the firm is a public-safety technology company whose products include license plate reader cameras and other technologies. Its LPR system is designed to capture images and data relating to vehicles and license plates and make that information searchable for authorized users. The way Flock Safety expert witnesses tell it, the firm describes its LPR technology as providing vehicle information, including license plate information and vehicle characteristics, along with dates, times, and camera locations. In a legal proceeding, the technology itself may become important evidence. For example, a case might involve a question about: - Whether a particular vehicle was captured by a Flock camera - What the image actually shows - Whether a license plate was correctly read - How the system identified vehicle characteristics - When an image was captured - Where a camera was located - How the evidence was stored - Who accessed the information - Whether data was shared - How long the information was retained - Whether the system was operating properly - How reliable a particular identification is - How an investigator interpreted Flock results A Flock Safety expert witness can help explain technical subjects that ordinary judges, jurors, attorneys, or other decision-makers may not be expected to understand without specialized knowledge. What Is Flock Safety? Flock Safety describes itself as a public-safety technology company whose products include LPR cameras, video cameras, gunshot-detection sensors, real-time crime-center
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I have used Google Photos for years now, backing up decades of photos and videos to its servers. But with my ever-growing library, my desire to have more control over it increased. So, I installed a Google Photos alternative and painfully transferred all my photos, fully prepared to leave Google's service behind. A few weeks later, I realized Google Photos is so deeply integrated with Android that leaving it changed how I accessed my pictures. I thought I was replacing a backup service Immich stored all my photos, but Android could no longer see them I had a straightforward plan. I would replace Google Photos with Immich, host it on my NAS, and move my entire library there. That way, I would have complete control over my photos and videos, while ending my dependence on Google's servers. Initially, I was happy with the switch. I could easily access my library through the Immich app, and a recent update fixed many of the issues that bothered me. For the first few weeks, it seemed like I had successfully replaced Google Photos. Then, I tried uploading an old photo to another app. That's when I discovered the mistake I had made by leaving Google Photos. Android Photo Picker makes it easy to select and upload pictures inside supported apps. It provides a single unified view of all the photos and videos stored locally on your phone alongside the cloud. Google Photos was my primary photo backup service for years, so decades of memories appeared alongside recent snaps. I never had to remember whether an image was stored locally or only in the cloud. More importantly, the Photos integration provided more than access to my photos and videos. I could search my Google Photos library using people's faces and browse my existing albums.
When a man browsing his local thrift store came across a painting bearing a distinctive signature, he decided to investigate. Mark Harrington is an avid thrifter—and he’s not alone. According to figures published by Capital One Shopping, around one in five Americans shops at thrift stores in a typical year. For Harrington, thrifting appeals for a number of reasons. “What I personally like about thrifting is that it feels like a window into the past,” he told Newsweek. “It feels like a treasure hunt, and I get to learn about all of these beautiful things. I often daydream about the path that led them to me. It’s a little sad that they were once so loved and end up being undervalued. But that’s also what I enjoy.” Another appealing aspect is that, thanks to modern technology, it has become easier to spot a bargain and sell it for a profit. “There are so many ways to resell—Whatnot, eBay, and Vinted, for example,” he said. “Combined with Google Lens, an image-recognition tool from Google that uses artificial intelligence to identify objects, being so accessible, you don’t necessarily need prior knowledge to find out whether something is valuable.” The painting he recently came across is a case in point. “Saturday morning, I was at one of my favorite thrift stores,” Harrington said. “By pure chance, I thumbed through a bin of wall hangings before I left and found the Stan Sobossek painting.” The presence of Sobossek’s signature in one corner intrigued Harrington, but it was not the sole reason he decided to buy it. Stanley Sobossek was a New York artist known for his mid-20th century oil paintings. “The painting caught my eye because the subject was an antique store, and I thought it would make good décor for my hobby space,”
A patterned 2009 Toyota Yaris drove past a Flock surveillance camera at DEF CON on Friday and, according to researcher Bill Swearingen, avoided the system's automated detection. TechCrunch describes it as noRecognition's first public physical-camera test. The result is narrower than a car or person becoming invisible: the camera still recorded footage, while the demonstration video and logs are not public. TechCrunch is the only detailed public account we found. The distinction matters because the project's own research dashboard labels its published benchmarks digital and simulated. They test rendered patterns against detector software, including weights extracted from a deployed camera, but they are not measurements of printed fabric or a vehicle under uncontrolled outdoor conditions. The Las Vegas drive-by is therefore an intriguing lead, not yet a reproducible result. The evidence has to be read detector by detector and condition by condition. - The field resultA Toyota Yaris wrapped in a generated pattern reportedly passed one Flock camera without triggering the expected automated detection. - The evidence gapNo public demo video, alert log, control run or repeated physical-test dataset is available yet. - The benchmarkThe project's strongest published percentages are held-out digital simulations, not printed-fabric or real-camera measurements. - The next standardRepeated independent trials across cameras, light, distance, angles and controls are needed before the pattern can support a dependable privacy claim. What happened in Las Vegas Swearingen presented the work at Black Hat on August 6 in a briefing titled “Could a Pattern on Your Clothing Fool Facial Recognition?”. TechCrunch reports that the first public physical test followed on Friday at DEF CON. With help from Donut Media, the team covered the Yaris in a newly generated pattern and drove it past a Flock camera. Swearingen said the attempt was effective, although the wheels complicated the design. Donut Media
Solomon Technology Corp (所羅門) is expanding its artificial intelligence (AI)-powered systems for humanoid robots, with chairman Johnny Chen (陳政隆) saying that the company aims to address one of the biggest hurdles to wider adoption — machine perception. Rather than manufacturing humanoid robots, Solomon develops AI-powered 3D vision modules and machine vision software that enable robots to perceive, understand and interact with their surroundings. The company is also applying the technology to drones, autonomous mobile robots (AMRs) and industrial inspection systems. Photo: CNA In an interview, Chen cited an intelligent drone inspection system for solar farms in southern Taiwan as one of Solomon’s latest commercial applications. Drones equipped with the company’s 3D machine vision and AI software can automatically detect obstructions on solar panels that reduce power generation, he said. The drones identify potential problems during aerial inspections and transmit high-resolution images to maintenance crews, allowing faults to be addressed more quickly and improving the efficiency of solar farm operations, according to Chen. The same AI vision technology has also been deployed in AMRs, robotic arms, pan-tilt-zoom cameras and quadruped robots used in industrial inspection projects in markets including the US and Japan, he said. Humanoid robots must combine three core capabilities to perform practical industrial tasks: reasoning, active perception and action, Chen said. Although advances in large language models and vision-language models have improved robots’ ability to understand instructions, perception remains a major challenge because robots still struggle to identify distant or partially obscured objects in complex environments, he said. To address the problem, Solomon has developed generative AI vision technology that uses synthetic images to train AI models, reducing training time while improving robots’ ability to recognize unfamiliar objects and adapt to different real-world scenarios. The company has also developed an “active perception” system that replaces conventional single-image recognition
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Bill Swearingen has spent the past year running largely the same test, over and over again. The goal was to produce a computer-generated pattern that could block the surveillance cameras lining America’s streets from detecting it. Some 31 million tests later, Swearingen says he can now produce patterns on-demand that, when applied to clothing and objects, prevent some of the most commonly deployed license plate readers and surveillance cameras from detecting whatever the pattern covers, from people to vehicles. His project, which he calls noRecognition, allows people to escape the automatic detection and algorithmic surveillance used across the U.S. and beyond. In recent years, surveillance cameras have been supercharged with the ability to detect what is happening in the footage being recorded, from tracking the license plates of speeding vehicles to using facial recognition to identify suspected criminals, albeit with mixed success and sometimes terrifying results. The detection algorithms that power most surveillance cameras today can sift through vast amounts of footage, allowing law enforcement to pick out activity of interest, akin to pulling a needle out of a haystack. Swearingen’s computer-generated patterns do not block surveillance cameras from recording video footage. Instead, they scramble the camera’s ability to identify objects, people, or faces, so that the cameras do not trigger any detection alerts. By blocking the camera’s ability to detect what the pattern covers, the person becomes a needle in a haystack again — until someone knows where to look. “Privacy is a fundamental right,” Swearingen told TechCrunch in a call this week. He described his patterns as a way to allow people to “opt-out of being tracked.” In its first public test Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen successfully demonstrated the pattern printed on a vehicle, proving that these patterns can be effective
The Delhi police significantly bolstered security at the iconic Red Fort for Independence Day celebrations, deploying cutting-edge technologies like the Abhigyan app and Facial Recognition Systems to identify and deter suspicious individuals, ensuring a safe national event. Key Points - Red Fort security for Independence Day features a multi-layered approach with advanced technology. - Delhi police is deploying the Abhigyan app for real-time fingerprint verification against a criminal database. - Facial Recognition System (FRS) vans and 1,000 CCTV cameras are monitoring the Red Fort and surrounding areas. - Anti-drone systems and snipers are in place to prevent aerial threats and ensure comprehensive surveillance. - These smart policing initiatives aim to identify suspicious persons and ensure a safe August 15 event. The Red Fort has been placed under a multi-layered security cover ahead of the Independence Day celebrations, with the Delhi police deploying advanced technologies, including the Abhigyan app and facial recognition system (FRS) vans to identify suspicious and wanted persons in the crowd, officers said. The security arrangements have been strengthened amid intelligence inputs. Around 15,000-20,000 Delhi police and paramilitary personnel are expected to be deployed for the August 15 celebrations. Nearly 25,000 people, including VIPs and VVIPs, are expected to attend the event at the Red Fort. Advanced Technology For Crowd Screening For the first time as part of the enhanced security arrangements, the Abhigyan application is being used extensively to screen people in the crowd, the officers said. The application has a database of more than one crore criminals, and police personnel are using handheld devices to collect fingerprints of randomly selected persons, they added. The fingerprint is checked against the database within seconds. A red indication means the person has a criminal record, with details appearing on the screen, while a green indication indicates that no criminal
The iFootage Shark Slider Nano II 860 (24.9″) is a slightly longer version of the original Shark Slider Nano II. It is a compact motorized slider that features advanced AI technology that enables multi-axis coordination. There is 24.9″ of usable track, and the slider features multiple 3/8″-16 holes that can interface with a wide variety of tripods and joystick attachments, as well as 1/4″-20 and 3/8″-16 screws for mounting compatible fluid heads. Key features - Motorized Slider with Advanced AI - 24.9″ Usable Track Length - Multi-Axis Coordination for Flexibility - For RS 2/RS 2 Pro/RS 3 Pro/RS 4/RS 4 Pro - Horizontal Payload: 11 lb - Vertical Payload: 5.5 lb - 360° Pan Axis Accuracy - Touchscreen Gesturing Key Frames - Facial Recognition, Intelligent Tracking - Aluminum Alloy/Carbon Fiber Design The Shark Slider Nano II has a track design that is claimed to minimize shake and enhance stability. It can handle horizontal payloads up to 11 lb and vertical payloads up to 5.5 lb. The Nano II supports horizontal movement and 360° rotation. The Nano II uses advanced AI for accurate tracking. You can select a target in the app to activate features such as facial and object recognition. Co-developed with DJI, the slider works with select DJI gimbals, including the RS 2, RS 2 Pro, RS 3 Pro, RS 4, and RS 4 Pro, with the removal of the gimbal grip and using the included DJI adapter, and it can achieve multi-axis, zero-latency synchronization when using the iFootage custom RS adapter. When paired with the RS 2, RS 2 Pro, RS 3 Pro, RS 4, and RS 4 Pro, it works with their pan, roll, and tilt axes. By connecting to the custom DJI adapter, there is direct data transfer that enables zero-latency control and Users can control