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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
Hard Krypton Exclusive | Backed by three rounds of investment from Li Zexiang, a PhD graduate from Zhejiang University has developed the world's first vision-based after-sales technical customer service robot According to Hard Krypton, visual model enterprise MicroLink Intelligence has recently completed an angel round financing of nearly 10 million RMB, with investors being Li Zexiang and Lu Qi. The investment entities are Dongguan Clear Water Bay Phase II Venture Capital Partnership (Limited Partnership), Ningbo Institute of Intelligent Technology Co., Ltd., and Beijing Qichuang Chuangtan Phase II Venture Capital Center (Limited Partnership). This round of financing will be mainly invested in research and development. Founded in 2023, MicroLink Intelligence's founder Ning Dongdong is 31 years old this year. He pursued his doctoral degree at Zhejiang University, engaged in machine vision research, and is one of the early practitioners of deep learning-based machine vision in China, with rich experience in image processing. Ning Dongdong told Hard Krypton that he originally followed Li Zexiang to engage in product R&D in the image field, and found in the investigation that the customer service of the consumer electronics industry was very primitive. However, Ning Dongdong did not act rashly. In order to fully understand the e-commerce customer service chain, he investigated three different entities: e-commerce platforms, merchants on the platforms, and tool vendors selling AI customer service. Ning Dongdong's first stop was to work as a customer service intern at JD for 3 months. He found that JD's internal system was very perfect. "The logistics, finance and e-commerce departments are fully connected, and customer service can directly modify the detail page of self-operated stores or adjust the logistics address." Later, he went to work as a pre-sales customer service for an electrical appliance merchant on the platform. The situation here is different, which is
Big Brother is watching: Are Hoosiers ready to draw the line? Jerry Davich, The Times Aug 8, 2026 1 hr ago 0 Facebook Twitter Bluesky WhatsApp SMS Email Print Copy article link Save Automated license plate readers. Flock camera technology. Cell tower site simulators. Facial recognition. Ring doorbell cameras. Predictive policing. Database sharing. Everyone is being watched all the time. This camera is located on Calumet Avenue in Valparaiso. Jerry Davich, The Times As featured on Facebook Twitter Bluesky WhatsApp SMS Email Print Copy article link Save Most Popular Hammond man guilty of killing wife's girlfriend, sentenced to nearly 30 years in prison Missing evidence a factor in plea deal for Crown Point sex trafficking case, lawyer says Removal of old State Line Bridge in Northwest Indiana gets final approval LaPorte County mansion sells for $5.1 million, second-highest in Northwest Indiana history Crown Point student among 7 charged in Indiana University fraternity hazing case Promotions Get Healthy: Play can spark creativity, boost mood for adults Ask the Expert: Immunizations – what they protect you from, when to receive them, and myths surrounding them. View All Print Ads Other VISITING ANGELS Aug 5, 2026 Other HSPA ICAN PLUS Aug 5, 2026 Grocery Food STRACK & VAN TIL / INDIANA GROCERY GROUP LLC Aug 5, 2026 Services FOUR SEASONS HEATING, AIR CONDITIONING, PLUMBING, & ELECTRIC Aug 5, 2026 Other HSPA ICAN PLUS Aug 5, 2026 Healthcare FAMILY DENTAL CARE Aug 2, 2026 Services L'AMOUR TOUJOURS DESIGNS BY LAURA Updated 15 hrs ago Events Entertainment ST ELIJAH CHURCH Aug 2, 2026 Grocery Food STRACK & VAN TIL / INDIANA GROCERY GROUP LLC Aug 2, 2026 Real Estate STEINER HOMES 15 hrs ago
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Camera glasses get harder to police when they stop looking expensive. Price: $89 AUD ($63) Where to Buy: KSmart For years, camera glasses felt like a Silicon Valley problem. They were pricey, visibly branded, and easy to file away as something early adopters would argue about before the rest of us had to care. The KSmart Camera Glasses make that argument harder to ignore. At A$89 ($63), these are no longer a premium experiment sitting behind a luxury eyewear logo. They’re cheap enough to land in a cart next to school supplies, Bluetooth speakers, and a replacement phone charger. The Price Is the Real Story Kmart lists the Camera Glasses at A$89 ($63). That is the kind of price that changes how people think about a category. Smart glasses with cameras have usually been framed around flagship names, app ecosystems, and early-adopter curiosity. This pair strips that down to a much simpler pitch: camera, Bluetooth audio, calls, and app-connected AI features in a mass-market frame. The product itself isn’t framed like a developer toy. Kmart’s listing says the glasses can capture photos up to 8MP, record videos up to 1080P, play music over Bluetooth, handle hands-free calls, and use smart touch controls. What You Actually Get for A$89 The box includes the Camera Glasses, a 2-pin charging cable, a cleaning cloth, an eyeglass pouch, and an instruction manual. Kmart lists the dimensions at 14.6cm high, 16cm wide, and 5.3cm deep. This is still recognizably a pair of glasses rather than a tiny action camera strapped to your face. The battery is a non-replaceable 290mAh rechargeable lithium-ion pack. Kmart lists about 3.5 hours of working time at 80 percent volume, with a 2-hour charging time. The manual also lists IPX4 water resistance and a 10m working distance with no obstacles. That
New feature coming to Pixel 11 turns conversations into recommended steps of action Hidden code reveals a new Pixel 11 feature that uses Gemini to turn conversations into advice that you can follow. New Pixel 11 app is very useful | Image by Google One of the best moments in Pixel history took place in October 2019 with the release of the Pixel 4 and Pixel 4 XL. This model was unique in Pixel history as it included a version of facial recognition feature Face Unlock that was nearly an exact copy of Apple's Face ID. For the first and only time in Pixel history, there was no fingerprint sensor on the two versions. Pixel 4 series had some new features that were "one and done" The Pixel 4 and 4 XL also had a feature driven by the Soli radar chip that was placed in the phone's top bezel. It allowed the user to answer or dismiss a call, skip or repeat a song, snooze an alarm, dismiss a timer, and pause or resume audio playback. It also lowered the ringtone or alarm volume when it sensed a hand heading toward the device. The Pixel 4 series did so poorly that this version of Face Unlock never returned and never again would a Pixel model be released without a fingerprint sensor. In fact, the Pixel 4 line so thoroughly broke Google's expectations that the Pixel 5 ended up as a mid-range phone. It was almost as though Google knew that the Pixel needed to take a step back before it embarked in a new direction with the Pixel 6 series. The 2019 Pixel series debuted a red-hot Pixel-exclusive app The Pixel 4 and Pixel 4 XL also debuted a new app that has become a hit. It is unique
What Disney Knows About You As Soon As You Enter The Park For many, the appeal of Disney theme parks is the escape from the real world into fantasy, and its sense of care-free security. When you're at a Disney park, you're in a bubble, and in that bubble everything runs smoothly and feels personalized, like magic. So, in an increasingly technological world, Disney Parks rely heavily on data collection to provide visitors with that special experience they dream of, in a seemingly worry-free zone. Before even entering a Disney park, guests can choose from different apps and devices to make their visit more convenient and personal. Although for some, the excessive planning is one of the reasons to avoid taking a Disney vacation, the different park apps can help organize your day or entire trip. Once you hit the park, the app allows you to see ride wait times, order and pay for food, and find the nearest bathroom. The MagicBand+ wrist device — available in the U.S. and some international locations like the U.K. — can save your credit or debit card, tickets, and reservations, so you never have to reach for a wallet or your phone. The catch is, all of these tools can collect data, including rides you take and purchases you make, so Disney can track what's popular and profitable, as well as tailor your in-app park experience by knowing your past preferences and choices. Depending on how you use them, these apps and devices hold all of that data plus info like your name and address. Disney parks utilizes various technologies — including biometric fingerprints, face scanning, and GPS — to shorten lines, prevent ticket fraud, and display in-app walking directions around the park by knowing your specific location (much like Google Maps would).
In 2001: A Space Odyssey, a mysterious monolith appears before a group of our ape-like ancestors. Startled by the alien device, a few brave individuals approach it cautiously at first, and eventually the whole troop gathers around to investigate. Now, thanks to primatologists from Emory University and computer scientists from the Georgia Institute of Technology, capuchin monkeys in a Costa Rican forest preserve have had a similar, real-life experience. The researchers recently published their findings in the American Journal of Primatology. The team started by building a homebrew AI-powered facial recognition program called CapuchinAI. They then installed it on a computer outfitted with a camera and touchscreen, loaded it in a protective DIY box, and included a dispenser to dole out dried forest banana slices.
What's the deal? Plinq, a Brazilian startup that lets women check the criminal and judicial records of potential partners, has closed a R$1.3 million ($240,000) seed round. Spectra Investments led, with angel investors Anton Osika, Liana Selles and Fernando Montero participating. What's next? The funding will support the launch of Plinq's mobile app, planned for early September 2026, and the expansion of its 10-person team. Founder and CEO Sabrine Matos said the launch will be the company's first major product release. The app will allow women to enter a name and phone number and receive a report based on public records, including court databases and official gazettes. It will use facial recognition to restrict access to women. Why now? The round was announced on 7 August 2026, the 20th anniversary of Brazil's Lei Maria da Penha, a landmark law addressing violence against women. Plinq says 10% of its risk alerts relate to domestic-violence records, including 433 mentions of protective orders and 219 references to domestic violence. Plinq plans to move from one-off web searches to a monthly subscription after the app launches. It is also exploring advertising and contracts with state governments for violence-prevention technology. Read more: Startups.com.br Image credit: Generated with Gemini 41
Abstract The increasing demand for personalized skincare solutions highlights a significant gap: many consumers struggle to find suitable products without professional guidance. While the commercial potential for tailored product recommendations is vast, a key challenge remains the lack of effective methods for skin profiling via image classification. To address this challenge, this paper introduces a comprehensive benchmark dataset of 3203 standardized facial consumer selfies, annotated across eight primary cosmetic skin features. We establish a transparent baseline evaluation utilizing the open-source medical image classification framework AUCMEDI. A variety of deep learning architectures were trained to recognize and classify various skin features, including sagging skin, wrinkles, under-eye circles, redness, shine, pigment spots, acne, and pore size. The baseline model’s performance was evaluated using the mean absolute error (e), which appropriately accounts for the ordinal distance in cosmetic grading. Utilizing standard deep learning architectures, the baseline benchmark established promising performance in structural categories like sagging skin (\(e = 1.71\)) and wrinkles (\(e = 1.40\)). While achieving satisfactory results for under-eye circles (\(e = 0.32\)), redness (\(e = 2.22\)), and shine (\(e = 0.23\)), the baseline models encountered significant architectural limitations when resolving highly localized or imbalanced features such as acne (\(e = 2.59\)), pore size (\(e = 2.57\)), and pigment spots (\(e = 2.20\)), the latter three underperforming a trivial constant-mean predictor. Similar content being viewed by others Subjects Introduction Deep learning algorithms, particularly convolutional neural networks, have emerged as a dominant force in medical image analysis1,2,3. Their superior performance in tasks like image classification, segmentation, and object detection has significantly advanced the field1,2,3. Medical image classification (MIC), a fundamental task involving the categorization of medical images into predefined classes (e.g., health status, tumor type), is a prime application area for these models. Accurate MIC can support clinical decision-making and improve diagnostic efficiency4,5.
Cabot will cease use of its Flock Safety license plate readers pending a review of the technology after residents expressed concerns about privacy, the mayor said. The suspension went into effect immediately, Mayor Ken Kincaid said in a Facebook post Thursday night announcing the decision. He didn’t give a timetable on when the “comprehensive review” would be completed or who would work on it, but said city officials would seek input from local leaders and residents. “As your Mayor, I firmly believe that public safety and civil liberties must never conflict; both must be protected equally,” Kincaid said. The post doesn’t say how many of the license plate readers the city has, and a lieutenant at the Police Department referred all questions about the system to City Hall. Kincaid didn’t respond Friday to messages left at his office. Flock license plate reader cameras capture images of passing cars and log license plate numbers, comparing the captured plate numbers against so-called hot lists assembled by the agencies that operate them. The company’s website states the cameras don’t use facial recognition software but boast the ability to search captured images based on a description — for example, a vehicle towing a skid steer on a trailer — when a plate number isn’t known. Many cities in Arkansas use the cameras, including Little Rock, North Little Rock, Conway, Hot Springs, Benton, Bryant, Jacksonville and Sherwood. Nationally, more than 5,000 law enforcement agencies have bought Flock’s license plate readers, the company’s website states. Kincaid, who said he’s the son of a police officer, credited the cameras as a useful investigative tool to help police locate stolen vehicles and “assist in critical public safety alerts.” The decision in Cabot comes less than two weeks after Pea Ridge police Chief Lynn Hahn announced that his Police
A smart lock that recognizes your face sounds like a party trick until your hands are full of groceries, a sleeping child, or a muddy garden hose. I don’t need my front door to feel futuristic. I need it to open for the right person, keep working when the phone stays inside, and give guests a backup route that doesn’t turn into a support call. That makes face unlock a useful option, not an automatic upgrade. The right comparison isn’t face versus old fashioned keys. It’s face unlock versus a keypad, fingerprint reader, phone, watch, and physical key working as a complete access system. Facial recognition earns its place only when it removes a daily step without creating a new failure at the door. Face recognition solves one specific problem The strongest case for facial recognition is device-free access. A phone-based system asks you to carry a phone, keep the app available, or rely on geofencing. A keypad asks you to remember a code and find the right digits. A fingerprint reader asks you to touch a sensor with a clean, uncovered finger. Face unlock removes that deliberate action. You approach, the lock scans, and the door opens. Lockly says Visage uses dual infrared sensors for facial recognition. It encrypts facial-recognition data and stores it on the device rather than sending it to a server. Its product page also lists built-in Wi-Fi, Apple Home Key, a PIN Genie keypad, fingerprint access, app control, voice control, and a physical key. That backup list is the key design choice. A face scanner can’t be the only way into a home. Lighting, sunglasses, hats, a changed appearance, a dirty sensor, or a failed battery can interrupt the experience. A good lock treats biometrics as one route in a wider access plan. Why backups