https://news.ycombinator.com/Links for the intellectually curious, ranked by readers.https://krebsonsecurity.com/2026/07/read-this-before-you-buy-that-tv-streaming-stick/Thu, 30 Jul 2026 17:04:53 +0000https://news.ycombinator.com/item?id=49112744<a href="https://news.ycombinator.com/item?id=49112744">Comments</a><div class="entry-content" morss_own_score="5.747609942638624" morss_score="99.11377774450263"> <p>Security experts have been sounding the alarm for years about the risks of using generic TV boxes that promise unlimited content streaming for a one-time fee, warning that they secretly rent the user’s Internet connection out to strangers. But a groundbreaking new analysis finds these devices also routinely spoof themselves as mobile phones clicking ads on AI-generated websites as part of sprawling operation that seeks to defraud online merchants and advertising networks.</p> <p><strong>Pedro Falé </strong>is a threat researcher with the security firm <strong>Bitsight</strong>. Falé told KrebsOnSecurity he was able to peer inside a vast and complex ad fraud network by registering an expired domain name that was used to coordinate fake ad clicks across a particularly popular brand of these streaming devices known as <strong>H96</strong>.</p> <img src="https://krebsonsecurity.com/wp-content/uploads/2026/07/h96-amazon.png"><p>An H96 TV streaming device currently advertised for sale on Amazon.</p> <p>Falé said the domain he scooped up was previously used for telemetry, periodically collecting full hardware information and the entire list of installed apps from tens of thousands of H96 streaming sticks plugged into television sets around the globe. But upon inspecting the traffic being funneled to the domain, he discovered nearly all of the TV boxes transmitting data claimed to be mobile phone models from a variety of manufacturers, including Samsung, Vivo, Huawei, and Xiaomi.</p> <p>“We noticed something was wildly wrong,” Falé said. “Multiple devices reporting to this factory Android TV Box backdoor were ‘phones.'”</p> <img src="https://krebsonsecurity.com/wp-content/uploads/2026/07/h96-shipspreinfected.png"><p>Image: Bitsight.</p> <p>The researcher found all of the devices reported having the same two apps installed, and that those apps were made by a company called <strong>Zhejiang Fengwo IoT Technology Ltd</strong>, an entity founded in 2019 in mainland China which operates an ad-publishing portfolio under the name <strong>Fengwo Group</strong>. Further investigation into the Fengwo Group revealed it has registered multiple patents that match the inner workings of these apps.</p> <p>“Bitsight TRACE identified several Hong Kong, Singapore, and single person ‘legal’ shell identities used to collect the monetization and traced the operation back to a mainland China company known as Zhejiang Fengwo IoT Technology Co., Ltd, which operates under the Fengwo Group,” Falé <a href="https://www.bitsight.com/blog/fuyao-enterprise-building-ad-fraud-empire-ai-and-kids-coding-blocks">wrote</a> in a report released today about their findings.</p> <p>Falé said an analysis of the apps shows they help to coordinate an ad fraud network that uses these H96 devices as a captive traffic source to click on ads at AI-generated websites operated by the Fengwo Group.</p> <p>Bitsight discovered the websites contain machine-generated news articles and graphics across a range of categories, including finance, health, education, gaming, music and food blogs. But they also found none of those sites displayed ads unless the device visiting the page matched the spoofed mobile profile of these H96 devices.</p> <h2>AI DIGITAL HUMANS</h2> <p>The domain for the Fengwo Group — fwgcloud[.]com — claims the company is “redefining the boundaries of human-AI interaction,” and that it has created more than 120,000 “AI digital humans” available to rent for everything from emotional companionship to 24/7 customer service and creative design.</p> <img src="https://krebsonsecurity.com/wp-content/uploads/2026/07/fwgcloud-dot-com.png"><p>The homepage for fwgcloud dot com.</p> <p>Falé said the Fengwo Group’s domain shared its SSL certificate data with other domains associated with the apps found on H96 devices, specifically the phone spoofing mechanism. He noted the domain also has an internal wiki platform that directly ties the Fengwo Group to a proprietary implementation of a Google-built visual programming language called <strong>Blockly</strong>, which was originally designed to help kids learn how to write software.</p> <p>According to Bitsight, the Fengwo Group’s employees use Blockly to build the sham websites, allowing low-skilled operators to drag blocks of code together in their Blockly editor — without any need to understand what the underlying code blocks do or how they work.</p> <img src="https://krebsonsecurity.com/wp-content/uploads/2026/07/tryblockly.png"><p>The Blockly homepage.</p> <p>“An operator can drag blocks together in their Blockly editor, to define each fraud routine, given a task type,” reads Bitsight’s report. “Once the routine is saved, it gets exported as JavaScript and uploaded to the S3 buckets. An operator doesn’t need as much understanding of the underlying technicalities, as it is all set in place for ease of use.”</p> <p>Bitsight even found one of the Fengwo Group app developers mentioning exactly these advantages, noting the developer remarked that “only a small number of highly-skilled developers are needed to build the template execution-unit images,” and that “developers who create execution units from those templates have significantly lower technical requirements, greatly reducing the company’s operating costs.”</p> <p>Falé said if a user’s H96 streaming stick is selected for a specific fraud task, it will be pushed the appropriate Blockly module according to the task desired, which can include silently launching a web browser, visiting websites, browsing pages, managing tabs, and clicking on ads.</p> <p>To ensure the TV boxes masquerading as mobile phones can reliably click on ads displayed via the AI-generated websites, the Fengwo group “fuses three vision and reasoning systems into a single interface,” allowing the bots to correctly identify an ad on the webpage and navigate the site much like a human would, the Bitsight report observed.</p> <img src="https://krebsonsecurity.com/wp-content/uploads/2026/07/fengwogroupwebsites.png"><p>Examples of ad landing pages linked to the Fengwo Group. Image: Bitsight.</p> <h2>TV ON? PROXY. TV OFF? AD FRAUD</h2> <p>Bitsight found the H96 devices were either relaying residential proxy traffic or participating in ad fraud, but never both at the same time. In fact, they concluded that when these TV boxes detect an HDMI signal from an attached television — indicating the user intends to stream video content — the box is usually functioning as a residential proxy. When the TV is off, it switches back to waiting for ad fraud jobs.</p> <p>Falé said he believes the TV boxes are set up this way because its ad fraud activities are far more resource intensive and could interfere with the device’s stated purpose — streaming video content over the Internet.</p> <p>Despite repeated <a href="https://www.fbi.gov/investigate/cyber/alerts/2025/home-internet-connected-devices-facilitate-criminal-activity">warnings from the FBI</a> and security industry leaders about the security and privacy risks of using these streaming devices, major e-commerce providers like Amazon, Best Buy, Newegg and others continue to sell hundreds of different models and brands that bundle unofficial versions of Google’s Android operating system and are frequently marketed (<a href="https://krebsonsecurity.com/2025/11/is-your-android-tv-streaming-box-part-of-a-botnet/">via online influencers</a>) as a way to access a broad array of streaming services and live broadcasts without a subscription.</p> <img src="https://krebsonsecurity.com/wp-content/uploads/2026/07/fbi-iot-warning-tvboxes.png"><p>Image: fbi.gov.</p> <p>In addition to enlisting the user’s TV box in ad fraud networks, these off-brand streaming devices almost universally come with <strong>residential proxy</strong> software pre-installed. This software rents the user’s Internet address out to anonymous paying customers, who run the gamut from aggressive content scraping firms to ticket scalpers and outright cybercriminals.</p> <p>What’s more, because these generic (and generally dirt cheap) TV boxes are all horribly insecure by default and bereft of any kind of authentication, installing one on your home or office network only invites further mischief. In January, the proxy tracking service <strong>Synthient</strong> documented how multiple botnets had <a href="https://krebsonsecurity.com/2026/01/the-kimwolf-botnet-is-stalking-your-local-network/">rapidly enslaved millions of TV boxes</a> using a complex interplay of security vulnerabilities in both the residential proxy software and the streaming devices themselves.</p> <h2>SHOW ME THE MONEY</h2> <p>Bitsight said it tracked approximately 38,000 TV boxes globally phoning home to the expired Fengwo Group domain, and based on that number the report estimates this ad fraud network brings in revenues of close to $50,000 a day (not counting substantial revenue from the residential proxy side of the business). However, Falé emphasized that these estimates are highly conservative and based on telemetry from just one of the Fengwo Group’s core (but older) domains.</p> <p>As for the Fengwo Group’s claim to have 120,000 “digital humans” at their disposal, Bitsight’s report concludes it could be just a clever marketing scheme and/or a way to avoid drawing suspicion to the company’s operations.</p> <p>“Historically, when dealing with proxy services or DDoS, we sometimes see these websites undertake inconspicuous facades, so as not to advertise their DDoS capability or botnet size,” Falé wrote in the report. “This could also be the case here.”</p> <p>If the Fengwo Group truly does have tens of thousands of “AI humans” at its beck and call, it does not appear to have dedicated any of them to fielding inquiries from its own website. KrebsOnSecurity sought comment from the Fengwo Group by emailing the contact address listed on the company’s homepage, but the request bounced back with the reply, “Your message couldn’t be delivered to postmaster@fwgcloud[.]com. Their inbox is full, or it’s getting too much mail right now.”</p> <p>As Bitsight’s analysis shows, when it comes to TV boxes and streaming sticks, it’s best to stick to name brands from reputable manufacturers, and then to be sparing and careful with any apps you choose to install on the device — as <a href="https://krebsonsecurity.com/2026/07/lg-to-ban-residential-proxies-from-smart-tv-apps/">many of those can bundle residential proxy software as well</a>. Google says consumers can confirm whether or not a device is built with the official Android TV OS and Play Protect certification by following <a href="https://support.google.com/googleplay/answer/7165974">these instructions</a>.</p> <p>Additionally, Synthient maintains <a href="https://github.com/synthient/public-research/blob/main/2026/01/kimwolf/product_names.csv">a running list of IoT devices</a> that have been known to ship to consumers with residential proxy software and other malicious apps pre-installed. Careful readers will notice Synthient’s list includes other IoT devices apart from streaming sticks and boxes: As the FBI has warned, residential proxy software has also been found in other popular consumer IoT devices from random brands, particularly digital photo frames.</p> </div> https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/Thu, 30 Jul 2026 15:15:48 +0000https://news.ycombinator.com/item?id=49111237<a href="https://news.ycombinator.com/item?id=49111237">Comments</a><main id="page-content" morss_own_score="5.48289649044869" morss_score="44.261264111751"><span> July 30, 2026 </span><span>Models</span><h1>Gemini Robotics 2 brings whole body intelligence to robots</h1> <div>Share</div><p>From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks</p><p>For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Now, that vision takes a significant stride forward.</p><p>Most robots are pre-programmed or teleoperated for narrow, repetitive task sequences. They lack the ability to truly learn for themselves or adapt to unpredictable environments. Moreover, transferring learned skills from one robot body to another remains incredibly difficult. To take on the hardest problems at scale, robots of every shape and size need AI models giving them the ability to think, act, and interact intelligently to safely complete tasks.</p><p>We demonstrated how Gemini's multimodal understanding could drive real-world action with <a href="https://deepmind.google/models/gemini-robotics/">Gemini Robotics</a>. Today, we are introducing Gemini Robotics 2 - the intelligence layer powering the next generation of truly adaptable robots. As it takes its first literal steps, this major advance unlocks intelligent whole-body control, advanced dexterity, and multi-robot collaboration.</p><p>Gemini Robotics 2 enables robots to reason through every movement, unlocking a broad range of tasks. For example, it can enable a humanoid to walk, crouch, stretch, and manipulate objects to clean up a cluttered room. It can even team up with other robots to finish the job faster. And this profound intelligence can also run locally on-device while seamlessly adapting to entirely new robotic bodies in just a few hours.</p><p>We are making this possible through three highly capable models:</p><ul><li><a href="https://deepmind.google/models/gemini-robotics/vla/">Gemini Robotics 2</a>: Our most advanced vision-language-action model (VLA) that converts vision and language input into motor control, enabling a robot to take action. This model is capable of controlling full humanoids, from feet to fingertips, and other bi-arm robots. It also brings a new level of dexterous manipulation on both hands and grippers.</li><li><a href="https://deepmind.google/models/gemini-robotics/embodied-reasoning/">Gemini Robotics ER 2</a>: Our most capable embodied reasoning (ER) model. It is a vision language model (VLM) that acts as our agent, enabling robots to communicate with humans, understand the physical world and plan multi-step tasks lasting several minutes. We are also introducing the ability for robots to work together as a team.</li><li><a href="https://deepmind.google/models/gemini-robotics/on-device/">Gemini Robotics On-Device 2</a>: Our most efficient vision-language-action model (VLA) optimized to run locally on robotic devices. This model can now achieve fast adaptation to completely new robot embodiments with a few hours of data.</li></ul><div>Slide 1 of 3</div><figure><figcaption><p>Gemini Robotics 2 controlling three different embodiments, using the same model checkpoint — the Apptronik Apollo 2 robot with SharpaWave hands, the Apollo 2 robot with Inspire hands, and the Franka Duo with the Robotiq gripper — on a wide variety of whole-body and dexterous manipulation tasks. Each bar represents the average success rate over multiple tasks within the same skill category. For multifinger tasks we show individual task performance. While Gemini Robotics 2 achieves a medium to high success rate for whole-body and gripper-based dexterous tasks, the multi-finger dexterous manipulation remains challenging.</p></figcaption></figure><p>Gemini Robotics ER 2, our reasoning model, is now available on <a href="https://ai.dev/prompts/new_chat?model=gemini-robotics-er-2-preview">Google AI Studio</a> and in private preview on <a href="https://console.cloud.google.com/agent-platform/publishers/google/model-garden/gemini-robotics-er-2-preview-info">Gemini Enterprise Agent Platform</a>. Our VLA and On-Device models are available to <a href="https://docs.google.com/forms/d/1sM5GqcVMWv-KmKY3TOMpVtQ-lDFeAftQ-d9xQn92jCE/viewform?ts=67cef986&amp;edit_requested=true">early-access partners</a>. Read how to bring these models to your hardware on our <a href="https://blog.google/innovation-and-ai/models-and-research/google-deepmind/gemini-robotics-er-2/">Developer blog</a>.</p><h2>Humanoids in motion: Managing whole-body tasks</h2><p>The world is built for human movements; it requires us to reach, bend, and balance in tight, cluttered spaces. While our previous models controlled the humanoid’s upper-body to achieve table-top tasks, Gemini Robotics 2 expands physical AI into whole-body motions.</p><p>For the first time, our model can now control entire humanoid robots, translating intent into intelligent whole-body control. For example, when controlling <a href="https://apptronik.com/apollo/apollo-2">Apptronik’s Apollo 2</a> humanoid robot, we can ask it to <em>“put the watering can into the green bin in the bottom shelf.</em>” Apollo processes the instruction, walks to the table, and picks up the watering can, takes a few steps to the shelves, and places it precisely in its destination. While our robots have more to advance in movement speed, this is an important step towards the skills needed to complete more complex, real-world tasks that require whole-body coordination.</p><h2>Bringing advanced dexterity to hands and grippers</h2><p>To be genuinely useful in our homes and workplaces, robots need finesse. Gemini Robotics 2 unlocks a new level of physical dexterity across different end effectors, whether a robot is using hands or grippers, enabling robots to be more useful than ever before.</p><p>The model can now control the five-fingered, 22 degree-of-freedom SharpaWave hand on the Apollo 2 robot to complete delicate actions like tying knots or sealing a ziplock bag. It can also operate standard two-fingered parallel grippers on a <a href="https://franka.de/fr3-duo">Franka Duo platform</a> to perform complex dexterous tasks (e.g. tight packing). We are continuing to advance the level of precision and speed to achieve human-level dexterity.</p><h2>Unlocking advanced tasks with agentic reasoning and multi-robot collaboration</h2><p>Most real-world tasks require multiple steps over an extended period of time. To manage this complexity, our embodied reasoning (ER) model, Gemini Robotics ER 2, serves as the robot’s high-level brain, processing user instructions and communicating with humans. It observes the room, reasons about the steps needed to complete the task, coordinates with the VLA to carry out the actions, and tracks progress until the task is done. This setup allows robots to execute complex multi-step tasks, self-correct if a step fails, and generalize to novel situations and goals.</p><p>In this update, we are enabling robots to more reliably execute longer task sequences, lasting several minutes and involving hundreds of decisions. Gemini Robotics ER 2 now understands when tasks begin and end, and can pinpoint the moment key events occur, marking a step change in progress understanding.</p><p>Furthermore, we are introducing multi-robot collaboration. This enables different types of robots to communicate and work together to solve complex workflows a single robot could not do alone.</p><h2>Adapting fast on-device models for any robot</h2><p>Many robotic applications need to operate without network latency or internet connectivity. Gemini Robotics On-Device 2 is built specifically to handle these constraints — it is our most-efficient vision-language-action model (VLA) optimized to run locally on robotic devices.</p><p>This model is natively multi-embodiment and inherits our advanced “motion transfer” techniques from <a href="https://deepmind.google/blog/gemini-robotics-15-brings-ai-agents-into-the-physical-world/">Gemini Robotics 1.5</a>. We can now adapt to new bi-arm robot embodiments with just a few hours of adaptation time, typically with less than 200 examples. This works even with new embodiments with drastically different shapes, sensors and degrees of freedom, as shown below with a diverse set of tasks being performed by the Dexmate, SO101, and Trossen platforms.</p><h2>Advancing our commitment to safe and responsible robotics</h2><p>Safety is foundational to our robotics research. As robots gain more physical capabilities, we are committed to ensuring end-to-end safety and alignment. With each release, we’ve taken a multi-layered approach that combines traditional physical safety measures with robust AI safety frameworks.</p><p>Gemini Robotics 2 specifically advances robotics safety for navigating the uncertainty of the real world and collaborating alongside humans.</p><p>We’re introducing <a href="https://huggingface.co/datasets/google/asimov_agentic/blob/main/README.md">ASIMOV-Agentic</a>, a new benchmark for agentic safety orchestration and uncertainty resolution. For example, it measures the embodied reasoning agent’s ability to refuse unsafe tool calls from a VLA.It also measures the agent’s ability to predict whether a task is possible and to proactively request human intervention when uncertain.</p><p>Additionally, with enhanced embodied reasoning, Gemini Robotics ER 2 is our safest robotics model to date in safety constraint following and human proximity benchmarks. It can better detect when humans are nearby, trigger safety tool calls and bring the robot to a safe stop if someone approaches too closely. This is a key requirement in collaborative safety standards. Read our <a href="https://storage.googleapis.com/deepmind-media/gemini-robotics/Gemini-Robotics-2-Safety.pdf">Gemini Robotics 2: Safety Technical Report</a> for more details.</p><h2>Building towards general-purpose physical AI</h2><p>Gemini Robotics 2 marks an important milestone on the path toward solving AGI in the physical world. Unlocking the true potential of robotics requires moving past single-task automation toward general-purpose intelligence. By building this core intelligence, our goal is to enable AI in the physical world that can work alongside humans to solve complex challenges.</p><p><strong>Explore Gemini Robotics 2</strong></p><p><strong>Acknowledgements</strong></p><p>This work was developed by the Gemini Robotics team: Abhijit Ogale, Abhishek Jindal, Adil Dostmohamed, Adrian Collister, Alan Thompson, Alessio Quaglino, Alex Bewley, Alex Hofer, Alex Taeho Kim, Alex X. Lee, Alex Zihao Zhu, Allen Chai, Amaris Paryag, Amit Hampaul, Amy Nommeots-Nomm, Amy Shen, Andre Araujo, Anirudha Majumdar, Anna Volosina, Annie S. Chen, Annie Xie, Anthony Brohan, Antoine Laurens, Arunkumar Byravan, Asaf Revach, Assaf Hurwitz Michaely, Baruch Tabanpour, Ben Moran, Benoit Landry, Bingyi Cao, Bogdan Mazoure, Brandon Hernaez, Brijen Thananjeyan, Bryan Anenberg, Caden Lu, Carl Doersch, Carolina Parada, Charles Shu, Chengda Wu, Christine Chan, Christy Koh, Chuyuan Fu, Claire Cui, Clare Lee, Claudio Fantacci, Connor Schenck, David Rendleman, Deepali Jain, Demetra Brady, Dennis Li, Dhruv Shah, Dimple Vijaykumar, Dirk Ehrlich, Divya Garikapati, Dmitry Kalashnikov, Dre Mahaarachchi, Dushyant Rao, Erik Frey, Fangchen Liu, Francesco Romano, Frankie Garcia, Gabor Simko, Gautam Salhotra, Giulia Vezzani, Grace Popple, Grace Vesom, Graziano Misuraca, Guangyao Zhou, Hagen Soltau, Hanzi Mao, Hao-Tien Lewis Chiang, Harris Chan, Hila Noga, Howard Zhou, Ian Storz, Idan Lev-Yehudi, Ignacio Rocco, Inessa Konstanz, Isaac Reid, Ishita Prasad, Ivan Kapelyukh, J. Chase Kew, Jacky Liang, Jake Varley, James Susilo, Jasmine Hsu, Jerad Kirkland, Jeremy Plassmann, Jessica Lo, Jie Tan, Jimmy Yan, Jingwei Zhang, Jinyu Xie, Jose Enrique Chen, Joshua Ainslie, Joss Moore, Juanita Bawagan, Junkyung Kim, Justin Lidard, Kanishka Rao, Kathryn Quinn Shea, Kaustubh Sridhar, Keerthana Gopalakrishnan, Ken Caluwaerts, Kenneth Oslund, Khimya Khetarpal, Konstantinos Bousmalis, Krista Reymann, Krzysztof Choromanski, Ksenia Konyushkova, Kun Zhang, Kunal Aneja, Laura Graesser, Leen Verburgh, Leonard Hasenclever, Li-Heng Lin, London Chappellet-Volpini, Lucie Kerley, Maria Attarian, Maria Bauza Villalonga, Marissa Giustina, Max McCabe, Meet Kirankumar Dave, Mehdi S. M. Sajjadi, Metin Tokosz-Exley, Michael Neunert, Michael Noseworthy, Michiel Blokzijl, Miguel Rivas, Mithun George Jacob, Mitsuhiko Nakamoto, Mo Dawoud, Mohan Kumar Srirama, Mohit Sharma, Mohit Shridhar, Muinat Abdul, Murilo F. Martins, Nathan Batchelor, Nicolas Heess, Niko Milonopoulos, Norman Di Palo, Oliver Groth, Ouais Alsharif, Padmini Copparapu, Parth Parekh, Paul Ruiz, Paul Wohlhart, Peide Huang, Peng Xu, Peter Pastor, Petko Yotov, Phil Duffy, Philemon Brakel, Rachel Sterneck, Rajkumar Vasudeva Raju, Ravin Kumar, Razvan Surdulescu, René Wagner, Reza Sanatinia, Robert Baruch, Robert Moreno, Rohan Thakker, Roland Hafner, Sajjad Zafar, Sally Jesmonth, Sam Haves, Saminda Abeyruwan, Sandy Han Huang, Scott Crowell, Seliem El-Sayed, Sergey Yaroshenko, Sergio Martinez Abad, Serkan Cabi, Sharath Maddineni, Shuang Li, Sichun Xu, Silvia Cruciani, Skanda Koppula, Skye Yang, Soo Sung, Stefan Welker, Stefani Karp, Stefano Saliceti, Steven Hansen, Stuart Bowers, Sumeet Singh, Svetlana Grant, Takahiro Miki, Takuma Yoneda, Thomas Buschmann, Thomas Lampe, Thomas Power, Thor Schaeff, Tim Hertweck, Tingnan Zhang, Todd McInally, Todor Davchev, Tong Zhao, Travers Rhodes, Tsang-Wei Edward Lee, Vika Koriakin, Vikas Sindhwani, Wenhao Yu, Wentao Yuan, Xiaolin Fang, Yahav Nussbaum, Ying Sheng, Ying Xu, Yuheng Kuang, Yuxiang Yang, Yuxiang Zhou</p><p>For their leadership and support of this effort, we’d like to thank: Jean-Baptiste Alayrac, Zoubin Ghahramani, Koray Kavukcuoglu and Demis Hassabis. We’d like to recognize the many teams across Google and Google DeepMind that have contributed to this effort including Legal, Marketing, Communications, Responsibility and Safety Council, Responsible Development and Innovation, Policy, Strategy and Operations, and our Business and Corporate Development teams. We’d like to thank everyone on the Robotics team not explicitly mentioned above for their continued support and guidance. Finally, we’d like to thank our partners: Apptronik, Boston Dynamics, and Agile Robots teams for their support.</p><article><h3>Gemini Robotics</h3><a href="https://deepmind.google/models/gemini-robotics/"> <div>Learn more</div> </a><source type="image/webp"><img src="https://lh3.googleusercontent.com/QetWez9OHWvris-YT-8xvXSoLkDG975oWnx4N_zTaxVrLSn84xgZeE34qbPMT6L3ZX9YhbiNaOjBGi_bR3Fl92kP3vAB1_etc1-I1e2xuVHu-kJ3KxA=w2464-h2464-n-nu"></source><source type="image/webp"><img src="https://lh3.googleusercontent.com/QetWez9OHWvris-YT-8xvXSoLkDG975oWnx4N_zTaxVrLSn84xgZeE34qbPMT6L3ZX9YhbiNaOjBGi_bR3Fl92kP3vAB1_etc1-I1e2xuVHu-kJ3KxA=w2464-h2464-n-nu"></source></article><article><h3>Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration</h3><time>July 2026</time><span>Models</span><a href="https://blog.google/innovation-and-ai/models-and-research/google-deepmind/gemini-robotics-er-2/?utm_source=deepmind.google&amp;utm_medium=referral&amp;utm_campaign=gdm&amp;utm_content="> <div>Learn more</div> </a><source type="image/webp"><img src="https://lh3.googleusercontent.com/rqQt2pJ2zKizyUuFJo7xLJt0-7UPpiefAtFVnTflDRmF0HbqTFZUhVqmyQ_kgZPlJZVRy6xoItAULvSKzHOcdUmrhzq8RiCzzWAnxWflo9XeStIM6w=w704-h704-n-nu"></source><source type="image/webp"><img src="https://lh3.googleusercontent.com/rqQt2pJ2zKizyUuFJo7xLJt0-7UPpiefAtFVnTflDRmF0HbqTFZUhVqmyQ_kgZPlJZVRy6xoItAULvSKzHOcdUmrhzq8RiCzzWAnxWflo9XeStIM6w=w704-h704-n-nu"></source></article><article><h3>Gemini Robotics-ER 1.6: Powering real-world robotics tasks through enhanced embodied reasoning</h3><time>April 2026</time><span>Models</span><a href="https://deepmind.google/blog/gemini-robotics-er-1-6/"> <div>Learn more</div> </a></article><article><h3>Gemini Robotics 1.5 brings AI agents into the physical world</h3><time>September 2025</time><span>Models</span><a href="https://deepmind.google/blog/gemini-robotics-15-brings-ai-agents-into-the-physical-world/"> <div>Learn more</div> </a></article><article><h3>Gemini Robotics brings AI into the physical world</h3><time>March 2025</time><span>Models</span><a href="https://deepmind.google/blog/gemini-robotics-brings-ai-into-the-physical-world/"> <div>Learn more</div> </a></article></main>https://github.blog/changelog/2026-07-30-stacked-pull-requests-are-now-in-public-preview/Thu, 30 Jul 2026 16:26:16 +0000https://news.ycombinator.com/item?id=49112232<a href="https://news.ycombinator.com/item?id=49112232">Comments</a><div class="PostContent-main editorial-content-block js-table-of-contents-source" morss_own_score="5.181818181818182" morss_score="47.24431818181818"> html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd" <p>Stacked pull requests break large changes into small, reviewable pull requests. They’re an ordered series of pull requests that each represent focused layers of your change. With stacks, you can independently review and check each pull request, then merge everything together in one click. No more opening a single large pull request that takes forever to review, or splitting work across multiple branches you have to keep manually rebasing.</p> <blockquote><p> “We’ve been using GitHub stacked PRs for Next.js for the past few months. It has helped us introduce smaller individual changes while shipping larger features, making it easier to review PRs. – Tim Neutkens, NextJS lead, Vercel” </p></blockquote> <p>With stacked pull requests, teams can:</p> <ul> <li><strong>Keep large changes moving</strong> by reviewing short, narrowly scoped pull requests in parallel.</li> <li><strong>Maintain quality across every layer</strong> by using focused pull request reviews alongside existing branch protections to protect <code>main</code>.</li> <li><strong>Merge one, some, or all</strong> by landing an entire stack altogether or individual layers one at a time.</li> </ul> <p>And because stacked pull requests are built into GitHub, your existing reviews, checks, and merge requirements all work out of the box.</p> <blockquote><p> “The new Github Stacked PRs preview is incredible. Landing 5 stacked PRs directly to a merge queue all at once! A+++! This removes so much friction (and the gh cli tools + agent skill help a ton)” – John Resig, creator, jQuery </p></blockquote> <h2><a href="https://github.blog/changelog/2026-07-30-stacked-pull-requests-are-now-in-public-preview/#get-started-with-the-cli-extension">Get started with the CLI extension</a></h2> <p>Install the CLI extension and create your first stack in under a minute:</p> <pre><code>gh extension install github/gh-stack </code></pre> <h2><a href="https://github.blog/changelog/2026-07-30-stacked-pull-requests-are-now-in-public-preview/#create-stacks-from-your-terminal-or-github-com">Create stacks from your terminal or github.com</a></h2> <p>Work with stacks on github.com, the GitHub CLI, the GitHub mobile app, or with a coding agent such as GitHub Copilot using the gh-stack skill. Start with a branch and pull request for your first change. Then add branches and pull requests on top of it; each pull request targets the layer below it.</p> <h2><a href="https://github.blog/changelog/2026-07-30-stacked-pull-requests-are-now-in-public-preview/#review-each-layer-independently">Review each layer independently</a></h2> <p>Open any pull request in the stack to review only the diff for that specific layer. Use the stack map at the top of the pull request to see how the change you’re reviewing fits into the larger work. You and your teammates can each review different layers in parallel without blocking further work.</p> <blockquote><p> “AI has made TED’s developers dramatically more productive, but that created a new bottleneck: PRs were growing large enough that reviewers were struggling. Stacked PRs help to solve that. By breaking large changes into small, dependency-ordered pieces, review happens in smaller logical chunks – not just faster PR reviews, but more accurate ones. Stacked PRs tighten our feedback loop and help get stable code to ted.com faster.” – Andy Merryman, CTO, TED </p></blockquote> <p><img src="https://github.com/user-attachments/assets/0937148e-b9fd-4f5c-849a-8bc4499ea838"></p> <h2><a href="https://github.blog/changelog/2026-07-30-stacked-pull-requests-are-now-in-public-preview/#merge-everything-in-a-single-click">Merge everything in a single click</a></h2> <p>Merge the latest ready pull request to land it and every unmerged layer below it in one single operation. To land part of a stack, merge one or more lower layers—the pull requests above it stay open and automatically rebase and retarget. Your existing branch protections and required checks still govern what reaches <code>main</code>.</p> <blockquote><p> “A big change used to mean one giant PR nobody wanted to review. Now it’s a stack of small ones reviewers can actually follow, and the whole stack merges in one shot. It stopped feeling like a tool on top of GitHub and started feeling like GitHub.” – Mayank Saini, connectivity engineer, WHOOP </p></blockquote> <h2><a href="https://github.blog/changelog/2026-07-30-stacked-pull-requests-are-now-in-public-preview/#find-out-more-and-share-your-feedback">Find out more and share your feedback</a></h2> <p>Stacked pull requests are rolling out in public preview to all repositories over the coming days. <a href="https://docs.github.com/repositories/configuring-branches-and-merges-in-your-repository/configuring-pull-request-merges/managing-a-merge-queue?utm_source=changelog-merge-queue-docs&amp;utm_medium=changelog&amp;utm_campaign=stacked-prs-gtm-public-preview-2026">Merge queue</a> support for stacked pull requests is rolling out progressively over the coming weeks.</p> <p>For more information, check out the <a href="https://gh.io/stacks?utm_source=changelog-stacked-pr-docs&amp;utm_medium=changelog&amp;utm_campaign=stacked-prs-gtm-public-preview-2026">stacked pull requests documentation</a>, and share your feedback with us in the <a href="https://gh.io/stacks-feedback?utm_source=changelog-pr-stacks-feedback&amp;utm_medium=changelog&amp;utm_campaign=stacked-prs-gtm-public-preview-2026">stacks discussion</a>.</p> </div>