Custom silicon is stepping up to challenge NVIDIA's grip on the edge this morning, with new joint ventures and RISC-V platforms targeting low-latency robot control. In the data labs, foundation models are hitting new milestones by digesting massive troves of egocentric human video and raw internet footage to teach robots general-purpose movement.
Taiwanese electronics maker ASUS officially unveiled its proprietary humanoid robot, Gallop, on Friday, November 13, developed by its Physical AI Solutions Business Group. Driven by an onboard NVIDIA Jetson AGX Thor computer, Gallop utilizes a four-layer AI architecture spanning high-level planning, supervision, skill execution, and low-level control. In live pilot tests inside an ASUS electronics manufacturing service factory, the robot operates custom in-house tactile hands to package 15 distinct motherboard accessories, targeting a 99.5% task success rate within a 20-second window per unit. Customer pilots are scheduled for 2027 ahead of full commercial release in late 2028.
Why it matters
ASUS's entry into physical hardware highlights how major IT hardware conglomerates are converting internal manufacturing plants into test beds for general-purpose physical AI. Electronics packaging requires manipulating thin, flexible cables and small components that defeat traditional rigid industrial grippers. Proving high-yield assembly in brownfield factory lines provides ASUS with an in-house deployment roadmap before offering commercial units to third-party contract electronics manufacturers.
ASUS engineering leads state that pairing head-mounted human demonstration videos with synthetic simulation data allows the platform to achieve high accuracy without endless manual coding. Conversely, industrial automation critics caution that achieving a 99.5% success rate under lab conditions is vastly easier than maintaining continuous uptime across thousands of shift hours on live production floors.
Amazon formally rebranded its home security division as Ring Robotics on Thursday, November 12, introducing two autonomous hardware offerings: the $249.99 Ring Drone indoor flying camera and the $999.99 Ring Rover commercial security bot. Driven by an onboard AI Pilot vision engine, the indoor drone executes automated patrol flights with active subject tracking starting January 27, while the ground rover launches December 16. Both devices incorporate visible LED recording indicators and local sensor obstacle avoidance.
Why it matters
Rebranding Ring around autonomous drones and rovers marks a significant expansion of consumer robotics into everyday home security. Leveraging an established smart home software ecosystem lowers adoption friction for autonomous indoor flight. Mass commercial distribution of $250 indoor security drones intensifies competition across home automation hardware.
Amazon hardware leads argue that autonomous aerial and ground patrols resolve static camera blind spots across residential and commercial properties. However, consumer privacy advocates express concern over continuous indoor video mapping and autonomous tracking within domestic environments.
Following up on Robbyant's initial release of the LingBot-Touch tactile model earlier this week, the Ant Group subsidiary published further evaluation metrics for its 5.3-million-parameter variant. The model, which standardizes piezoresistive, magnetic, and optical sensor inputs into a single fingertip coordinate frame, was demonstrated unscrewing bottle caps on Sharpa Wave and XHand hardware, expanding its downstream manipulation capabilities.
Why it matters
Tactile data in robotics has remained deeply fragmented due to competing sensor technologies that require custom, hand-tuned calibration scripts for every new hand geometry. By establishing a unified coordinate space and releasing pre-trained tokens under an open license, LingBot-Touch enables researchers to drop tactile awareness into existing vision-language-action policies. This lowers the barrier for contact-rich assembly tasks where vision alone suffers from self-occlusion.
Robbyant developers contend that decoupling tactile perception from underlying hardware allows tactile learning to scale at the same pace as visual pretraining. However, robotics researchers note that while continuous coordinate mapping handles steady-state pressure, high-frequency slip dynamics during fast manipulation still require low-latency sensor-level processing.
Y Combinator-backed startup Cerulion open-sourced its zero-copy robot runtime on Friday, November 13, introducing native ROS 2 Jazzy and Lyrical compatibility alongside automated run recording to MCAP log files. The runtime isolates each computational node into a dedicated process and introduces verification exit codes that function as automated CI gates, catching system divergence prior to physical fleet deployment. Prebuilt binaries were published for Linux and macOS environments.
Why it matters
Debugging physical edge failures has long been compromised by asynchronous sensor timing jitter and non-deterministic execution across ROS 2 nodes. By converting physical runtime divergence into deterministic exit codes suitable for continuous integration pipelines, Cerulion brings standardized software engineering practices to physical AI development. This reduces testing cycles for hardware teams pushing frequent software updates to deployed fleets.
Cerulion maintainers state that deterministic log replay is essential for certifying autonomous software safety before pushing over-the-air updates to physical fleets. However, open-source developers note that full deterministic replay still depends on hardware-level timestamping across multi-camera and LiDAR sensor suites.
Following Xiaomi's release of the Xiaomi-Robotics-1 foundation model we tracked last week, the company published detailed architectural specs for the system on Friday, November 13. The pipeline combines a Qwen3-VL backbone with a Flow-Matching Diffusion Transformer to generate action chunks from its 100,000 hours of egocentric UMI training data, followed by 10,000 hours of cross-embodiment alignment. Benchmark trials showed the top 10.5-billion-parameter variant reaching a 79% manipulation success rate in unseen environments.
Why it matters
The findings present empirical proof that scaling pretraining on handheld egocentric video dramatically boosts policy generalization in unmapped physical environments. Bypassing direct teleoperation during initial model pretraining solves the primary data collection bottleneck that has throttled physical AI. For robot builders, this framework demonstrates that investment in low-cost human data collection rigs yields higher operational success than simply scaling neural network parameter counts.
The Xiaomi research team emphasizes that handheld UMI data collection democratizes training by enabling non-technical workers to record diverse real-world workflows without expensive robot hardware. Conversely, independent robotics engineers point out that while handheld data speeds up free-space trajectory learning, fine force control still demands physical alignment on real robot joints.
Researchers from Alibaba DAMO Academy, CUHK, and Hupan Lab detailed RynnVLA-Latent on Friday, November 13, introducing a 4-billion-parameter robot policy trained on 745.5 million latent-action descriptors derived from over 30,000 hours of unlabelled web video. Rather than relying on direct joint command logs, the model translates raw video frames into continuous 3D geometric scene representations that account for camera motion. In benchmark evaluations, the policy recorded a 98.8% success rate on the LIBERO manipulation suite and 88.6% on bimanual tasks in RoboTwin 2.0 without requiring hardware-specific pre-labeling.
Why it matters
RynnVLA-Latent shifts policy pretraining away from expensive teleoperation logs toward vast archives of unannotated internet video. Converting passive video into camera-compensated 3D spatial representations decouples foundational feature learning from specific robot hardware configurations. This workflow offers robotics teams a scalable pipeline to pretrain generalist manipulation skills before fine-tuning on physical arms.
The authors maintain that geometric latent actions bridge the gap between passive video observation and physical control without manual annotation. However, independent researchers note that converting latent 3D trajectories into low-level joint torques still requires downstream physical calibration to handle varying load dynamics.
A research team from Yale University and UC San Diego introduced 'SunLightBots' on Friday, November 13, detailing subgram soft robots powered entirely by ambient solar radiation without electronic components, onboard batteries, or wiring. Constructed from liquid crystal elastomers (LCEs) that combine photothermal conversion with mechanical actuation, the devices utilize a self-resetting snap-through instability mechanism to accumulate energy. Operating under 1 Sun illumination (100 mW/cm²), the untethered structures generate an actuation stress of 0.1 MPa and a peak mechanical power density of 89 W/kg, enabling swimming, crawling, and impulsive jumping motion.
Why it matters
Heavy onboard power supplies present a fundamental barrier to scaling subgram autonomous machines for long-duration environmental sensing. Demonstrating that material-level mechanical energy storage can convert ambient light into cyclic high-power mechanical work eliminates reliance on rigid batteries. This elastic actuation mechanism opens up new possibilities for deploying untethered micro-robot swarms across agricultural monitoring and environmental cleanup tasks.
The research authors highlight that embedding power harvesting directly into structural materials allows micro-robots to operate indefinitely without maintenance. Conversely, soft robotics engineers point out that relying on photothermal snap-through instability limits continuous motion control, making precise trajectory tracking difficult under varying light conditions.
Shanghai-based sensor startup Weite Robotics (ViTai Robotics) secured a Series A funding round in the hundreds of millions of yuan on Thursday, November 12, co-led by CRRC Capital and Yongxin Ark, alongside Luxin Venture Capital and Yinxinggu Capital. Spun out of MIT's CSAIL in 2024, the company manufactures vision-based tactile sensors that calculate dynamic grip force vectors and detect micro-slippage in real time. Weite confirmed that its tactile skins are currently deployed on Xiaomi's smartphone assembly lines to guide delicate component placement.
Why it matters
Heavy backing from major industrial funds demonstrates how tactile sensing hardware is becoming critical infrastructure as humanoids move onto assembly lines. Operating without high-resolution tactile feedback forces robotic manipulators to rely on conservative grip forces that slow cycle times or damage fragile components. Securing direct placement on Xiaomi's production lines confirms Weite's ability to manufacture robust sensor skins at commercial scale.
Weite's founders argue that optical tactile sensing delivers superior spatial resolution compared to resistive arrays, allowing manipulators to handle microscopic assembly tasks. Conversely, competing hardware developers contend that vision-based tactile sensors introduce higher compute overhead and camera bulk inside compact robotic fingertips.
Berlin-based Pi4_robotics debuted its 1.6-meter-tall Workerbot9 service platform at the Deutscher Pflegetag conference on November 12 and 13, prior to initiating live ward trials at Charité University Hospital. Developed under the MuRoC research program alongside Fraunhofer IPA, the robot integrates a dexterous gripper arm, multi-sensor navigation, and object-recognition software to automate ward supply restocking and non-contact vital sign monitoring. The project aims to directly reduce the estimated 15% of nursing shift hours currently consumed by manual logistics errands.
Why it matters
Hospital automation often struggles when mobile delivery carts cannot manipulate doors, drawers, or supply bins. Equipping service robots with articulated gripper arms allows Workerbot9 to handle complex restocking tasks directly within clinical wards. Successful trials at Charité provide a reference model for addressing severe clinical nursing shortages through targeted logistical automation.
MuRoC project leaders state that automating routine supply retrieval lets nursing staff devote more time to direct patient care. However, hospital operations managers note that integrating mobile manipulators into crowded, dynamic hospital corridors requires strict safety protocols to avoid disrupting emergency patient transport.
Robotics chip venture XILECT, established in May 2026 by Chinese GPU maker MetaX and humanoid robot builder UBTech, closed an RMB 500 million ($69M) angel funding round on Thursday, November 12. Co-led by GGV Capital and Matrix Partners China at an RMB 4 billion post-money valuation, the startup is developing a unified System-on-Chip (SoC) incorporating a dual 'big-and-small-brain' architecture. The silicon integrates GPU compute for multimodal perception and decision-making alongside real-time motor control loops on a single substrate, backed by a CUDA-compatible software toolchain. Partner evaluation platforms are scheduled for 2027, with full commercial tape-out planned for 2028.
Why it matters
XILECT's massive angel valuation highlights the aggressive drive within China to manufacture domestic hardware alternatives to NVIDIA's Jetson Thor platform amid ongoing U.S. semiconductor export restrictions. Splitting high-level VLA model reasoning and deterministic motor execution across separate boards introduces latency and power overhead that limits real-time dynamic balance in humanoids. By integrating both workloads onto one die, the platform aims to lower total bill-of-materials costs while providing UBTech and independent OEMs with tight hardware-software alignment.
XILECT founders argue that off-the-shelf automotive processors fail to address the specific low-latency motor feedback required by 40-plus actuator joints. However, semiconductor analysts point out that relying on parent company UBTech creates initial customer concentration risks, as competing humanoid builders may hesitate to adopt a chip controlled by a direct market rival.
Building on the Actus RISC-V platform reveal at electronica we covered yesterday, MIPS announced global shipments of three physical reference hardware modules—Actus-1, Acies-1, and Aegis-1—on Friday, November 13. Delivering up to 50 TOPS of edge AI acceleration within a 10-watt thermal budget, the Actus-1 module pairs dual MIPS Atlas M8500 real-time cores with an ARC vector processing unit. Concurrently, the Acies-1 M.2 expansion card leverages an Atlas S8200 compute engine to drive local Vision-Language-Action inference.
Why it matters
Transitioning from IP core licensing to physical reference hardware gives embedded engineering teams immediate hardware platforms to benchmark deterministic execution before committing to expensive ASIC tape-outs. Delivering 50 TOPS at 10 watts addresses the strict power and heat boundaries of compact robot joints. Standardizing motor control and neural inference on open RISC-V architectures provides builders with an alternative to proprietary ARM and x86 silicon stacks.
MIPS leadership asserts that integrating safety-critical real-time processing and AI vector acceleration onto FD-SOI silicon simplifies ISO 26262 compliance for autonomous machines. However, embedded software developers note that RISC-V compiler toolchains for VLA inference still require broader optimization to match NVIDIA's mature CUDA library ecosystem.
NXP Semiconductors launched the Ara240 neural processing unit on Friday, November 13, delivering up to 40 equivalent TOPS (eTOPS) of local AI acceleration for embedded vision and generative workloads. The discrete chip houses eight programmable neural network cores, dual RISC-V vector processing units, and supports up to 16GB of LPDDR4 memory within a 6-to-8-watt thermal envelope. Interfacing via PCIe Gen4, the processor includes a software development kit optimized for running Hugging Face models locally on industrial and mobile robotics platforms.
Why it matters
Executing vision-language models locally at 6 to 8 watts allows mobile robots to operate without continuous cloud connectivity or heavy thermal heat sinks. Adding dedicated RISC-V vector units alongside programmable neural cores accelerates local inference for edge devices. This chip provides embedded engineers with an energy-efficient compute block for running local spatial AI workloads.
NXP product architects state that local 40 eTOPS processing removes cloud transmission latency and secures operational privacy in sensitive industrial environments. However, systems integrators caution that 16GB LPDDR4 memory bandwidth may bottleneck larger multi-billion-parameter vision-language-action models during dense multimodal inference.
Following yesterday's news that GXO Logistics signed a Letter of Intent to scale Apptronik's Apollo humanoids to 1,000 units, new details emerged regarding the deployment timeline. The Robotics-as-a-Service (RaaS) agreement follows evaluations across 50 distinct logistics tasks, with multi-site commercial facility trials slated to commence in North America in late 2027. The rollout will focus on order picking, tote movement, packing, and sortation, subject to binding performance KPIs.
Why it matters
Expanding beyond single-site pilot tests, this LOI creates a clear operational pathway for scaling humanoid fleets across enterprise supply chains. Utilizing a RaaS commercial structure mitigates upfront capital expenditure for logistics providers while holding hardware suppliers accountable to strict uptime metrics. The multi-year roadmap establishes an industrial benchmark for evaluating whether bipedal humanoids can achieve cost parity with traditional fixed warehouse automation.
GXO executives assert that bipedal humanoids offer the flexibility needed for brownfield logistics facilities that cannot accommodate multi-million-dollar fixed conveyor infrastructure. However, logistics analysts emphasize that the 1,000-unit target remains strictly conditional on Apollo meeting rigorous throughput and mean-time-between-failure targets during live 2027 trials.
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Robust AI, co-founded by Rodney Brooks and Anthony Jules, detailed operational deployments of its Carter warehouse robot on Friday, November 13. Designed around a cart form factor with holonomic wheels and a force-sensitive handlebar, the autonomous vehicle allows warehouse workers to manually grab, steer, or redirect the machine mid-trajectory without stopping motors or triggering error locks. Carter splits fulfillment tasks between human pickers and autonomous transport, operating across active logistics and manufacturing facilities.
Why it matters
Rigid autonomous mobile robots often cause warehouse floor friction when workers must adapt to fixed machine paths. Incorporating physical force overrides and familiar cart ergonomics allows seamless human-robot collaboration without extensive retraining. This pragmatic approach offers logistics managers an immediate productivity boost without requiring full facility re-engineering.
Robust AI leadership contends that physical override capabilities respect natural worker workflows and eliminate safety shutdowns caused by unexpected human path crossings. Conversely, traditional warehouse automation engineers argue that manual intervention reduces overall system predictability compared to fully automated, isolated transport zones.
Advancing the magnetic microrobot research we've been tracking from ETH Zurich and Charité University Hospital, the engineering team filed a formal patent application for the technology on Friday, November 13. While earlier studies confirmed the 8-micrometer tumbling microbots dissolve clots six times faster than drugs alone, the new filing details live porcine trials that demonstrated doubled vascular restoration rates using the localized fluid currents.
Why it matters
Passive drug diffusion often fails when complete vascular blockages arrest localized blood flow, raising ischemia risks during acute strokes. Active magnetic somersaulting overcomes fluid transport barriers by churning therapeutics directly at the clot face. Advancing from large-animal porcine validation to formal patent filings marks a major step toward clinical translation for untethered medical microrobotics.
The ETH Zurich research team asserts that surface-rolling magnetic microrobots avoid high blood-velocity drag while delivering localized mechanical mixing. On the other hand, interventional radiologists emphasize that clinical adoption requires demonstrating complete biodegradable safety to ensure microbot remnants do not cause downstream microvascular embolizations.
Engineers at the University of Colorado Boulder introduced a class of self-propelled medical micro-bots on Friday, November 13, fabricated using high-precision 3D printing. Measuring five to ten times thinner than a human hair, the rocket-shaped structures incorporate three stabilization fins designed for high-speed fluid navigation. Prototype testing demonstrated that the micro-bots travel at relative speeds equivalent to a human running 400 miles per hour through biological fluids. While initial versions pass through the urinary tract intact, the team is currently integrating biodegradable polymers to enable post-treatment absorption.
Why it matters
High fluid drag and micro-scale viscosity have long hindered untethered micro-scale drug delivery systems from reaching deep tissue targets. Utilizing 3D printing for sub-hair-scale geometries enables rapid iteration of hydrodynamic profiles. Developing fully biodegradable micro-bots removes the need for surgical retrieval after targeted drug delivery.
CU Boulder lead researchers highlight that high-speed propulsion enables micro-bots to navigate against active fluid flows to target localized tumors. However, biomedical engineers point out that controlling directionality in turbulent internal environments without onboard guidance hardware remains a major hurdle for clinical applications.
A collaborative team from Caltech, Sungkyunkwan University, KAIST, and NTU Singapore detailed BeetleBot on Friday, November 13, an insect-scale soft robot powered by printed liquid crystal elastomer (LCE) artificial muscles. Electrical currents heat the LCE strips to drive leg contraction, matching the stress generation profile of biological muscle tissue. Controlled via a wearable rubbery finger patch, BeetleBot incorporates a miniature camera and jaw-mounted capacitive force sensors to stream real-time visual and tactile telemetry back to the operator, enabling remote manipulation in tight spaces.
Why it matters
Replacing rigid electric motors with printed LCE artificial muscles overcomes weight and size limitations in insect-scale robotics. Integrating bidirectional tactile and visual feedback allows operators to sense contact force while navigating constrained spaces. This bio-inspired design offers a functional template for micro-scale search-and-inspection tools.
The international development team emphasizes that LCE actuation matches mammalian muscle stress outputs while eliminating heavy mechanical gearboxes. Conversely, robotics researchers note that actuation requiring thermal heating above 110°C (230°F) introduces high power consumption and slow cooling cycles that limit continuous leg step frequencies.
Researchers at PLOS ONE detailed a two-degree-of-freedom pneumatic flexible robotic wrist on Thursday, November 12, featuring a central controllable braking mechanism that increases external load resistance by a factor of 4.98. The prototype arranges three parallel soft pneumatic actuators around a central cross-hinge pneumatic brake to combine compliant motion with high structural stiffness. Operating at 0.35 MPa brake pressure, the module generated a maximum holding torque of 0.842 Nm while supporting continuous single-axis pivot angles up to 45.2 degrees.
Why it matters
Soft robotic manipulators typically struggle to hold heavy payloads because compliant materials yield under load. Integrating a pneumatic brake directly into a flexible wrist decoupling continuous pivoting from pose stabilization solves this structural limitation. Achieving a fivefold increase in load capacity allows compliant manipulators to execute industrial assembly tasks without sacrificing safety.
The study authors demonstrate that selective pneumatic braking allows soft wrists to switch instantly between compliant movement and rigid holding modes. Conversely, industrial robotics engineers note that adding secondary pneumatic brake lines increases plumbing complexity and total valve control overhead.
Following yesterday's announcement of May Mobility's strategy to deploy 10,000 autonomous Volkswagen ID. Buzz vehicles, the company detailed its supply chain roadmap. Partnering with ECARX for hardware integration and Charlton Intelligence.ai for fleet support, May Mobility plans U.S. public road testing in early 2027 ahead of 2028 production deliveries. The company projects this tier-one integration will reduce per-vehicle hardware costs by over 50% compared to its current fleet platform.
Why it matters
Scaling robotaxi operations depends heavily on slashing per-vehicle hardware costs to achieve commercial fleet viability. Partnering directly with automotive suppliers like Volkswagen and ECARX allows May Mobility to deploy modular autonomous driving kits onto mass-produced vehicle frames without building custom car foundries. This asset-light approach provides a commercial framework for competing against vertically integrated autonomous fleets like Waymo and Tesla.
May Mobility executives emphasize that co-designing hardware integration with tier-one suppliers cuts per-vehicle capital costs in half while accelerating commercial scaling. However, autonomous vehicle analysts note that achieving multi-city Level 4 scale by 2028 requires navigating complex local regulatory approvals across target U.S. jurisdictions.
Building on its recent robotaxi expansion into San Antonio, Tesla upgraded its unsupervised service to 24/7 operations across all eight of its active U.S. markets on Thursday, November 12. Powered by Full Self-Driving version 15, the round-the-clock service removes overnight operational pauses in five Texas and three Florida markets. Austin remains the largest active deployment, with over 400 registered autonomous units operating on the platform.
Why it matters
Moving to continuous 24/7 autonomous operations increases fleet asset utilization and tests vision-only software under challenging night driving conditions. Expanding coverage across major metropolitan areas brings Tesla into direct competition with Waymo's established commercial footprint. Operational data gathered during 24/7 service will be critical as Tesla prepares for broader Cybercab production deliveries.
Tesla autonomy engineers state that FSD version 15 handles low-light visibility and dynamic night obstacles reliably enough to eliminate overnight pauses. Conversely, safety regulators and industry skeptics emphasize that scaling steering-wheel-free Cybercab units still requires formal federal NHTSA safety clearances.
Co-Developed Edge Silicon Challenges Generic GPU Dominance Humanoid makers like UBTech and ASUS are abandoning generic compute off-the-shelf in favor of custom SoCs and dedicated edge hardware. By combining high-level neural inference with sub-millisecond motor control loops on a single substrate, hardware builders are targeting the latency and power walls that off-the-shelf chips hit during real-world dynamic balance.
Egocentric Video Pipelines Bypass Expensive Teleoperation Frontier models from Xiaomi and NVIDIA demonstrate that pretraining on vast amounts of human first-person video drastically slashes the need for costly robot play datasets. By extracting 3D geometric latent actions from video streams before fine-tuning on minimal hardware demonstrations, data scaling costs are dropping significantly across the embodied AI stack.
Tactile Standardization Unifies Heterogeneous Hardware Touch perception is decoupling from proprietary sensor hardware through open foundation models like LingBot-Touch. Mapping disparate magnetic, optical, and piezoresistive signals into unified coordinate frames allows dexterous hands from competing vendors to execute contact-rich manipulation tasks without custom calibration layers.
Enterprise Logistics Shifts Toward Performance-Tied RaaS Deployments Commercial logistics giants like GXO are expanding humanoid deployments through Robotics-as-a-Service contracts tied strictly to operational KPIs. Rather than committing heavy upfront capital for hardware purchases, enterprise adopters are requiring multi-year operational proof across picking and sortation before scaling fleets into the thousands.
Compliant Actuation Decouples Dynamic Locomotion from Rigid Motors Researchers in soft robotics and micro-actuation are replacing traditional electric motors with printed elastomer muscles, pneumatic brakes, and biological hydrogels. These compliant mechanisms resolve the trade-off between structural flexibility and payload rigidity, enabling subgram and insect-scale machines to navigate constrained spaces.
What to Expect
2026-11-15—Jingdong (JD) and Sunwoda formally debut the 10-second hot-swappable JD Jingzao Lithium No. 3 robot battery at APEC.
2026-11-16—DFKI showcases robotic rehabilitation exoskeletons and 6G emergency care systems at MEDICA 2026 in Düsseldorf.
2026-12-02—FDA hosts a two-day hybrid public workshop on regulatory frameworks for autonomous and remotely operated surgical robots.
2026-12-16—Amazon Ring Robotics launches the commercial Ring Rover security robot for enterprise site surveillance.
2027-01-05—Public comment period closes for the FDA discussion paper on autonomous and teleoperated robotic medical devices.
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