Throughout the current year, Netstar has been actively integrating its services into the South African sports industry. Rather than merely engaging in sponsorship, the company demonstrates a genuine interest in utilizing existing technologies to solve new and interesting problems.
During the Amashova Durban Classic event, where 4000 cyclists covered the route between Pietermaritzburg and Durban, Netstar implemented a real-time tracking system for all operational transport. All lead vehicles, ambulances, as well as support and repair services were connected to the monitoring, allowing them to be managed in real time.
It is important to note that the athletes themselves were not tracked, but rather the infrastructure ensuring their safety. Tracking racers is a consumer function and an already solved problem, whereas knowing the location of an ambulance in case of a racer's injury on a closed road far from a hospital is an operational task that Netstar successfully addresses.
Netstar has spent decades solving problems for thousands of enterprises, which they term fleet management. This experience is evident in all of the company's sports projects this year.
At the Comrades Marathon 2026, Netstar deployed real-time traffic monitoring across the entire route under the slogan 'Keeping Everyone on Track.' This provided organizers and marshals with the necessary information to manage resources on race day, and updates were broadcast to the public via the event's official social channels. The scale was significant: the 99th edition and the 50th Up Run gathered approximately 21,600 runners, and the introduction of a new three-group start system aimed to reduce congestion, improve runner flow, and assist medical services along the course.
Mark Forbes, Netstar's General Manager for Bureaucratic Services, emphasized: 'We have been monitoring the roads of South Africa for over 30 years. Race day changes the scale and raises the stakes, but it does not change the fundamental nature of the task.'
In the case of the Sunshine Tour, there was no need to create a specific product for Comrades. Netstar simply applied an existing capability to the road network under temporary extreme load and provided a service that met the need without modification.
The partnership with Sunshine Tour demonstrated the same concept in golf. In February, Netstar and Sunshine Tour announced a joint development of a tool for analyzing golf performance metrics. This tool applies telematics and analytical capabilities borrowed from fleet management to create a player development platform, allowing players to track and measure their performance. The system combines wearable GPS devices recording the player's position on the course with a shot-recording application controlled by trained markers during rounds, providing data on shot dispersion, accuracy trends, and strategic insights. Pilot tests were conducted at the Investec South African Open and the Joburg Open, with the official launch planned for the 2026/2027 season in May.
Dylan Frittelli, who reached the top five at The Masters and The Open, noted the value of this tool: having international experience, he saw how data analysis distinguishes good players from champions, and this gives emerging talents tools to perfect their game. He believes that the lack in South African golf has never been talent, but rather infrastructure.
Furthermore, Netstar is present at motorsports tracks, such as the Toyota Gazoo Racing Cup (GR Cup). In this series, all cars are mechanically identical—they have the same engines, suspension, and tires. The only variable factor is the driver and their actions on the track.
This reflects what Netstar sees in the industries it operates in: when everyone has the same technology, value is created through data and execution. The way the capability is used becomes more valuable than the capability itself. This is the essence of telematics. Two fleets using identical vehicles on the same routes can yield significantly different results in terms of safety and cost, and the entire value of the data layer lies in making this difference visible for elimination.
By combining cycling, ultra-marathon route scouting, golf performance analytics, and series motorsport, we see that they do not share a common audience, a common media asset, or a unified brand promotion logic. What unites them is the problem of movement, and Netstar possesses one platform that solves movement problems.
These four sports served as an exceptionally rigorous testbed. Each loads the platform differently: Amashova represents a fleet in conditions unacceptable to any logistics operator—closed roads, moving perimeter, lives dependent on response time. Comrades is a road network under temporary extreme load, a familiar problem on an unfamiliar scale. The GR Cup eliminates all variables, leaving only execution. Golf completely transfers functionality from the car to the player walking the course, striving to improve their result, marking the platform's transition from a focus on vehicles.
Netstar has moved beyond the role of a tracking partner to something less categorizable. The company transforms movement into information, and its role becomes increasingly independent of what is moving. Over thirty years, covering four sports and two million assets, Netstar has never strived for mere visibility; it has pursued the knowledge and insights generated by a single mobile platform.
Shen Hua, co-founder of LimX Dynamics, asserts that the motion control technology for humanoid robots is reaching a point of convergence, shifting industry focus from demonstrating capabilities to practical deployment in real-world scenarios.
According to Shen Hua, technological development in humanoid robot motion is nearing completion, and bottlenecks are narrowing. The main emphasis has shifted from hardware and motion control to generalization, which encompasses the coordination of the body, cerebellum, and brain. The WAIC 2026 exhibition marked a clear transition from showcasing technologies to exploring deployment possibilities, where integrators tested palletizing and machine servicing tasks, and guidance and performance applications confirmed commercial value. This shift is driven by both hardware improvements and progress in artificial intelligence models.
Founded in 2022 by Professor Zhang Wei of SUSTech in Shenzhen, LimX Dynamics has developed a three-pronged strategy. It includes Luna—a full-scale interactive humanoid robot; modular TRON 2 robots for task execution; and the LimX COSA 0.5 brain system for long-term complex operations. The FluxVLA Engine embodied intelligence infrastructure provides a standardized foundation for data, training, simulation, and deployment on real robots, allowing industries to develop VLA models without needing adaptation for each project.
Shen Hua refuted a common industry belief that the cerebellum lags behind the brain. He stated that in some aspects, the robot's ability to control motion, which is related to the cerebellum, has reached parity with the capabilities of the brain and the body hardware. The critical issue is no longer improving one module's capabilities but integrating the brain, cerebellum, and body: combining task understanding and decision-making, motion control, and robot hardware to achieve true embodied intelligence. The cerebellum remains closely tied to the physical body, requiring adaptation when motor selection or joint architecture changes, but the bottleneck has become systemic integration and scenario fit.
LimX Dynamics' trajectory from a motion control specialist to an AI systems company reflects the evolution of the entire industry. The initial focus on the body was based on the assumption that embodied intelligence requires physical interaction with the world through a specific body, and motion control itself is part of the model's capabilities. The company has invested in brain-level models, including VGM for video-based robot training and DreamActor for the real-to-simulation-and-back learning paradigm. As embodied intelligence enters the deployment phase, the industry must address issues of stability and predictability of capabilities, which requires rethinking the robotic intelligence architecture at a systemic level. Shen Hua summarized the next stage: once motion control ceases to be the primary barrier, competition among humanoid robots will depend not only on models and technologies but also on who first finds truly viable use cases and achieves product-market fit. The industry is moving from the question of what robots can do to how robots enter real environments and create value.
The Kimi K3 technical report details infrastructure, including MoonEP, KDA, and AttnRes. However, the true differentiator remains the engineering expertise behind the implementation of MFU, expert parallelism, and shared experts.
Artificial intelligence infrastructure acts as an operating system connecting hardware and applications. When developers use proprietary models, costs are significantly lower than using third-party solutions, which is due to the gap in infrastructure experience. OpenAI engineer Weng Jiayi noted that the main problem in training is precisely related to infrastructure, and engineers are primarily engaged in debugging this area.
As architectures converge and distillation lowers reproduction barriers, it is infrastructure engineering that determines training cost, deployment efficiency, and stability. One hundred companies utilizing Kimi K3 can achieve one hundred different levels of efficiency. Infrastructure errors include both traditional flaws affecting correctness and stability, and systemic issues that do not violate mathematical accuracy but lead to GPU downtime, memory loss, communication blocking, and reduced throughput.
The model's FLOPs utilization rate measures training efficiency as the ratio of observed throughput to theoretical maximum. Publicly verifiable large-scale training cases demonstrate that ByteDance MegaScale achieved an MFU of 55.2%, which is one of the highest recorded results. As a counterargument, information is provided that xAI, using about 550,000 H100 GPUs, reported an MFU of only 11% according to internal memos, which is significantly below the industry standard of 35–45% and the figures from competitors Meta (43%) and Google (46%). Higher training efficiency directly leads to shorter training cycles, reduced costs, increased experimentation, and scaling of data or models.
The Kimi K3 distributed system employs classic parallelization methods such as pipeline parallelism, expert parallelism, and ZeRO sharding. Pipeline parallelism distributes model layers across GPUs using micro-batch processing to reduce downtime caused by pipeline 'bubbles'. Tensor parallelism splits weight matrices and operations across GPUs, requiring high GPU interconnect bandwidth. A crucial detail is that MoE layers use shared experts replicated across EP ranks, meaning each GPU contains identical shared experts plus various routed experts, allowing shared experts to learn common features in the data. The KDA and AttnRes architectural mechanisms combine with corresponding infrastructure design, while MoonEP addresses unique challenges of expert parallelism in MoE architectures. When working with larger models, problems appear that are unnoticed in smaller models, as previously unrecognized computational processes or memory objects become significant relative to GPU capacity upon scaling.
Open access to weights democratizes model usage, but not infrastructure competence. Differences in deployment costs, amounting to several orders of magnitude, create a structural advantage for the developer in the training and inference economy. This report serves as both a knowledge exchange and a signal of competitive capabilities: the architecture can be studied, but engineering experience at a scale of 2.8 trillion cannot be conveyed through documentation. Model capabilities are increasingly becoming a commodity, while infrastructure perfection transforms into a long-term defensive advantage.
The LINK satellite, launched into near-Earth orbit in early July 2026, has encountered serious problems. On July 28, 2026, two out of three flywheel engines in the attitude control system failed, causing the spacecraft to begin rotating and experience communication difficulties.
Furthermore, a decrease in the efficiency of the propulsion system, which uses three xenon thrusters, has been noted. These malfunctions are linked to incorrect functioning of the power supply system. Despite this, the satellite retains the ability to charge its batteries and maintain contact with the ground control center. It is planned that once attitude control is restored, updated software will be installed on the spacecraft.
LINK was developed by Katalyst Space Technologies for NASA. The primary objective of this spacecraft is to connect with the Swift gamma-ray observatory using three manipulators and raise the telescope's orbital altitude almost back to its original level. If this does not happen, the telescope risks burning up in Earth's atmosphere within the coming months.
After launch, the satellite successfully deployed its solar panels, established contact with Earth, and conducted initial system diagnostics and transmitted images. However, almost two weeks after launch, an anomaly was detected in the operation of one of the flywheel engines, which was promptly resolved.
The text also mentions images captured by the Artemis-2 crew during the flyby of the far side of the Moon on the night of April 6–7, 2026, which were demonstrated to NASA. One of these images shows the crescent Earth setting behind the lunar horizon. The astronauts' goal was to reproduce a known image of the Earth's sunrise taken by the Apollo-8 crew in 1968. Such observations are possible due to the Moon's libration, but they are limited both by the observation area for low orbits near the satellite and by time, as the Orion crew only had a few minutes to capture the planet's sunrise or sunset. Additionally, one of the images recorded a total solar eclipse, which was observed by humans from cislunar space for the first time.
