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<div style="display: none; max-height: 0px; overflow: hidden;">Testimony in a South Korean criminal case alleges that CXMT, now China's largest DRAM maker, was founded with no in-house research capability </div>
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<h1><strong>TLDR Hardware <span id="date">2026-08-24</span></strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.tomshardware.com%2Fpc-components%2Fdram%2Fcxmt-planned-to-use-stolen-samsung-ip-to-develop-its-dram-court-hears-former-samsung-engineer-who-jumped-to-chinese-memory-maker-now-behind-bars%3Futm_source=tldrhardware/1/010001a033e71ee4-c9ff1219-e5da-45d3-9bc0-06f5dfdf897d-000000/WClz8qnuT2JYceRd59uaP9ttHfsRXH-IwMHmaLdONOY=452">
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<strong>Court Hears CXMT's DRAM Was Built From the Start on Stolen Samsung IP (4 minute read)</strong>
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Testimony in a South Korean criminal case alleges that CXMT, now China's largest DRAM maker, was founded with no in-house research capability and a deliberate plan to obtain Samsung's process technology IP rather than develop it independently. Former Samsung engineer Jeon, who moved to CXMT around 2016 after 28 years at Samsung, was sentenced to seven years in prison this April for stealing a 600-step Process Recipe Plan that detailed Samsung's DRAM fabrication process, including its 18nm-class node, information covering deposition and etch conditions, anneals, temperatures, and tool types that can shave years off developing a memory node from scratch.
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<strong>Nvidia Customers Notified About AI-Related Price Hikes Above 15% (3 minute read)</strong>
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Nvidia and server contract manufacturers have informed major cloud and enterprise customers that prices for AI server systems will rise by more than 15% across several configurations. The price hikes—slated to take effect on systems shipping in early 2027, including platforms powered by Grace Blackwell and Vera Rubin architectures—are primarily driven by sharp cost escalations across high-bandwidth memory (HBM) and advanced DRAM components.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgizmodo.com%2Fspacex-finally-recovers-starship-after-24-days-of-tugging-it-through-rough-waters-2000800828%3Futm_source=tldrhardware/1/010001a033e71ee4-c9ff1219-e5da-45d3-9bc0-06f5dfdf897d-000000/6AUaA0olCS4FczoFHoBhXehJJWT2A1E5cDv6ddDkQjg=452">
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<strong>SpaceX Finally Recovers Starship After 24 Days of Tugging It Through Rough Waters (2 minute read)</strong>
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After Starship's upper stage splashed down intact in the Indian Ocean following its 13th test flight in late July, SpaceX decided to actually recover it, an unplanned bonus given the vehicle survived reentry in one piece. What followed was a grueling 24-day tow through increasingly rough seas, with the 171-foot spacecraft finally reaching calm waters off Christmas Island, Australia, where engineers will now inspect it before deciding how and when to get it back to Starbase. A close physical inspection gives engineers far more detail than onboard sensors and cameras alone, informing modifications ahead of Starship Flight 14, targeted for late August to early September, which will attempt the vehicle's first full orbital test.
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<strong>World Humanoid Robot Games shifts from sprint demos to autonomous work tests (5 minute read)</strong>
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The Beijing competition now includes 21 scenario-based events spanning cable connection, industrial assembly, material loading, EV charging, restaurant work, and emergency response. More than 40% of the 51 total events require full autonomy, forcing robots to handle misalignment, shifting objects, controlled force, and error recovery rather than scripted locomotion. The cable-connection challenge specifically combines vision, hand positioning, mechanical alignment, and force control, making it a much more revealing physical-AI benchmark than sprinting or dancing.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fdiscourse.openrobotics.org%2Ft%2Fannouncing-fastswarmsim-lightweight-multi-uav-ros-2-simulation-with-lock-step-time%2F57578%3Futm_source=tldrhardware/1/010001a033e71ee4-c9ff1219-e5da-45d3-9bc0-06f5dfdf897d-000000/Y4oEv85kDADDZIHquZenDusfTVuQH6QORb8lzc0RB20=452">
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<strong>FastSwarmSim: Lightweight Multi-UAV ROS 2 Simulation with Lock-Step Time (5 minute read)</strong>
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This newly announced simulator aims to streamline swarm robotics research. By utilizing lock-step time, researchers can guarantee synchronized simulation environments for ROS2 across multiple unmanned aerial vehicles. This allows for more reliable testing of complex swarm behaviors and communication protocols before deploying code to physical drones.
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<strong>Tiangong Ultra robot cuts its 100m time from 21.50 s to 9.39 s in one year (4 minute read)</strong>
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Beijing Humanoid Robot Innovation Center's Tiangong Ultra completed a 100-meter heat in 9.39 seconds, down from 21.50 seconds at last year's World Humanoid Robot Games. Honor's Lightning followed at 9.47 seconds after researchers lengthened its legs by 10 cm. A preparatory run reached 9.32 seconds and a 15.5 m/s peak speed. The results show a greater than 2× year-over-year improvement in full-size humanoid sprint performance.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Nvidia Is Spending $6 Billion to Build a US Alternative to Chinese AI models (3 minute read)</strong>
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Nvidia is deploying roughly $6 billion in a major strategic transaction with AI startup Poolside to develop high-performance open-weight models capable of competing directly with leading Chinese releases like DeepSeek and Kimi. Under the agreement, Nvidia is paying $6 billion to license Poolside's core technology—including its Model Factory platform—and is investing an additional $1 billion at a $12 billion valuation while absorbing over 100 of the startup's engineers. The incoming technical team will focus on expanding Nvidia's open-weight Nemotron project, strengthening Western frontier model development and ensuring competitive parity against state-backed open-source AI initiatives emerging from China.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.geopoliticalmonitor.com%2Fthe-quantum-arms-race-has-a-baseline-problem%2F%3Futm_source=tldrhardware/1/010001a033e71ee4-c9ff1219-e5da-45d3-9bc0-06f5dfdf897d-000000/ogvIXcZepRmjm6s5WwCYw08hTcukX2LmWF4_LKiFkKU=452">
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<strong>The Quantum Arms Race Has a Baseline Problem (4 minute read)</strong>
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As the US and China both push quantum computing, sensing, and networking as strategic priorities, the real question isn't who's ahead but ahead of what, since a quantum advantage is only meaningful relative to the classical system it's compared against. Quantum radar is the clearest case: the underlying physics of quantum illumination is real and has shown genuine detection gains in controlled lab conditions, but a laboratory advantage doesn't automatically translate into a long-range operational radar advantage once range, clutter, atmospheric loss, and receiver complexity enter the picture. The same trap applies to quantum machine learning and quantum computing broadly, where a system can look superior on paper simply because it was benchmarked against a weak or poorly optimized classical competitor rather than the best available alternative.
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<strong>Anthropic Taps Google Chip Veteran as Part of Push Into Hardware (3 minute read)</strong>
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Anthropic has hired former Google custom silicon executive Amir Salek to join its compute team as the frontier artificial intelligence lab begins laying the groundwork for an in-house semiconductor initiative.
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