The FCC issued a ban this week on new, foreign-made humanoid robots, quadrupeds, robovacs, and sidewalk delivery robots, citing privacy risks from devices that map homes and public spaces plus supply-chain security concerns. China threatened retaliation, calling it a threat to "economic and trade stability." While the ban doesn't name China outright, the country controls roughly 85% of the global humanoid robot market. The rule doesn't target specific companies or actually curb data collection, and many US robotics firms that rely on Chinese components are worried it could gut their own supply chains instead.
morningbrew.com·3 min read
Hyperscalers like AWS and Meta are snapping together factory-prefabricated modular datacenters, building mechanical and electrical fit-outs off-site like standardized Lego blocks to bypass construction labor shortages, compressing building schedules by 36% and cutting on-site licensed electrician labor by 85%. It's part of a broader trend of routing around old bottlenecks entirely: developers are increasingly wiring GPU clusters directly to co-located natural gas generation, small modular reactors, or existing nuclear plants, cutting time-to-power from 60 months down to under 18.
newsletter.semianalysis.com·7 min read
A developer got a 28.9M parameter language model running entirely on an ESP32-S3, a microcontroller with just 512KB of SRAM. The trick, borrowed from Google's Gemma models, is called Per-Layer Embeddings: instead of loading the full network into fast memory, about 25 million parameters live in flash as a lookup table, and only the bytes needed for the current token get read out during inference, while the smaller layers stay in SRAM. After quantization, the whole model shrinks to 14.9MB, fitting comfortably on the board's flash, and it generates about 9.5 tokens per second on an $8 chip.
Qualcomm finished buying Modular, the AI infrastructure company behind the Mojo programming language and the MAX inference framework. Modular's whole appeal is that its software lets the same model code run across CPUs, GPUs, NPUs, and custom silicon without rewriting kernels for each one. Qualcomm just raised its 2029 target to $40 billion in non-handset revenue, with data center alone expected to contribute over $15 billion, and it's betting that owning the compiler and serving layer outright, not just the silicon, is what gets new Qualcomm AI chips performing well from day one.
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General-purpose AI and data tools aren't built for physical systems. The result? Frequent breakage under pressure, at speed, and on high-consequence projects. Nominal [8] connects the major product components engineers now stitch together to help hardware engineers validate, operate, and improve machines. See how Anduril engineers
Robots trained on step-by-step instructions often fail on new combinations of familiar steps they weren't directly shown, and collecting demonstrations for every possible combination is impractically expensive. This paper breaks that failure down into three distinct causes - shifts in individual instructions, shifts in how instructions combine, and shifts in the surrounding context - to figure out exactly what a training set needs to cover rather than how much of it. Full coverage of every instruction combination isn't necessary: a carefully chosen quarter of the task space can match it, as long as that subset preserves the real dependencies between actions rather than just varying surface details.
Tau Robotics has opened an invite-only San Francisco pilot using humanoid robots for vacuuming, surface cleaning, trash removal, and deep cleaning around appliances. The robots combine AI with live human supervision, with each unit assigned to one appointment at a time during the initial roll-out. The service is priced at $30 per hour and is currently limited to selected households.
tau-robotics.com·4 min read