Microduck 로봇 확산, llm-d 추론 구조화, 비공개 AI 평가와 문서 파싱 고도화

We built a small biped robot you can teach new tricks to. Train it in simulation, run it on the real thing. Meet Microduck $399, shipping before Christmas. https:// pollen-robotics.com/microduck https:// github.com/pollen-robotic s/microduck …

We have a huge news to share today! Today we are unveiling the first truly accessible RL robot - welcome Microduck A 25 cm tiny open-source biped with 15 actuators and packed with sensors (camera, speaker, LiDAR, NFC, bluetooth, wifi, etc) that you train yourself with reinforcement learning. It's also playable out of the box with more than half a dozen fun and playful pre-trained policies to have it walk, sit, crouch, roller-skate, pick up objects with its articulated beak, and recover on its own. And all for less than $400. See all the details, play with the simulator and order it at: https:// pollen-robotics.com/microduck/ (video with sound on )

Kubernetes meets LLM inference. Google, NVIDIA, IBM, and Red Hat are all backing the same open-source project to make it work. the problem is that LLM inference does not scale the way normal web services do, and the usual Kubernetes answer makes it worse. let me explain: run one vLLM or SGLang server and the KV cache is a clean win. the server keeps the attention keys and values for tokens it has already processed, so a prompt that shares a prefix with an earlier one skips past that computation and starts generating. put a standard Kubernetes Service in front of several replicas and that saving mostly evaporates. the Service hands each request to whichever pod is next in rotation, and that pod usually never saw the prefix, so it recomputes the entire context from scratch. round-robin assumes every replica serves every request equally well. true for stateless web traffic. false the moment prefill caching exists, because replicas now differ by what they remember. teaching Kubernetes that difference turns into four problems. → knowing which replica holds the prefix. each server streams an event every time it creates or evicts a cache block, and the router keeps a live index of who holds what. → knowing when to ignore that index. cache affinity pulls traffic onto warm replicas, so past a load threshold the router drops affinity and picks on load alone. otherwise the warm replica turns into the bottleneck. → extending where the cache lives. accelerator memory fills fast, so blocks spill to CPU memory and then disk. on four H100s at 250 concurrent users, that hierarchy delivered 13.9x the throughput of keeping everything on the GPU. → separating prefill from decode. prefill is compute-bound, decode is memory-bandwidth-bound, and running both on one replica underuses each. AWS measured up to 70% higher tokens per second after splitting them onto dedicated pools, though the KV cache now has to cross the network before the first token appears. the KV cache stops being something a single server manages. it becomes cluster state, and the routing layer has to track it. solve that and you get roughly 3x the output throughput and half the time to first token, on the same hardware, running the same model. llm-d is the project handling all four on Kubernetes. it sits above vLLM and SGLang rather than replacing them, so you keep whichever engine you already run and it takes over the routing, the cache index, the offloading, and the prefill/decode split. Apache 2.0, CNCF sandbox, with Tesla, Snowflake, Cohere and DigitalOcean running it. check it out on GitHub: https:// github.com/llm-d/llm-d i wrote the full breakdown of how inference works underneath all of this. the article is quoted below.

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In an industry first, we’re piloting double-blind evaluations for frontier AI. By creating a secure environment where neither test prompts nor model weights are revealed, we can ensure external safety and performance evaluations of our models remain private, robust, and trustworthy. → https:// goo.gle/3St2xan

NVIDIA Vera is heading to @awscloud .  NVIDIA’s Ian Buck hand-delivered AWS’s first Vera CPU Server and Vera Rubin GPU to Willem Visser and Supreeth Sheshadri at the AWS HQ in Seattle. Vera is purpose-built for agentic AI: more tokens per dollar, faster results for users, and the compute foundation AI factories need to scale.

This week I had the honor of speaking to Princeton’s entire incoming undergraduate class to address their AI anxieties. I had three messages for them — good news, bad news, and a note of optimism. Here’s a condensed version. The good news We have enough evidence now to conclude that the shrill predictions of rapid, massive job loss were misplaced. Even in a field like software engineering where AI has been rapidly adopted, its effect has been to shift, not replace the role of the human (see the “decide-execute-deliver” framework https:// normaltech.ai/p/why-ai-hasnt -replaced-software-engineers …) Similarly, the panic about what to major in is also misplaced. There will be enduring demand for computer science, philosophy, and just about everything else. (In fact, AI companies hiring philosophers has been a big recent trend.) The bad news AI seems to help senior people much more than juniors. I can use AI for coding because I spent 25 years learning how to code, which lets me supervise coding agents effectively. (See my post on the “growth cycle” vs the “dependence spiral” https:// x.com/random_walker/ status/2065408097640677572 …) You are in a bind — you can’t offload your skill-building to AI, but you’ll graduate into a market where employers will expect you to get work done with AI. We never faced this dilemma. As a result we haven’t figured out how to revamp our classes to help you do both. You’ll have to help us figure it out. And you’ll need to somehow resist the constant temptation to turn to the shortcut machine. The hope My point is not that AI is bad for learning. It’s an incredibly flexible tool. Is the internet good or bad for learning? Depends — are you using it to find research papers or waste time scrolling? I use AI every day for learning. The key is to use it to increase, not decrease your cognitive load. To learn deeper, not faster. There is no learning without the cognitive sweat. I try to make sure I’m mentally exhausted at the end of the day. I do feel that AI lets me push myself harder than I ever could before, and I have a vision that as AI continues to advance it will enable human-AI “co-superintelligence“. (I talked about this at the end of my ICML keynote. https:// normaltech.ai/p/what-will-be -left-for-us-to-work …)

This illustrates the problem with traditional vision ML: models are effectively blind outside annotated boxes—like throwing away 90% of the information. You cannot teach nuances with simple boxes. That’s the approach from the ImageNet era.