SYSTEMS

HALO AI System Teaches Robots to Collaborate Naturally with Humans

By

–

27 May 2026 17h58

HALO: The #AI System Teaching #Robots to Collaborate Naturally with Humans
by @lukas_m_ziegler
#Robotics #Engineering #ArtificialIntelligence #Innovation #Technology pic.twitter.com/hOSXnIfktj
— Ronald van Loon (@Ronald_vanLoon) 27 mai 2026

HALO: The #AI System Teaching #Robots to Collaborate Naturally with Humans
by @lukas_m_ziegler #Robotics #Engineering #ArtificialIntelligence #Innovation #Technology

→ View original post on X — @ken_goldberg

AI Latency for Cloud Robotics and Edge Embodiment

By

@ken_goldberg

–

27 May 2026 17h13

Agreed: Latency is now low enough to support robot inference in the cloud, and edge is where embodiment transforms and safety checks should be performed: https://
arxiv.org/abs/2205.09778

→ View original post on X — @kirkdborne

Highly rated new book on generative AI applications with LLMOps

By

@kirkdborne

–

27 May 2026 17h04

Highly rated new book from @PacktPublishing @PacktDataML … "Architecting Generative AI Applications: Build, deploy, and scale production-ready GenAI systems with LLMOps best practices" See it at https://
amzn.to/3Pv4dyF

AI advantage from faster leaner systems, not better prompts

By

–

27 May 2026 17h00

The next AI advantage won't come from a better prompt. It'll come from a faster, leaner system underneath the model. Her's Law and the Tau Scaling Law framework are worth understanding deeply if you're making AI infrastructure decisions. @huawei is leading this thinking. What

Enterprise AI’s new focus: infrastructure over model selection

By

–

27 May 2026 17h00

The enterprise AI question is shifting. It's no longer just: which model are we running? It's now: can the system underneath generate answers fast enough, efficiently enough, and economically enough? Infrastructure-level thinking is becoming the real AI differentiator.

AI response depends on more than compute – system latency matters

By

–

27 May 2026 17h00

Every AI response depends on more than compute. It also depends on: Memory access speed Interconnect efficiency Chip-to-chip communication overhead Data movement costs End-to-end system latency When any of these slow down, your AI gets slower and more expensive. The model

→ View original post on X — @emollick

Debate over Token Allocation as AI Becomes Essential for Coding

By

@emollick

–

27 May 2026 16h20

The fact that tokens went from something no one even put in a budget line a year ago to an absolute requirement for coding now is the cause of handwringing, not that AI is not turning out to be useful No one knows who should get tokens, how much they should get & how to control