AWS implemented a 15% price hike for EC2 Capacity Blocks for ML on January 3; Amazon says it "reflects the supply/demand patterns we expect this quarter" (Corey Quinn/The Register)
https://www.theregister.com/2026/01/05/aws_price_increase/
Jackie Quinn, Summer Quinn, and a line of people who want to be lifeguards
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Season 3 Episode 1 "River of No Return: part 1"
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Null Electronics
From viral music deep dives to long-form artist conversations with Hayden Quinn (Null)...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/null-electronics/
It’s cool that all three Hughes brothers are playing in the Olympics. Jack Hughes, Quinn Hughes, and Matthews.
Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Four- and six-photon stimulated Raman transitions for coherent qubit and qudit operations
Gregory, Ritchie, Quinn, Brudney, Wineland, Allcock, O'Reilly
It’s cool that all three Hughes brothers are playing in the Olympics. Jack Hughes, Quinn Hughes, and Matthews.
AgentCgroup: Understanding and Controlling OS Resources of AI Agents
Yusheng Zheng, Jiakun Fan, Quanzhi Fu, Yiwei Yang, Wei Zhang, Andi Quinn
https://arxiv.org/abs/2602.09345 https://arxiv.org/pdf/2602.09345 https://arxiv.org/html/2602.09345
arXiv:2602.09345v1 Announce Type: new
Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations. We present a systematic characterization of OS-level resource dynamics in sandboxed AI coding agents, analyzing 144 software engineering tasks from the SWE-rebench benchmark across two LLM models. Our measurements reveal that (1) OS-level execution (tool calls, container and agent initialization) accounts for 56-74% of end-to-end task latency; (2) memory, not CPU, is the concurrency bottleneck; (3) memory spikes are tool-call-driven with a up to 15.4x peak-to-average ratio; and (4) resource demands are highly unpredictable across tasks, runs, and models. Comparing these characteristics against serverless, microservice, and batch workloads, we identify three mismatches in existing resource controls: a granularity mismatch (container-level policies vs. tool-call-level dynamics), a responsiveness mismatch (user-space reaction vs. sub-second unpredictable bursts), and an adaptability mismatch (history-based prediction vs. non-deterministic stateful execution). We propose AgentCgroup , an eBPF-based resource controller that addresses these mismatches through hierarchical cgroup structures aligned with tool-call boundaries, in-kernel enforcement via sched_ext and memcg_bpf_ops, and runtime-adaptive policies driven by in-kernel monitoring. Preliminary evaluation demonstrates improved multi-tenant isolation and reduced resource waste.
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Quinn Mason & A Far Cry:
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