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Thinking

Long-form notes for reading the direction of technical change.

Thinking is a professional column: fewer quick posts, more durable essays. It keeps observations, source trails, assumptions, and evolving viewpoints visible over time.

Essays

4 essays shown

September 2026

4 essays
Thinking41 min read

Microsoft Moved "Yield" into AI Infrastructure: Maia 200 and the Yield-Defined Architecture

Microsoft's September 1 blog shipped no product; it proposed a new yardstick for AI infrastructure: yield. This analysis reads the Maia 200 inference platform together with that manifesto: how the FP4-first datapath, two SRAM tiers with three-level DMA, and the FCQ + ATL two-tier scale-up topology make useful tokens per dollar and per watt the design objective, closing with three judgments and three verifiable nodes.

AI
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Thinking75 min read

AI Cluster Observability Series: How Meta's RDMATracer Closes the Kernel Blind Spot with 13 eBPF Probes

A deep dive into Meta's NAIC '26 RDMATracer paper: 13 curated eBPF probes on RDMA control-path kernel functions, emitting only on failure, light up a blind spot where 96% of syscall failures carry no accompanying signal, at 0.0000024% of one CPU per host.

AI 基础设施
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Thinking38 min read

Looped Transformers Hit the Frontline: Why OpenAI, Zhipu, and Academia Converged on the Same Stack of Layers

Recurrent depth turns model depth from an architectural constant into a runtime variable. This piece traces the route's seven-year lineage and engineering boundaries: Huginn made the case, Nanbeige proved it in production, and Astra and Zhipu carried it into the frontier narrative. The limits of the gains, and the cost in monitorability, are equally clear.

AI
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Thinking59 min read

MetaRoCE in Depth: RDMA Designed for Loss, an Open Standard for Million-GPU Scale

MetaRoCE is Meta's clean-sheet RDMA transport protocol, opened through OCP. This analysis works through six questions: why it appeared, what it actually is, why Meta opened it, how the protocol works inside, what it is worth and to whom, and a closing peer review of ByteDance MegaScale, Alibaba HPN, Tencent Xingmai, AWS SRD, Google Falcon, and the UEC/MRC alliances.

AI 基础设施
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