Artificial Intelligence.
A Smoke Test of ZML's llmd: DFlash Speculative Decoding on Consumer AMD GPUs.
A first-contact test of ZML's alpha inference server on RDNA4 hardware their docs don't mention testing against: it works, and DFlash speculative decoding on Gemma-4 gave a real 1.66x decode speedup.
Too Long, Didn't Embed
A field guide to the quiet ways local embedding models fall over, and how to catch them before the bad vectors reach your index.
Bringing your (k)nowledge to Antigravity
How we built a resilient and developer-centric integration between Nowledge Mem and Google Antigravity 2.0.
The Fabric Schism is Over: Why Your Next Move Should Be Towards Open Ethernet
The painful choice between proprietary performance and open economics is over. The fabric schism has ended. As performance equalizes, the overwhelming TCO and strategic advantages of open Ethernet make it the only logical path forward for AI at scale.
Blueprint for an AI Supercluster: A Reference Architecture for 100,000+ Accelerators
How do you actually build a 100,000-GPU cluster? This blueprint outlines a practical reference architecture: a hybrid model using proprietary links for scale-up and open UEC Ethernet for the massive scale-out, all tied together by the DPU.
Performance-Adjusted TCO: The Real Math Behind Choosing Your AI Network
The most expensive network isn't always the best. As open Ethernet closes the performance gap, the real math comes down to Performance-Adjusted TCO. When speed is equal, the lower cost of hardware and people becomes the deciding factor.
From Monolith to Mosaic: How Composable AI is Redefining Enterprise Value
Enterprise AI is shifting from giant, costly models to efficient, specialized Small Language Models (SLMs) working in teams. This composable approach boosts accuracy, cuts costs, enhances privacy, and enables on-premise deployment—transforming AI into a competitive advantage for 2026 and beyond.
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