datacenternetwork.ai @DataCenterNetAI
Building the AI interface for data center networking. From intent and validation to operations and network intelligence. datacenternetwork.ai Joined September 2026-
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@CoreWeave @nvidia Multi-plane resilience is exactly what makes this interesting at AI scale: one bad path can waste a lot of GPU time. I built a small browser lab where you can drop an entire plane and watch traffic continue on the other: datacenternetwork.ai/fabriclive
@ciscoDC One thing I find fascinating about MRC/SRv6 is the shift in where resilience lives: the network can reconverge, or the endpoint can stop using the bad path. I built simplified hands-on versions of both approaches and wrote up what I learned: linkedin.com/pulse/same-fai…
@ciscoDC Exactly. AI infrastructure only becomes real when engineers can validate how it actually behaves — not just how the architecture looks on paper. Intent, configuration, failure modes and recovery all need evidence before production.
@HPE_Networking The “act” part is where this gets especially interesting. As networks become more agentic, the challenge isn’t just generating the right action — it’s proving that action is safe before it reaches production. Validation has to become part of the AI operating model.
@AristaNetworks @Meta @Microsoft @Arm @AMD @cerebras @OpenAI @Google @AnthropicAI The network is quickly becoming part of the AI system itself, not just the infrastructure underneath it. We think the next step is changing how engineers interact with these fabrics — from intent and validation through operations and network intelligence.
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