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Technical guides, platform updates, and engineering insights from the team.

Multi-Cloud GPU Kubernetes Clusters: Joining Shadeform Nodes to a k0smotron Control Plane

How to join a Shadeform-rented GPU VM into a k0smotron hosted control plane, run real workloads on it, and the two cross-node …

Turning Telco Sovereign AI Infrastructure Into Per-Token Revenue

Telcos own the GPUs, the sovereign footprint, and the enterprise relationships. Here is how they turn that into per-token AI revenue …

The AI Engineering Tool Landscape in 2026: A Category Map

A categorized map of the tools AI engineers use in 2026, across agents, RAG, inference, fine-tuning, observability, and gateways, with …

The Open Source AI Framework Landscape in 2026: A Map for AI Engineers

A categorized guide to the OSS frameworks AI engineers use in 2026, across agent orchestration, retrieval, serving, training, …

What an LLM Inference Stack Actually Looks Like

The layers of a production LLM inference stack, why each one exists, and which parts you get from open source versus build yourself.

The Complete Guide to GPU Cloud Infrastructure

The architecture, operations, and failure modes of running a GPU cloud in 2026. Written for the people building them.

Speeding Up Kubernetes Image Pulls with Spegel

How we deployed Spegel, a cluster-local OCI registry mirror, to cut cold-start image pull times on our Nebius deployment, and the …

Why GPU Clouds Need a Platform Layer

GPU clouds that sell only compute hours are losing enterprise customers to hyperscalers. Enterprise AI teams don't evaluate GPU clouds …

Should GPU Cloud Operators Build or Buy a Platform Layer?

In this article, we’ll discuss what it actually takes to build a platform layer in-house, what it costs, and where the decision tips …