A deep dive into the network fabric that makes large-scale AI training possible: RDMA, InfiniBand, RoCE, EFA, NVLink, and how to design lossless GPU cluster networks.
A principal cloud architect's guide to choosing between LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK for production agentic AI systems. Covers state management, cost control, and deployment architecture.
A practical guide to fine-tuning large language models in production: how LoRA and QLoRA adapters work, when to use FSDP vs DeepSpeed for multi-GPU training, and how to set up cloud infrastructure that doesn't burn your budget.
How retrieval-augmented generation actually works in production: chunking strategies, hybrid search, re-ranking, evaluation, and the infrastructure patterns behind reliable RAG systems.
A practical guide to instrumenting LLM applications with OpenTelemetry GenAI semantic conventions, choosing between Langfuse, LangSmith, and Arize Phoenix, tracking token costs, and running evaluation in production.
Ray is the distributed compute engine behind OpenAI, Cohere, and most serious AI labs. Here's how it actually works, how to run it on Kubernetes with KubeRay, when to use it, and when Dask or Spark is the better call.
A principal cloud architect's guide to choosing between vLLM, SGLang, TensorRT-LLM, and other LLM inference engines. Includes benchmarks, trade-offs, and a practical decision framework for production AI workloads.
Kubernetes Dynamic Resource Allocation (DRA) graduated to GA and fundamentally changes how GPU and accelerator resources are scheduled. Here is what it means for AI infrastructure teams.
MCP is the protocol that lets AI agents use tools, query databases, and call APIs. Here's how the architecture works, why it matters, and how to deploy it in production.
Virtual machines are the default in cloud computing, but for a growing set of workloads, the hypervisor is pure overhead. Bare metal cloud gives you dedicated hardware without the colocation operations burden. Here's when it makes sense.
A practitioner's guide to GPU cloud infrastructure: NVIDIA Blackwell vs Hopper, cloud provider GPU instances, cost optimization, and how to right-size your AI compute.
How to build production-ready LLM inference infrastructure: GPU selection, model serving frameworks, batching strategies, and cost optimization for AI workloads.
A practical guide to vector databases for AI applications: when to use pgvector vs dedicated vector DBs, how ANN indexing works, and what I've learned shipping RAG systems in production.
Practical deep dives on infrastructure, security, and scaling. No spam, no fluff.
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