From Notebook to Distributed Training
The architectural decisions that matter when an experiment becomes a coordinated training job.
Ideas for building practical AI systems
HEXBIT / INSIGHT
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AI Infrastructure
Designing Reliable AI Inference Systems
A practical framework for balancing model quality, latency, throughput, and operating cost.
6 min readChoosing Between VMs, Containers, and Bare Metal
Match the operating model to the workload instead of starting with the infrastructure label.
Building Observable AI Agents
Plan tool boundaries, state, evaluation, and runtime signals before an agent reaches production.
Capacity Planning for Growing AI Teams
How to evolve from flexible on-demand resources to predictable dedicated capacity.
Retrieval Patterns for Production RAG
A concise guide to embeddings, metadata filters, reranking, and evaluation loops.