Production RAG Architecture: Retrieval Pipelines That Actually Work
Ingestion, chunking, embeddings, hybrid search, reranking and context assembly, plus the tenancy leaks and injection paths each stage quietly opens up.
· 16 minCovers AI application security, model risk, and agentic system abuse.
ai-securityresearchapplication-security
Coverage by Owen Park
RTX PRO 6000 Blackwell 96GB: The Local LLM Workstation Buyer's Guide
A workload-first reasoning guide to the 96 GB single-address-space card: what genuinely fits, where MoE models break it, and when a cluster or rental wins.
· 12 min
Multi-Node DGX Spark: Tensor-Parallel Sizing for 2x and 4x Clusters
A 135 GB model against a 128 GB box forces a cluster. Here is the decision order, the parallelism maths, and what the interconnect really costs per token.
· 13 min
Agent Harness Architecture: How Coding Agents Actually Execute Tools
A mechanism-first breakdown of the runtime that wraps a model: the loop, tool schemas, sandboxing, context strategy, permissions, and injection defence.
· 14 min
SGLang vs vLLM vs TensorRT-LLM: Choosing a Production Inference Engine
Paged KV, radix-tree prefix sharing and ahead-of-time compilation all pull in different directions. Work out which one your own workload should pay for.
· 14 min
KV Cache Explained: Why Long Context Costs More VRAM Than Your Weights
A model that fits can still die on one long prompt. Here is the arithmetic behind the second allocation that grows with context, and how to shrink it.
· 11 min
Will It Fit? VRAM Math for Open-Weight Models Across Every Quantization Format
Weights are only one of four memory buckets, and past roughly 115k tokens the KV cache costs more than the model does. Here is the arithmetic in full.
· 16 min
Inference Engineering: A Practical Primer for Serving Your Own Models
Prefill is compute-bound, decode is bandwidth-bound, and KV cache is what really caps concurrency. The metrics, memory maths and tradeoffs behind serving.
· 16 min