NVIDIA GB300 NVL72: Blackwell Ultra Architecture (2026)
NVIDIA GB300 NVL72 explained: Blackwell Ultra GPUs, the 72-GPU NVLink rack, memory and power, and how it scales AI training and inference at rack level in 2026.
AI Inference Cost Optimization: GPU FinOps in 2026
An AI inference cost optimization decision record: continuous batching, KV-cache, quantization, speculative decoding, spot GPUs, and autoscaling the inference path.
LLM Gateway Architecture: The Control Plane for AI Apps
An LLM gateway architecture for production AI: routing, semantic caching, rate limits, budgets, fallbacks, and observability across multiple model providers.
Fine-Tuning vs RAG vs Long-Context: A 2026 Cost/Quality Decision
A 2026 cost and quality decision record for fine-tuning vs RAG vs long-context LLMs: token economics, latency, accuracy trade-offs, and a decision matrix.
Image Segmentation Models: Types, Comparison, and Uses (2026)
A 2026 technical overview of image segmentation models: semantic, instance, and panoptic segmentation, U-Net to SAM 2, with a comparison and applications.
Agentic AI Security: Defeating Prompt Injection in 2026
An applied defense-in-depth pattern for agentic AI security: the indirect prompt injection kill-chain, OWASP LLM/Agentic Top 10, and layered mitigations.
Corrective RAG and Self-RAG: Architecture Patterns (2026)
Corrective RAG (CRAG) and Self-RAG explained for 2026: retrieval grading, query rewriting, self-reflection loops, a reference design, and when each pays off.
MiniMax M3: An Open-Weight LLM Benchmark Analysis (2026)
A 2026 benchmark analysis of MiniMax M3: open-weight coding, 1M-token context, and multimodality — methodology caveats, results, and how to read the numbers.
A Comparative Analysis of State-of-the-Art Object Detection Models
A comparative analysis of state-of-the-art object detection models, updated for 2026: YOLO11/12, RT-DETR, transformer detectors, accuracy, latency, and trade-offs.
LLM Semantic Router: An Inference Routing Pattern
The LLM semantic router pattern in 2026: route requests by intent and cost to the right model, with vLLM Semantic Router, embeddings, and a reference design.