LLM Model Routing Architecture: Cost and Quality at Scale (2026)
LLM model routing architecture: how to route requests across models by cost, quality, and latency with semantic routers, cascades, and eval-driven policies in 2026.
Grok 4.20 Explained: Architecture, Benchmarks, and Deployment (2026)
Grok 4.20 architecture explained: the 4-agent council, 2M-token context, benchmarks, pricing, and how xAI's flagship compares to GPT-5 and Claude in 2026.
Kimi K2 Explained: Architecture, Training, and Benchmarks (2026)
A deep dive on Moonshot AI Kimi K2: the Mixture-of-Experts architecture, training recipe, agentic and coding benchmarks, open weights, license, and how it compares to peers.
LLM Semantic Caching Architecture: Cut Inference Cost and Latency (2026)
A semantic caching architecture for LLM apps: exact vs embedding-similarity cache tiers, thresholds, invalidation, eviction, and the cost/latency math behind GPTCache-class systems.
Small Language Models on Device: Edge Inference Architecture (2026)
How to run small language models (SLMs) on-device: model sizing, distillation, quantization, NPU acceleration, memory budgets, and when a 1-8B SLM beats a cloud LLM.
Google Gemini 3.5 Pro Explained: Architecture, Benchmarks, and 2M Context (2026)
Google Gemini 3.5 Pro explained: the 2M-token context flagship, architecture, training, benchmark scores, pricing, and how it compares to GPT-5.6 and Claude.
Google Gemini 3.5 Flash Explained: Architecture, Benchmarks, and Deployment (2026)
Google Gemini 3.5 Flash explained: the MoE multimodal architecture, context window, real 2026 benchmarks, pricing, latency, and how it compares to GPT and Claude.
Agent Benchmarks in 2026: SWE-bench Verified, GAIA, and tau-bench
A deep dive into 2026 AI agent benchmarks: SWE-bench Verified, GAIA, and tau-bench — what they measure, how they leak, and how to read agent leaderboards honestly.
Constrained Decoding: Architecture for Guaranteed-Valid LLM Output (2026)
How constrained decoding guarantees valid LLM output: grammars, FSAs, token masking, JSON-schema enforcement, and where structured generation breaks in production.
DeepSeek V4 Explained: Architecture, Sparse Attention, Benchmarks, and Deployment (2026)
DeepSeek V4 explained: the 1.6T-parameter MoE architecture, Compressed Sparse Attention, 1M-token context, SWE-bench and reasoning benchmarks, pricing, and how to deploy it.