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.
AI for Science Landscape 2026: Periodic Labs, Lila Sciences, and the Self-Driving-Lab Race
A 2026 map of the AI-for-science landscape: Periodic Labs, Lila Sciences, Berkeley A-Lab, Emerald Cloud Lab, and academic SDLs — capital, technical approach, moats, and honest caveats.
The Autonomous Materials-Discovery Pipeline: Closed-Loop Synthesis and Characterization (2026)
Inside an autonomous materials-discovery pipeline: ML screening, robotic synthesis, automated characterization, and the learning loop — plus an honest look at the A-Lab novelty debate.
The AI Scientist Architecture: LLM Planners That Generate Hypotheses and Dispatch Experiments (2026)
How an AI scientist works: an LLM planner that generates hypotheses, decomposes goals, calls simulators and tools, dispatches to a lab, and verifies results — with memory and guardrails.
Bayesian Optimization for Autonomous Experiments: The Planner Inside a Self-Driving Lab (2026)
How Bayesian optimization and active learning plan physical experiments: surrogate models, acquisition functions, batch/parallel design, and cost-aware search over a real design space.
The Experimental-Data Moat: Why AI Labs Are Building Robots to Make Their Own Data (2026)
The public internet is running dry as a training source. Why frontier AI-for-science labs are manufacturing proprietary physical-world experimental data — the compounding-data thesis, explained.
Self-Driving Lab Architecture: The Closed Loop That Runs Experiments (2026)
A reference architecture for self-driving labs: the design-make-measure-learn closed loop, orchestration, safety interlocks, and human-in-the-loop control for autonomous experimentation.
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.