IoT Device Identity and Attestation Architecture (2026)
IoT device identity and attestation architecture: hardware roots of trust, DICE, TPM/secure elements, provisioning, and remote attestation for device fleets in 2026.
ML Interatomic Potentials: Simulation-in-the-Loop Discovery (2026)
Machine learning interatomic potentials explained: MACE, foundation potentials, and GNoME-class screening that narrows the search before robots run experiments.
Agentic Payments Architecture: How AI Agents Pay Safely (2026)
Agentic payments architecture: how AI agents authorize and settle purchases with scoped mandates, tokenization, and verifiable intent - a systems view for 2026.
OpenTelemetry Logs: Unified Telemetry Pipeline Architecture (2026)
OpenTelemetry logs and a unified telemetry pipeline: the Collector, log-trace correlation, OTTL processing, tail sampling, and cost control patterns for 2026.
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.