Payment orchestration platform architecture: routing across PSPs, cascading retries, tokenization vaults, smart routing, and reconciliation - a systems view for 2026.
IoT device identity and attestation architecture: hardware roots of trust, DICE, TPM/secure elements, provisioning, and remote attestation for device fleets in 2026.
Agentic payments architecture: how AI agents authorize and settle purchases with scoped mandates, tokenization, and verifiable intent - a systems view for 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.
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.
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 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.
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.