How GPS Atomic Clocks Actually Work: Precision Timekeeping
How GPS atomic clocks work in 2026 — rubidium and cesium oscillators, satellite timekeeping, relativity corrections, and PNT alternatives.
Sparkplug B 3.0 Edge of Network Architecture: Production Reference
Sparkplug B 3.0 edge of network architecture for production IIoT — topic namespaces, primary/secondary hosts, store-and-forward, MQTT 5 mapping, and 2026 deployment patterns.
UNS vs Data Mesh vs Data Fabric for Industrial Data (2026)
Unified Namespace vs Data Mesh vs Data Fabric for industrial data in 2026 — semantics, governance, ownership, edge vs cloud, and a decision matrix for OT/IT convergence.
OPC UA PubSub over MQTT 5: A Production Implementation Tutorial
Step-by-step OPC UA PubSub over MQTT 5 implementation — DataSetMessage mapping, JSON vs UADP, security profiles, and a working aiomqtt + asyncua tutorial for 2026.
Why Industrial AI Pilots Fail in 2026 — The Data Maturity Problem
Industrial AI pilots are failing at an 80% rate in 2026 — the root cause is not model quality but data maturity. Honest analysis with architecture-level remediation patterns.
RAG Over CAD and BOM: Reference Architecture for PLM Knowledge Retrieval
RAG over CAD and BOM data for PLM knowledge retrieval — chunking strategies for engineering drawings, BOM graph embeddings, and a reference architecture proven in 2026 production.
Q2 2026 Open-Source Embedding Models Benchmark: BGE, GTE, E5, Stella, Nomic
Q2 2026 open-source embedding models benchmarked — BGE-M3, GTE-Qwen2, E5-Mistral, Stella, Nomic on MTEB plus latency, memory, and industrial retrieval tasks.
Sidecar vs eBPF Service Mesh: An ADR for 2026 Production Workloads
ADR for sidecar vs eBPF service mesh in 2026 — context, options (Istio Ambient, Cilium Mesh, Linkerd2), trade-offs, consequences, and the decision criteria we settled on.
Hyperscaler Capex Wars 2026: AI Compute Reshapes Cloud Economics
The 2026 hyperscaler capex wars — Microsoft, Amazon, Google, Meta, Oracle spending breakdowns, GPU vs ASIC strategies, power constraints, and the new cloud unit economics.
Geometric Deep Learning for Materials Discovery: GNoME, MatterGen, 2026 SOTA
Geometric deep learning for materials discovery in 2026 — GNoME, MatterGen, equivariant GNNs, the inverse design problem, and where ML-driven materials science actually ships.