MPR CB, Author at IoT Digital Twin PLM

The Experimental-Data Moat: Why AI Labs Are Building Robots to Make Their Own Data (2026)

By MPRAUTO MPRAUTO July 12, 2026TechNo Comments

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)

By MPRAUTO MPRAUTO July 12, 2026TechNo Comments

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)

By MPRAUTO MPRAUTO July 10, 2026AINo Comments

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)

By MPRAUTO MPRAUTO July 10, 2026AINo Comments

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.

TwinOps: The Operational Lifecycle Architecture for Digital Twins (2026)

By MPRAUTO MPRAUTO July 10, 2026Digital TwinNo Comments

TwinOps applies DevOps discipline to digital twins: model versioning, continuous state sync, validation gates, drift detection, and closed-loop control across the twin lifecycle.

Card Tokenization and the PCI DSS Vault: A Payment Security Architecture (2026)

By MPRAUTO MPRAUTO July 10, 2026SecurityNo Comments

How card tokenization shrinks PCI DSS scope: vault design, format-preserving vs random tokens, network tokens, detokenization flows, and key management for payment systems.

Small Language Models on Device: Edge Inference Architecture (2026)

By MPRAUTO MPRAUTO July 10, 2026AINo Comments

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)

By MPRAUTO MPRAUTO July 10, 2026AINo Comments

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.

Airflow vs Dagster vs Prefect: Data Orchestration Compared (2026)

By MPRAUTO MPRAUTO July 10, 2026DevelopmentNo Comments

Airflow vs Dagster vs Prefect compared: execution model, asset vs task orchestration, backfills, dynamic pipelines, observability, and a decision matrix for 2026.

The Experimental-Data Moat: Why AI Labs Are Building Robots to Make Their Own Data (2026)

Self-Driving Lab Architecture: The Closed Loop That Runs Experiments (2026)

Kimi K2 Explained: Architecture, Training, and Benchmarks (2026)

LLM Semantic Caching Architecture: Cut Inference Cost and Latency (2026)

TwinOps: The Operational Lifecycle Architecture for Digital Twins (2026)

Card Tokenization and the PCI DSS Vault: A Payment Security Architecture (2026)

Small Language Models on Device: Edge Inference Architecture (2026)

Google Gemini 3.5 Pro Explained: Architecture, Benchmarks, and 2M Context (2026)

Airflow vs Dagster vs Prefect: Data Orchestration Compared (2026)

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