# thingbook.io

> AI-optimized mirror of thingbook.io containing 30 pages totalling 28,269 words of clean markdown content, structured data, and semantic HTML. Original source: https://thingbook.io/. Last updated: 2026-06-14T03:47:49.829Z.

Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG).

## Homepage

- [Training-Free Time-Series Intelligence.  Instantly.](/content/site-root.html): Thingbook is a platform that powers DriftMind, a CPU-efficient, self-adaptive forecasting engine. 140x faster than Deep Learning. Runs on the Edge. No training required. (936 words)

## Articles & Blog Posts

- [doc/echo-pattern-of-interest-html.html](/content/doc/echo-pattern-of-interest-html.html) (1 words)
- [doc/dev-guide-html.html](/content/doc/dev-guide-html.html) (1 words)
- [doc/real-time-forecasting-html.html](/content/doc/real-time-forecasting-html.html) (1 words)
- [doc/driftmind-html.html](/content/doc/driftmind-html.html) (1 words)
- [Why Time Series AI Breaks at Scale in Power Grids](/content/doc/time-series-anomaly-power-grid-html.html): Why time series AI breaks at scale in power utilities — renewables, distributed energy resources, and continuous load drift are invalidating centralised retraining pipelines. How architecture, latency, and economics shape AI adoption in grid operations. (2,182 words)
- [AI Consulting, Delivered by Engineers.](/content/services-html.html): Anomaly detection consulting for time series, IoT, and telecom. 20 years of hands-on AI delivery — from executive strategy to production-grade forecasting systems. DriftMind-powered, CPU-only, no retraining. (1,790 words)
- [The Energy Problem in RO Desalination](/content/doc/desalination-reference-implementation-html.html): How DriftMind integrates with existing SCADA infrastructure (AVEVA PI, InTouch, WinCC) to forecast energy consumption, detect membrane fouling, and recommend optimal setpoints — modelled against a 10,000 m³/day RO plant's historical data. (1,534 words)
- [We Cannot Analyse Everything](/content/doc/why-i-built-driftmind-html.html): The personal story behind DriftMind: why the real bottleneck in production forecasting is not accuracy but the cost and latency of learning. Benchmarked across 4 NAB datasets with honest results. (1,409 words)
- [Predictive Intelligence For Data Center Operations](/content/datacenter-html.html): Predictive intelligence for data center operations: PUE optimization, thermal anomaly detection, cooling predictive maintenance. 48,000 predictions/second on CPU. Edge-deployed, no training required. (1,310 words)
- [CSV X-Ray: Anomaly Detection and Forecasting for Excel and CSV Users](/content/doc/csv-xray-anomaly-detection-forecasting-excel-csv-users-html.html): CSV X-Ray: no-code anomaly detection and forecasting for CSV and Excel users. Explore time-oriented and static datasets without coding, setup, or data retention. (1,483 words)
- [Predict Network Anomalies Before They Hit Subscribers](/content/telecom-html.html): Predict network anomalies at 33,000–48,000 predictions/second on CPU. DriftMind's training-free AI deploys in 2 weeks with zero retraining. Start your free telecom pilot. (1,492 words)
- [Why Time Series AI Breaks at Scale in Telecom](/content/doc/time-series-anomaly-telco-html.html): Why time series AI breaks at scale in telecom — and how architecture, latency, and economics limit AI adoption despite strong benchmark results. (1,500 words)
- [Thingbook Object Structure (TOS)](/content/doc/thingbook-object-structure-tos-html.html): TOS is a reusable framework for behavioural AI, built around BPE, Sensor, Feature, and RBI objects to accelerate anomaly detection and forecasting deployment. (1,242 words)
- [Multi-Perspective Anomaly Detection (MPAD)](/content/doc/multi-perspective-anomaly-detection-mpad-html.html): MPAD models multiple behavioural entities from the same dataset, correlates anomalies across perspectives, and improves root-cause analysis. (1,231 words)
- [The Problem With t-SNE and UMAP](/content/doc/sine-landmark-reduction-html.html): Sine Landmark Reduction (SLR) — a deterministic O(N) dimensionality reduction algorithm that embeds 9,000 points in under 2 seconds. No iteration, no randomness, browser-native. (1,101 words)
- [Reflexive AI: A Canonical Definition](/content/doc/reflexive-ai-manifesto-html.html): A technical definition of Reflexive AI: immediate, local, adaptive intelligence designed for cold-start, streaming, and drift-heavy environments. (1,224 words)
- [The Problem With Prediction](/content/doc/future-of-forecasting-html.html): A survey of seven adaptive forecasting strategies (2022–2024): online learning, meta-learning, adaptive normalization, memory-based methods, and more. Why the field is shifting from static models to living systems. (1,374 words)
- [Predictive Intelligence For Industrial Operations](/content/industrial-html.html): Predictive monitoring for industrial IoT: 48,000 predictions/second on CPU. Connects to OPC-UA, MQTT, Modbus, Profinet, Profibus, MSMQ. Edge-deployed, no training required. (1,439 words)
- [Telecom Connectors](/content/doc/telecom-connectors-html.html): Telecom connectors for DriftMind: TMF642 Alarm Management, TMF656 Service Problem Management, SNMP, gNMI, NETCONF, Kafka. Compatible with ProOptima, InfoVista, TEOCO, Amdocs, Nokia AVA, NetExpert, Netcool/OMNIbus. (841 words)
- [Industrial Connectors](/content/doc/industrial-connectors-html.html): Industrial connectors for DriftMind: OPC-UA, MQTT, Modbus TCP/RTU, Profinet, Profibus, Ethernet/IP, MSMQ, REST and PI Web API. Compatible with AVEVA PI System, AVEVA InTouch, Siemens WinCC, GE iFIX, Honeywell Experion, ABB 800xA. (810 words)
- [Standard Connectors](/content/doc/standard-connectors-html.html): The developer path into DriftMind. Native REST API (OpenAPI 3.0), official Python SDK, and native Kafka ingestion. The same code works against SaaS, Edge, and Enterprise deployments. (739 words)
- [Picasso Visual Operations for DriftMind](/content/doc/picasso-html.html): Picasso is the visual operations layer for DriftMind. Build N-level forecaster hierarchies, replay historical anomalies and Echo patterns of interest, and navigate from a plant overview down to a single sensor — all backed by your time-series database. (882 words)
- [Privacy Policy & Terms](/content/privacy-html.html): Thingbook privacy policy and terms of service. No data retention, no third-party selling. Read how we handle your data. (626 words)
- [Reproducible Benchmark · NAB + ETT Datasets](/content/benchmark-html.html): Independent benchmark results: DriftMind processes 33,000–48,000 predictions/second — 250–1,225x faster than ARIMA, 140x faster than OneNet — with lower error. CPU-only, no retraining required. (1,212 words)
- [Privacy Policy & Terms](/content/terms-html.html): Thingbook privacy policy and terms of service. No data retention, no third-party selling. Read how we handle your data. (626 words)
- [Engineering Notes](/content/blog-html.html): Technical articles on streaming forecasting, anomaly detection, concept drift, and adaptive AI — written by the engineers behind DriftMind. (618 words)
- [Legal Notice (Impressum)](/content/legal-html.html): Legal notice and Impressum for Thingbook Technologies Ltd as required by German TMG §5 / DDG. (454 words)
- [Upload CSV Data](/content/csvanalysis-html.html): Detect anomalies, explore data structure, and forecast trends directly from your CSV or Excel file. No code, no setup, no data retention. Powered by Thingbook. (133 words)
- [DriftMind Documentation](/content/sitemap-xml.html) (77 words)

## Resources

- [Full Page Index](/index.html): Browse all cached pages with rich metadata
- [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines
- [XML Sitemap](/sitemap.xml): Machine-readable sitemap for crawler discovery
- [Robots.txt](/robots.txt): Crawler directives