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TDTiger Datatigerdata.hashnode.dev1d ago · 18 min read

The Data Layer for the AI Data Center

Originally published on July 8, 2026. Written by Hien Phan, Noah Hein, Matty Stratton, and Nate O'Farrell. This is Part II of a two-part series on the AI data center stack. Part I, AI's Physical Const

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JLJeremy Longshorejeremylongshore.hashnode.devJul 1 · 4 min read

Nixtla Baseline Lab: Real statsforecast Baselines via MCP Server

Yesterday was plugin scaffolding. Today the Baseline Lab gets real models running real data through a real MCP server. Phase 2: Plugin Manifest and MCP Config The Baseline Lab plugin needed two pieces

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BSBerkan Sesensesenai.hashnode.devJun 9 · 14 min read

Changepoint Detection: Finding Regime Shifts in Financial Data

Markets do not stay in one regime. The S&P 500 can cruise at 10% annualised volatility for months, then a crisis hits and volatility doubles overnight. Any model trained on the calm period is useless

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SMScott McMahanaitransformeronline.hashnode.devApr 13 · 3 min read

AI Time Series Forecasting Has Crossed a Threshold

For a long time, time series forecasting was a solved-enough problem. ARIMA and its variants handled most production use cases. Seasonal decomposition filled in the gaps. The tooling was mature, the m

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RLRisingWave Labsrisingwave.comApr 2 · 11 min read

Real-Time IoT Data Processing: From Sensor to Dashboard in Minutes

Your factory floor has 10,000 sensors generating temperature, humidity, and pressure readings every second. That is 600,000 data points per minute flowing into your system. A traditional batch pipeline processes this data on an hourly schedule, meani...

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BSBerkan Sesensesenai.hashnode.devMar 31 · 15 min read

Hidden Markov Models: When Clusters Have Memory

In the previous post, we clustered data into groups with K-Means and GMMs. Both methods assume observations are independent — each data point is analysed in isolation, with no regard for what came bef

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AAAbstract Algorithmsabstractalgorithms.hashnode.devMar 9 · 14 min read

Elasticsearch vs Time-Series DB: Key Differences Explained

TLDR: Elasticsearch is built for search — full-text log queries, fuzzy matching, and relevance ranking via an inverted index. InfluxDB and Prometheus are built for metrics — numeric time series with a

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CNChris Naughtontensorops.hashnode.devFeb 22 · 2 min read

Rebuilding Legacy ML Projects: A Blueprint for Showcasing Real-World Skills

Justin Norman, a machine learning engineer, faced a common industry dilemma: how to showcase the impressive projects he built during previous roles when the code is owned by employers and often outdated. His solution? Rebuild them from scratch using ...

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TITech Insights Hubtopperblog.hashnode.devFeb 12 · 8 min read

Time-Series Compression: TimescaleDB

Why Traditional Compression Approaches Fail at Scale Standard PostgreSQL table compression using TOAST or filesystem-level compression provides minimal benefits for time-series workloads. TOAST compression operates row-by-row, missing opportunities t...

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MFMaheen Fatimamaheen-ai.hashnode.devJan 22 · 5 min read

Human Activity Recognition with Machine Learning

The Motivation: Why Motion Intelligence? Every day, our smartphones capture a silent symphony of data through accelerometers and gyroscopes. This data is the key to unlocking innovations in elderly healthcare (fall detection), industrial ergonomics, ...

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#time-series

Explore Hashnode

Trending tags this week

The Data Layer for the AI Data Center

Nixtla Baseline Lab: Real statsforecast Baselines via MCP Server

Changepoint Detection: Finding Regime Shifts in Financial Data

AI Time Series Forecasting Has Crossed a Threshold

Real-Time IoT Data Processing: From Sensor to Dashboard in Minutes

Hidden Markov Models: When Clusters Have Memory

Elasticsearch vs Time-Series DB: Key Differences Explained

Rebuilding Legacy ML Projects: A Blueprint for Showcasing Real-World Skills

Time-Series Compression: TimescaleDB

Human Activity Recognition with Machine Learning