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Build your first AI chatbot: from prompt to a working assistant
A useful AI chatbot is not just “a prompt connected to a model.” Even the smallest real application needs a few moving parts: instructions that define behavior, user input, an API call, conversation s
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OCR in 2026: document understanding beyond plain text extraction
OCR used to mean one thing: turn pixels into characters.
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Your first tool-using agent: function calling done right
A tool-using agent is not a language model that somehow reaches into your Python process and executes functions by itself.
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Structured output and JSON mode without the model going off the rails
Modern LLM applications often fail at a surprisingly mundane boundary: the model says something intelligent, but your program cannot safely consume it.
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Business rules engines: Drools, GoRules, and when domain logic must not be learned
Machine learning is excellent when the question is predictive:
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sklearn Pipeline and ColumnTransformer: preprocessing that cannot leak
This tutorial targets scikit-learn 1.9.0 , the current stable release as of August 2026. The current documentation describes Pipeline as the mechanism for assembling preprocessing and estimators so th
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ELT and dbt: transforming data where it lives, with tests and lineage
ELT changes the center of gravity of a data pipeline.
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Serving many models on one GPU: Triton, model repositories, and hot-swapping
A GPU is not a Kubernetes pod. Treating every model as if it deserves an entire accelerator can leave expensive hardware mostly idle, especially when you serve small classifiers, regressors, ranking m
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Random forests: why averaging many bad trees makes one great model
A fully grown decision tree is one of machine learning’s most intuitive models.
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Correlation is not causation, but it’s also not nothing: reading correlation matrices right
A correlation matrix is one of the fastest ways to get a rough map of a numeric dataset.