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Model cascades and routing: answer cheap when you can, expensive when you must
Large language models have changed how software answers questions, writes content, summarizes documents, and automates workflows. But production AI systems rarely have a single “best” model choice for
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Clustering without labels: k-means, DBSCAN, and how to know if clusters are real
Supervised machine learning starts with a comfortable assumption: examples come with answers.
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Distributions in practice: histograms, KDEs, and when the mean deceives
Statistics often starts with a simple question:
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Probability for ML: Bayes’ rule, likelihood, and why models output logits
Machine learning models often appear mysterious at the point where they produce an answer. A classifier receives an input, performs millions or billions of calculations, and returns something like “sp
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Optimizers beyond SGD: Adam, AdamW, and the learning-rate schedule that matters more
Training a neural network is an optimization problem disguised as an engineering project. We choose an architecture, prepare data, define a loss function, and then repeatedly update millions of parame
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Giving your chatbot memory and personality without prompt bloat
A common first attempt at building a useful chatbot is to place everything inside the system prompt: the assistant’s personality, company rules, user preferences, previous conversations, examples, and
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Deploying vision models to the edge: quantization and latency
Training a computer vision model and deploying that model on a real device are two different engineering disciplines. A network that achieves excellent accuracy in a cloud notebook with a powerful GPU
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The Model Context Protocol (MCP): connecting agents to real tools
Large language models are good at reasoning over text, but real-world software systems rarely live inside text alone. A useful AI agent needs to interact with calendars, databases, ticketing systems,
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Quantization in practice: running a capable model on one consumer GPU
Running a capable language model locally is mostly a memory-management problem.
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Rule-based NLP that still wins: regex, spaCy Matcher, and hybrid rule+ML pipelines
Large language models have changed what is possible in natural-language processing, but they have not changed a more basic engineering fact: when a text pattern is narrow, stable, and precisely specif