Editor · 132 articles
Eugene Serbin, page 4
LLM Hallucinations: Why They Happen and How to Reduce Them
Hallucination is the term for when a language model states something false with the same confidence it uses for something true. OpenAI's own research team put it…
Structured Outputs: How to Get Reliable JSON From an LLM
Structured outputs is the feature that stops an LLM's JSON from breaking your code. Instead of asking a model nicely to "please respond in JSON" and hoping, you hand it…
scikit-learn: The Machine Learning Library That Isn't Deep Learning
scikit-learn is a free, open-source machine learning library for Python — and, according to a 2022 Kaggle survey of nearly 24,000 developers across 173 countries, the…
TensorFlow: What It Is, Its Real History, and How to Use It
TensorFlow is Google's "end-to-end platform for machine learning" — its own description, not marketing gloss, since the project genuinely spans training, mobile and…
PyTorch: What It Is, Its Real History, and How to Use It
PyTorch is an open-source deep learning library originally built at Facebook AI Research (now Meta AI), and since September 2022 it's governed independently by the…
Claude Code Tutorial: From First Session to Subagents and Hooks
Claude Code is Anthropic's agentic coding tool: a terminal program, IDE extension, desktop app, and web client that reads your codebase, edits files, runs commands, and…
How to Reduce LLM API Costs: A $295-to-$5.75 Worked Example
Every LLM API bill is the same equation: tokens × price per token . OpenAI's own production guidance puts it in exactly those terms — cut the number of tokens, cut the…
How to Build an AI Agent: A Working Example in 100 Lines
An AI agent is a language model that can call your code, look at what came back, and decide what to do next. That is the whole idea. Anthropic puts it plainly in…
Prompt Caching: How It Works and What It Actually Saves
Prompt caching lets a model skip work it has already done. When two requests begin with the same text — the same system prompt, the same document, the same conversation…
How to Run an LLM Locally With Ollama
Running a large language model on your own computer takes two commands. The first installs Ollama, the second downloads a model and starts a chat: