Practical guides on reducing AI costs and optimizing token usage.
We open-sourced the tree-sitter detector and Typer CLI behind erabot.ai under MIT. Install it with pip, run it locally, read the code on GitHub. Here is what we kept open, what stays closed, and why.
Understanding context windows, token waste patterns, and how to scan your codebase for costly LLM calls that silently drain your AI budget.
A practical guide to reducing OpenAI API costs through token optimization, model selection, caching, and code-level fixes — with real dollar savings examples.
Long context windows from Claude and GPT-4 seem like a superpower — until you see the bill. Learn why context length is the biggest hidden cost driver in AI applications.