February 19, 2026
Mortals vs Models: Puns, Price & Privacy
Large Language Models for Mortals: A Practical Guide for Analysts with Python
A $60 AI playbook drops—readers demand evals, local models, and an HN discount
TLDR: A practical Python guide for AI chat tools launched at $59.99/$49.99 and promises real-world workflows. Commenters demand rigorous testing, push for local privacy-friendly models, and ask for a discount—plus jokes about “Large Lagrange Models” and confusion over crime examples, making price and proof the main battleground.
A new how-to book for using AI chat tools with Python just landed, promising hands-on guidance with OpenAI, Anthropic, Google, and AWS. It’s 354 pages, packed with 250 code snippets, 80 screenshots, and real examples (yes, some are about crime analysis). There’s a sample and a table of contents, but the community came for the drama: price, privacy, and proof. The top chorus? Evaluation, not vibes. One analyst says most guides stop after a few cute examples; what they want is systematic testing to see if prompts work across the whole domain. The author mentions measuring accuracy and costs, which hints at rigor—but readers want receipts. Second hot front: local models vs cloud models. Privacy hawks worry about sensitive data leaking to third-party services and want more push for models that run on your own machine. Then there’s the wallet war: fans want the book, but they’re begging for a HN discount. For levity, someone misread the title as “Large Lagrange Models,” and another asked if “CRIME” is an acronym or just, you know, actual crime. TL; entertainment: people like the practical vibe, but they’re loudly demanding cheaper, safer, and tested, not just shiny.
Key Points
- •New book released: “Large Language Models for Mortals: A Practical Guide for Analysts with Python.”
- •Available in paperback ($59.99) and epub ($49.99); 354 pages, letter-sized; 250+ Python snippets and 80+ screenshots.
- •Covers Python use of LLM APIs across OpenAI, Anthropic, Google, and AWS Bedrock.
- •Topics include API basics (e.g., temperature, structured outputs, reasoning, caching, cost), RAG, agents, testing, and accuracy measurement.
- •Includes a chapter on LLM coding tools (GitHub Copilot, Claude Code via AWS Bedrock, Google’s Antigravity editor) and some local model examples; first ~60 pages available for preview.