TeacHER Explains: How to check AI sources
Checking sources is the habit that turns AI research from convenient into trustworthy.
TeacHER Explains: How to automate safely with AI
Safe AI automation means starting small, limiting data and permissions, adding approval gates, tracking costs, keeping logs, and verifying results.
TeacHER Explains: How to use AI agents safely
Using AI agents safely means limiting scope, protecting private data, requiring approval for risky actions, and verifying the final result.
TeacHER Explains: Local AI privacy and safety checklist
A practical checklist for using local AI at home safely: privacy, file access, tool connections, hallucinations, updates, and responsible expectations.
TeacHER Explains: How to use AI for coding safely
A practical safety checklist for beginners using AI coding assistants, CLI tools, or coding agents.
How to Evaluate AI Agents in Production: Metrics for Reliability, Tool-Use Failures, and Non-Determinism
A practical framework for evaluating AI agents in production using layered metrics—session success, trajectory quality, and action/tool correctness—plus strategies to…
How to Evaluate AI Output in Real Life: A Practical Checklist for Detecting Hallucinations, Bias, and Missing Context
AI can save time, but it can also confidently deliver wrong, biased, or incomplete answers. This beginner-friendly guide gives you…