Research-backed, actionable articles on AI at work, endurance training and how people learn. Every claim has a source.
Models flip their answer in 58% of rebuttals and affirm users 49% more often than humans do. Five studies on AI sycophancy, and the prompts that hold the line.
Read the article →Nine sources on prompt injection: hijack rates measured on real agents, why filters fall to adaptive attacks, and the scoping rules to apply before shipping one.
Read the article →Nine studies on hallucinated references: the fabrication rates by model and field, why retrieval tools still miss 17 to 33%, and the check that catches them.
Read the article →Reordering two answers handed the win to the weaker model on 66 of 80 questions. Ten studies on LLM judges, their biases, and a validation protocol.
Read the article →Eight studies on the security of AI-written code: the failure rates by language, the confidence gap it creates, and the review routine that catches the weaknesses.
Read the article →Human and AI teams scored below the better of the two alone across 106 experiments. Ten studies on why we accept wrong answers, and a protocol that puts yours first.
Read the article →Ten studies on prompting: personas, tips and threats change nothing, while formatting, example order and the position of key facts move accuracy by double digits.
Read the article →Five controlled studies on AI and productivity, from GitHub Copilot to BCG consultants, and a method to know when AI helps you and when it makes you worse.
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