What happened: Postman users report its desktop app silently installing AI plugins and attempting to share “all API data.” The complaint is now the top-linked GitHub issue on the support repo, with plenty of developers venting in the comments.

Why it matters: Your API workspace can contain credentials, internal endpoints, and very embarrassing request histories. An AI feature that installs itself and starts sharing data without clear consent is less “helpful assistant” and more “guest who rearranged the server room.”

What happened: Simon Willison got Qwen 3.8 27B running locally and demonstrated it interpreting the weights of another model. The laptop experiment suggests the model can inspect neural-network internals rather than merely predict the next token.

Why it matters: If smaller local models can reason about their own architecture—or other models’—that could make debugging, interpretability, and model analysis far cheaper. The rise of the extremely online local AI lab continues.

What happened: The Little Book of Generative AI Foundations is a free mathematical primer aimed at making generative AI concepts more intuitive. It covers the underlying ideas without requiring readers to decode several semesters of graduate coursework.

Why it matters: AI explanations often swing between “just linear algebra” and “ask the chatbot.” A concise, approachable primer could help developers understand what their tools are actually doing, instead of accepting magical thinking with better typography.