Explain it like I’m not an engineer.

Purpose: A plain-language guide to staying calibrated when using AI: verification, uncertainty, and “don’t outsource your thinking.”

• What it is (and what it isn’t)
• A 5-step “sanity check” loop anyone can use
• Common failure modes: overconfidence, anchoring, “fluency bias”

Replace with your existing draft from prior threads.

Purpose: Why systems literacy matters for non‑STEM people—especially when systems are algorithmic, opaque, and influential.

• Feedback loops, incentives, unintended consequences
• Why “common sense” fails in complex systems
• Starter toolkit: stakeholders → flows → incentives → constraints

V1 stance: Publish a simplified explainer, explicitly labeled as an excerpt / preview of the forthcoming book.

• Analogies first, math last
• Capabilities vs limits
• How to ask better questions & verify outputs

A short note on identity, continuity, and “one organism / many trunks” as a metaphor for model versions, governance, and trust.

Method: A simple bug-report protocol anyone can follow.

1. Repro: steps
2. Expected
3. Actual
4. Evidence: screenshots/timestamps/errors
5. Environment: device/app/version

We’ll publish a downloadable one‑pager in Artifacts & Tools.

Podcast is tracked as part of Education & Outreach for v1. Full hub lives at Podcast.