Pieces that belong to no single domain: where fields meet, how to read the literature, and what the work is actually like day to day for the people doing it.
What eight real deep-science roles involve day to day, what qualification each actually needs, and the two axes — bench time and code time — that decide which will suit you.
What machine learning has actually changed in biology, what it has not, and why the loop between a prediction and a measurement is still the part that takes months.
A working order for reading a paper outside your field — figures first, abstract last — and the four questions that separate a supported claim from a plausible one.
AI, nano and bio compared on the axes that decide it in practice: what you need to start, how fast the feedback comes, and whether you can practise without a facility.
