Change a win once and it lands here, in your LinkedIn text, and in your interview answer. No copy kept in three places drifting apart.
User interviews, usability testing, survey design, qualitative coding, Figma, AI-assisted synthesis.
Northlight University, B.A. Psychology with a minor in Design, expected 2027.
Every line above traces back to a prompt you answered on the evidence page. Nothing here was written for you out of thin air.
One page, standard type, no template tricks. Recruiters skim in seconds and parsing software chokes on decoration, so the design stays out of the way and the numbers do the arguing.
Each line here is the same record that powers the LinkedIn text and the interview answer. Fix a number once on the evidence page and this document is already right.
The published AI-exposure number is built only from O*NET task data, Eloundou et al., and METR*. Pay, growth, networking, portfolio, and clean-up are context and never move it. A 0–100 exposure scale.
* This projection uses METR's established long-run time-horizon trend, not the faster 2026 estimates. Those newer figures come from a task suite near saturation, carry very wide confidence intervals, and measure software tasks specifically, so extrapolating them across all occupations is not yet reliable. See MIT Technology Review, "This is the most misunderstood graph in AI," February 5, 2026, https://www.technologyreview.com/2026/02/05/1132254/this-is-the-most-misunderstood-graph-in-ai/
This dashboard is a sample. Maya Rivera, Northlight University, Northbeam, Lumen, and Verdi Labs are invented, and every figure on this page is sample data, not a real result.
