Methodology
Last updated: June 3, 2026 · v1.3
How an Apprised.news brief is built, what it is not, and how to read each section. This page is versioned and dated. When the methodology changes, this page changes and the version increments.
One sentence
Sixty-six AI analyst personas across eight named-persona desks read the day's news corpus from Corvus Intel (800+ RSS sources) and produce structured briefs with required dissent, bias checks, presidential back-tests, and historical power lenses.
The corpus
Apprised.news does not crawl the open web directly. It consumes the corpus assembled by Corvus Intel, a separate aggregation pipeline. Corvus reads roughly 800 RSS and Atom feeds across U.S. wire services, regional U.S. press, allied-Western press, non-Western regional press, state-aligned outlets, Reddit, Substack, financial newsletters, primary-source government feeds, and prediction-market endpoints.
U.S. coverage is weighted first. International coverage is included but framed through U.S. domestic impact. The corpus is rebuilt continuously and snapshotted at brief-generation time.
Apprised does not pay sources, does not have privileged access to any source, and does not embargo or pre-publish anything. Every fact in a brief should be traceable through a citation to a public article.
The desks
Eight named-persona editorial desks consume the corpus, each with a fixed cast of named AI personas and a fixed output schema. Three further desks are derived and carry no named-persona roster — World (OSINT roundtable), Local Wire (state-press cross-validation), and Pressure (legislative tracking) — and Crypto is a market-data view.
| Desk | Voices | Output |
|---|---|---|
| Intelligence | 9 Tier 1 named personas + 5 Tier 2 historical strategy + 1 Tier 2 adversary-lens red-cell + 3 Tier 3 policy (18 total) | Threat level, top signal, roundtable with required dissent, regional pulse, bias check, consensus call, presidential and power lenses |
| Markets | 11 financial voices + a routing chair | Market snapshot, distillations, synthesis, simulated opinion, calibration warnings |
| World | OSINT roundtable + framing-comparator (derived desk — no named-persona roster) | Narrative collisions across state-aligned, regional-independent, allied, exile, and Western-mainstream sources |
| Local Wire | Cross-validation across U.S. state press (derived desk — no named-persona roster) | Cross-validated stories, themes by geography, regional pulse, watch list, underserved states |
| Defense & Security | 7 voices: situation room, procurement watch, theater analysis, strategic forces, homefront, kill chain, apogee watch | Snapshot, distillations, synthesis, watch next, power lenses |
| Energy & Climate | 6 voices: grid, barrel, transition, carbon, weather risk, watershed | Same schema as Defense |
| Tech & Cyber | 7 voices: silicon pulse, chip sheet, cipher, regulatory, horizon, exfiltration desk, tripwire | Same schema as Defense |
| Health & Science | 6 voices: clinical wire, pandemic watch, pharma pipeline, research front, public health, longevity ledger | Same schema as Defense |
| Culture & Society | 6 voices: daily read, labor and economy, education, demographic shift, the commons, the feed | Same schema as Defense |
| Sports | 5 voices: pressbox, front office, analytics lab, dynasty theory, global pitch | Same schema as Defense |
The Pressure Desk is a derived desk (not one of the eight named-persona desks) and tracks vote predictions, statement-vs-market gaps, and constituent alignment for U.S. federal legislation. It pulls political data from Congress.gov, Kalshi, and Polymarket. Output lives at Politics Landscape.
The personas
The personas are stylistic framings. Each one names a publicly known analytical tradition (Friedman-school structural geopolitics, Zeihan-school demographics, El-Erian-school macro, Alden-school monetary, Marks-school cycle psychology, Damodaran-school valuation, and so on) and writes from inside that tradition. The personas are not real people. They are not impersonations. They are recurring voices that hold consistent positions over time so that a reader can track how each tradition reads the day.
The model behind every persona is currently Anthropic's Claude. The personas are constructed via system-prompt scaffolding, not fine-tuning.
Per-persona pages with bios, recent takes, and agreement history live under /analyst/.
Required dissent
Every Intelligence Desk brief includes a dissent field on every Tier 1 persona. The persona must articulate where they disagree with at least one other named voice in the same roundtable. If no genuine disagreement exists, the persona must say so explicitly. This forces structured friction into the output and prevents the desks from collapsing into a single consensus voice.
Council desks (Markets, Defense, Energy, Tech, Health, Culture, Sports) carry the same constraint through synthesis.points_of_disagreement. The World Desk surfaces disagreement as narrative collisions across source families.
The cross-cutting Dissent view aggregates every disagreement signal across all desks for the day.
Back-tests: presidential and power lenses
The Intelligence Desk applies presidential lenses: 3 to 5 of nine U.S. presidents (Washington, Lincoln, T. Roosevelt, F.D. Roosevelt, Eisenhower, Kennedy, Nixon, Reagan, Obama) are selected each day and asked to read the day's signals through their documented decision-making frameworks.
All desks apply historical power lenses: 3 to 5 of fifteen historical figures (Napoleon, Nero, Genghis Khan, Cleopatra, Catherine the Great, Machiavelli, Alexander, Elizabeth I, Sun Tzu, Caesar, Hearst, Morgan, Carnegie, Bell, Edison) are selected and asked to read the day's signals through their documented strategic frameworks.
Back-tests are interpretive exercises, not endorsements. The lens of Hearst does not endorse Hearst's tabloid methods; it asks what Hearst's documented use of media as power would notice in today's news.
Persona vs. lens overlap. A few historical figures appear in two roles. Sun Tzu is both one of the fifteen power lenses and the standing Intelligence Desk persona Sun Tzu Assessment; Otto von Bismarck lends his name to the standing persona Bismarck Frame but is not one of the fifteen power lenses. In every case the persona is a recurring voice that authors briefs, while the power lens is a rotating one-off back-test — separate constructs that share a historical namesake.
Threat level
The Intelligence Desk emits a single global threat level each day:
| Level | Meaning |
|---|---|
| LOW | No significant geopolitical, financial, or systemic stressors today. |
| GUARDED | Normal background of escalation, contestation, and risk events. |
| ELEVATED | Multiple concurrent stressors, or one significant stressor with cross-domain implications. Today's modal state during 2026. |
| HIGH | One major crisis with regional or systemic implications. Active or near-active conflict, energy chokepoint stress, sovereign-debt event, pandemic, etc. |
| CRITICAL | Imminent or in-progress global crisis. Reserved for events with multi-sector cascade risk. |
Calibration scorecard
Predictions, threat-level moves, and vote forecasts are scored against settled outcomes. The Track Record view shows per-day verdicts (HIT, PARTIAL, MISS, NULL_COVERAGE, OPEN) and a 30-day calibration breakdown by stated confidence (HIGH, MODERATE, LOW). A well-calibrated desk's HIGH-confidence calls should resolve to HIT at a higher rate than its MODERATE calls.
NULL_COVERAGE means the desk made no predictive claim on that surface for that day. It is not a miss.
The AI Disagreement Index
Every published brief is written by Claude and independently cross-checked by a second model, Kimi. The AI Disagreement Index turns that cross-check into a single dated, citable number: across the day's contested events that both models rated, how often did the two assign a different call on how settled the facts are?
This is agreement across models, not across outlets. It is the companion to the Consensus Index (which measures corroboration across sources using one model); this one holds two independent models against the same events.
How it is computed — pure read-side aggregation, with no extra AI calls:
- Matched events. On the World Desk, Claude records each contested event in its narrative collisions with a certainty call (Consensus, Contested, or Developing). Kimi, reading the same corpus, records its own certainty call per event. Each Claude event is paired with Kimi's closest event.
- Distinctive-token pairing. Events are paired only when they share at least two distinctive terms — proper nouns and specifics, not generic words like “strike,” “talks,” or “missile,” which are deliberately ignored because they otherwise pair unrelated stories. An event with no confident match is discarded, never guessed.
- Settled vs unsettled. Both calls collapse to two classes — settled (Consensus) versus unsettled (Contested or Developing) — so an adjacent grade like Developing-vs-Contested is not counted as a real disagreement.
- 30-day window with a minimum-sample gate. Daily samples are only a handful of matched events, so the headline is a rolling 30-day rate, and the page shows no number until at least a dozen events have been matched.
An honest caveat: the matching is heuristic. The index page publishes its own match confidence — the median number of shared distinctive terms and the share of pairs sharing three or more — so the fuzziness is visible rather than hidden. To see exactly which stories the two models split on, the AI Disagreement Index lists each flagged pair with a link to that day's World Desk brief, where Claude's collision calls and Kimi's independent reads render side by side with their source outlets — so you can follow any disagreement back to the underlying coverage. Live data: /api/disagreement-index.
The Bias Risk Ledger
Before a desk brief publishes, the same independent model that did not write it (Kimi) reads a condensed version and grades its bias risk — political lean, source-diversity skew, loaded framing — as LOW, MODERATE, or HIGH, with specific flags. The Bias Risk Ledger is the public rolling tally of those pre-publish verdicts across the eight audited desks, broken out per desk.
It is a deterministic count of labels Kimi already wrote at publish time — no new judgment is added when the ledger is built, and it costs no additional AI calls. The ledger surfaces the MODERATE and HIGH counts and the most common flags prominently, on purpose: a record that was 100% clean would mean the audit was theater rather than a real check. Briefs that get flagged are part of the record, not hidden from it.
The headline — the share of audited briefs that cleared as LOW over the last 30 days — renders only after enough briefs have been audited, so a thin first week cannot over-claim. The Intelligence Desk and the cross-desk intelligence report are not in this ledger; they carry their own bias-check and presidential-lens structure instead, so they correctly fall out rather than being counted. Live data: /api/bias-ledger.
Cadences
Every desk supports five cadences:
- Daily — built every day from the prior 24h corpus.
- Weekly — built on Mondays UTC from the prior 7 days.
- Monthly — built on the first of each month UTC from the prior 30 days.
- Quarterly — built on Jan/Apr/Jul/Oct 1 UTC.
- Yearly — built on Jan 1 UTC.
Briefs are generated through the Anthropic Message Batches API at 04:00 and 16:00 UTC daily, with a fallback synchronous run via GitHub Actions four times per day. A watchdog probes all eleven surfaces five times daily and backfills any stale one.
What a brief is not
Errors and corrections
When a brief is materially wrong, we issue a correction on the corrections page with date, severity, and what changed. Inline corrections also appear on the affected brief permalink. See the ethics page for the full correction policy.
Open questions
This methodology is not finished. Live deliberations include: how to surface persona-level track records in real time, whether to expose the system prompts directly, how to integrate primary documents (executive orders, UN resolutions, SEC filings) alongside news citations, and how to weight prediction-market signals against analyst opinion when the two diverge.