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Methodology · Transparency · Data Sources

How AugurPulse
actually works

AugurPulse is a global sensemaking layer. It doesn't report the news. It doesn't predict markets. It doesn't calculate the truth about world conditions. It takes the fragmentary, emotionally charged, frequently contradictory signal environment that constitutes modern global information — and compresses it into interpreted situational awareness. The output is not certainty. The output is calibrated orientation.

Every day you encounter dozens of signals that each seem bad or good or alarming or fine, with no way to understand what they mean together. AugurPulse exists to answer that question. Here's exactly how — every source, every weight, every limitation.


The Score

What 0–100 means

The Structural Pulse is a single number from 0 to 100 representing the interpreted signal pressure across five domains: economic, geopolitical, technological, social, and cultural. It is not an objective measurement of global conditions. It is a calibrated interpretation of the currently available signal environment — what available sources are reporting, what patterns they suggest, and how coherently they point in the same direction.

Think of it less like a thermometer and more like a pressure index: it captures how much strain or stability the observable signal environment is conveying at this moment. 50 is genuine neutral — the baseline for a typical uneventful period. Most of recorded history since 2000 sits between 38 and 62. Anything below 30 or above 70 represents a historically notable signal configuration that requires strong corroboration before being weighted heavily.

Range Regime What it means Historical examples
75–100 Stable Unusually calm. Expansionary conditions, low geopolitical tension, strong institutional trust. Mid-2010s global bull run (2014–2019)
62–74 Broadly Positive Generally constructive. Growth outlook healthy, tensions manageable, no acute crisis. COVID recovery peak, Jun 2021
46–61 Transitional Baseline for most periods. Localized tensions, contradictions, no dominant direction. Most typical months in any year
30–45 Elevated Significant signal pressure across 2–3 domains. Recession signals, active conflict coverage, policy uncertainty elevated. Ukraine invasion, rate-hike stress 2022
16–29 Systemic Pressure Multiple domain signals deteriorating simultaneously. Financial, geopolitical, and social signals all under strain. Dot-com trough (2002), Eurozone crisis (2011)
0–15 Severe Stress Signal environments collapsing across all domains simultaneously. Rare. Requires convergent corroboration before full weight is applied. GFC Lehman collapse (Sep–Nov 2008), COVID crash (Mar 2020)

Live Scoring

How each score is built

Every 6 hours, the system runs a full data collection and analysis cycle. Nothing is recycled between scans. Here is the exact sequence:

1
Data Collection
Eight independent sources fetched in parallel
X/Twitter Trending Topics — top 25 trending topics in the US scraped from trends24.in, including volume counts.

Google Trends — top 20 trending US search queries from Google's public RSS feed, with approximate traffic volumes and associated news articles.

Reddit — top posts from 10 subreddits (r/worldnews, r/politics, r/economics, r/stocks, r/geopolitics, r/technology, r/news, r/investing, r/science, r/environment), sorted by score, from the past 24 hours.

NewsAPI — top headlines across 4 categories (business, technology, science, general) from major US outlets, filtered for freshness since the last scan.

Alpha Vantage — 30 financial news items with pre-scored sentiment (bullish/neutral/bearish) across economy, fiscal policy, financial markets, and technology.

S&P 500 — 35-day price history from Yahoo Finance, computing 30-day return and drawdown from recent peak.

FRED (Federal Reserve) — live macro indicators: VIX, 10Y–2Y yield spread, Baa–Treasury credit spread, unemployment rate, CPI year-over-year, and global economic policy uncertainty index.

Previous scan snapshot — the last score, breakdown, and dominant narrative, for continuity and change detection.
2
Temporal Filtering
Only new information is analyzed
News and Reddit posts are filtered to only include content published after the previous scan (with a 30-minute grace window). This prevents the same story from influencing multiple scans. If filtering removes too many sources, the system falls back to the full pool to maintain signal quality.
3
Claude AI Analysis
An AI analyst synthesizes all signals with live web search
All collected data is passed to Claude (claude-haiku-4-5) with a detailed analytical prompt. Claude is instructed to run 5–6 targeted web searches to corroborate signals, cross-check against macro anchors, and fill any coverage gaps before scoring.

Claude is explicitly calibrated against historical anchors — it knows what a score of 10 means (GFC 2008), what 60 means (post-COVID recovery), and what 50 means (normal). It is instructed to write like a plain-spoken analyst, not a newswire, and to separate observed facts from interpretation.

The output is a structured JSON object containing: overall score, domain breakdown, regime classification, direction, confidence, observed signals, interpreted analysis (kept explicitly separate), domain-specific briefings, and "The Brief" — the plain-English "so what" synthesis.
4
Structural Smoothing
The score is stabilized against transient noise
Claude's raw score reflects current intensity. To prevent a single alarming headline from swinging the headline number wildly, the score is passed through an exponential moving average before display.
Normal update: Structural Pulse = 85% × previous pulse + 15% × raw score
Spike detected: Structural Pulse = 95% × previous pulse + 5% × raw score

A spike is defined as a raw score differing from the structural pulse by more than 17 points. Spikes are displayed separately as Signal Velocity and flagged with ⚡ — so you can see the acute signal without it distorting the underlying trend.
This means the structural pulse moves slowly and requires sustained signal to shift meaningfully — which is accurate. Real conditions don't swing 20 points overnight. Signal Velocity captures the acute reading; the structural pulse captures the durable one.
5
Source Enrichment & Storage
URLs attached, all sources merged, stored in database
Claude identifies outlets by name. The system matches those names back to URLs from the pre-fetched data pool so every source becomes a clickable link. Reddit and news items Claude didn't cite directly are also added to the sources list so the full signal picture is visible. Everything is stored in Supabase and returned by the history API.

The Five Domains

What each domain covers

The composite score is built from five independent domain scores, each weighted equally. Claude assigns each domain a 0–100 score based on the signals it finds in that category. Domain scores are also EMA-smoothed using the same logic as the overall score.

Economic
Markets, growth, inflation, labor
Covers stock markets, bond yields, credit conditions, employment, inflation, central bank policy, corporate earnings, GDP signals, and consumer confidence. Primary sources: Bloomberg, WSJ, Reuters financial coverage, Alpha Vantage sentiment, FRED macro data. A score below 40 here typically means active recession fears, credit stress, or market dislocation.
Geopolitical
Conflicts, diplomacy, sanctions, alliances
Active armed conflicts, military escalations, diplomatic crises, sanctions regimes, border incidents, election instability, and institutional breakdown. Primary sources: Reuters, AP, Al Jazeera, BBC international. Geopolitical shocks often move this domain sharply before they appear in economic data — which is why the system watches it independently.
Technology
AI, infrastructure, regulation, security
AI development and deployment, cybersecurity incidents, platform regulation, semiconductor supply chains, major product launches and disruptions, data breaches, and the broader trajectory of tech policy. Sources include r/technology, tech press, and regulatory filings. This domain tends to be stable with occasional sharp negative spikes (major breach, platform failure, regulatory crackdown).
Cultural
Media narrative, public attention, values
The dominant cultural narrative — what is capturing collective attention, how public institutions are perceived, media framing, major public figures and events, and whether society's tone is constructive or reactive. X/Twitter trending topics and Google Trends are primary inputs here, cross-checked against editorial coverage to separate genuine cultural signal from manufactured noise.

Source Credibility

Not all sources are equal

Claude is explicitly instructed to apply a tiered weighting system when evaluating sources. Confidence in any score rises when Tier 1 sources converge across independent domains. Confidence falls when primary signal comes from a single outlet or from Tier 3 alone.

Tier 1
High weight
Primary evidence. Multiple Tier 1 sources agreeing on a signal is what moves the score confidently in any direction.
Reuters · AP · Bloomberg · WSJ · Financial Times · BBC · NYT · Government official releases · Central bank statements · Verified institutional reports · IMF / World Bank data
Tier 2
Moderate weight
Corroborating evidence. Used to confirm or contextualize Tier 1 signals, not to drive scoring independently.
Major national newspapers · Established broadcast networks (CNN, NBC, ABC, CBS) · Think-tank publications · Specialized industry press (Politico, Axios, The Hill)
Tier 3
Mood indicator only
Never used as primary evidence. Used only to gauge public mood, viral narratives, and grassroots reaction to events that Tier 1 sources have already confirmed. High Tier 3 volume around a confirmed event tells us about public salience, not event severity.
Reddit · X / Twitter · Personal blogs · Opinion aggregators · Viral social posts

Epistemic Architecture

How confidence is determined

Every scan includes an explicit confidence level — not just a score. Confidence reflects the quality and convergence of the available signal environment, not just the direction of the signals. A high-confidence bearish reading is different from a low-confidence bearish reading: the first is a strong signal, the second is a hypothesis.

High
Strong corroboration
Multiple independent Tier 1 sources converge on the same interpretation across different domains. The trend has persisted across multiple time windows. Narrative contradiction across outlets is low.
Example: Reuters, Bloomberg, and AP all independently report deteriorating trade conditions, with corroborating signals in credit spread data and market volatility.
Medium
Emerging signal
Some Tier 1 alignment, but signals are mixed across domains. The trend is emerging but not yet confirmed by multiple independent sources. Interpretation is warranted, but with explicit hedging.
Example: Economic signals suggest pressure, but geopolitical and market indicators remain stable. One strong Tier 1 source with limited corroboration.
Low
Thin or conflicting
Signals are primarily from Tier 3 sources, a single outlet, or are actively conflicting across domains. A low-confidence reading should be treated as a provisional observation, not an assessment.
Example: Reddit and Twitter show high sentiment pressure around an event, but Tier 1 sources have not confirmed structural significance. Or: signals are rapidly shifting and contradicting each other.

Each scan also includes a confidence basis — a plain-English explanation of exactly why that confidence level was assigned. This is not a generic statement. It names specific sources, notes where signals diverge, and flags when the primary signal comes from sources with limited reliability.


Epistemic Honesty

Counter-signals and revision conditions

Every scan explicitly acknowledges signals that contradict or complicate the dominant interpretation. This is not false balance — if signals are genuinely one-sided, that is stated directly. But in almost every real situation, some evidence points in a different direction. Surfacing it is part of the discipline.

The platform calls these Counter-signals: real observations from named Tier 1 or Tier 2 sources that complicate the dominant reading. They appear below the main interpretation in every scan. They do not cancel the interpretation — they qualify it.

"A temporary ceasefire headline may have limited impact if energy markets, freight costs, and geopolitical positioning remain unchanged."

Every scan also includes What Would Change This Reading: 2–3 specific, falsifiable conditions that, if observed, would materially alter the current interpretation. This transforms the system from a static declaration into a living hypothesis.

Example conditions:

These conditions are not predictions. They are the logical structure that underlies any interpretation: every reading implies conditions under which it would change.


Historical Record · 2000–Present

The Historical Reconstruction Engine

The live system uses real-time news and AI analysis to build scores forward from the present. But what was the world's "pulse" during the 2008 financial crisis? During 9/11? During COVID? To answer those questions, we built a completely separate system — the Historical Reconstruction Engine (HRE).

The HRE covers 9,627 days from January 2000 to present. It uses publicly available macro-financial data as proxies for global conditions — indicators that can be pulled retroactively and that have well-understood relationships to real-world stress and stability. It is entirely independent from the live system; scores cannot be directly compared without the client-side calibration applied in the chart view.

"The HRE can tell you that October 2008 was one of the worst months in modern history and that 2017 was unusually calm. It can't tell you exactly why — that's what the live AI analysis layer adds."

Nine indicators are combined into a weighted composite. Each indicator is normalized to a 0–100 scale using step-function calibration anchored to known historical extremes. Missing data days are forward-filled from the last available reading.

CBOE Volatility Index (VIX)
FRED · Daily
20%
The primary fear gauge. Measures implied volatility in S&P 500 options over the next 30 days — effectively, how much the market is paying to insure against a crash. VIX below 15 is calm; above 30 is elevated fear; above 45 is crisis. Spiked to 80+ during COVID crash in March 2020.
Baa–10Y Treasury Credit Spread
FRED · Daily
17%
The gap between investment-grade corporate bond yields and risk-free Treasury yields. When companies are seen as risky, this spread widens. Tight spreads (under 1.5%) signal confidence; wide spreads (over 4%) signal financial system stress. One of the best early-warning indicators for recessions.
GDELT Global News Tone
GDELT Project v2 · Daily (2015+)
15%
Average tone of global news articles about war, conflict, military, sanctions, and crisis events. Captures geopolitical shocks — invasions, bombings, escalations — often before they appear in financial indicators. For dates before February 2015 (when GDELT v2 coverage begins), a hardcoded event shock table provides synthetic values for ~80 major historical events including 9/11, the Iraq War, the Fukushima disaster, and others.
10Y–2Y Treasury Yield Spread
FRED · Daily
14%
The yield curve. When short-term rates exceed long-term rates (an "inversion"), it has historically preceded recessions by 12–18 months. An inverted curve signals that bond markets expect economic trouble ahead. The HRE uses this as a medium-term stress indicator — not a short-term signal, but a reliable structural warning.
St. Louis Financial Stress Index
FRED · Weekly
10%
A composite of 18 financial indicators covering interest rates, yield spreads, and asset prices — built by the St. Louis Fed specifically to measure financial system stress. Negative values mean below-average stress (good). Positive values mean elevated stress. Forward-filled to daily from weekly publications.
S&P 500 30-Day Return
Yahoo Finance · Daily
9%
Market momentum over the past month. Captures whether equity markets are trending up or down — a broad proxy for investor confidence and economic expectations. Supplementary only: the raw S&P 500 close is also stored for context but is not included in the HRE formula.
UMich Consumer Sentiment
FRED · Monthly
8%
Survey-based consumer confidence from the University of Michigan — the most widely-watched sentiment survey in the US. Captures whether ordinary households feel the economy is improving or worsening. Highly correlated with actual conditions and media mood. Monthly; forward-filled to daily.
US Unemployment Rate
FRED · Monthly
5%
A lagging indicator of labor market health. Unemployment rises during recessions with a delay — it's rarely the first signal of trouble, but it confirms structural deterioration. Healthy below 4%; stressed above 5%. Monthly; forward-filled to daily. Weighted lower because of its lag.
Global Economic Policy Uncertainty
FRED · Monthly
2%
A news-based index measuring policy uncertainty across 20 countries, published by the Economic Policy Uncertainty project. Captures trade policy uncertainty, fiscal cliff debates, currency crises, and geopolitical policy instability. Monthly; weighted low due to its broad scope and lag.

How We Know It's Right

Historical calibration anchors

The HRE is calibrated against known historical extremes. Each indicator's normalization step-function was tuned so that the composite score falls within expected ranges for well-understood historical periods. If the GFC didn't score below 20, or if COVID March 2020 didn't score below 15, the calibration would be revised.

These are the anchor events used to validate the system:

9/11 shock week
Sep 2001
30 – 42
Acute short-term shock; markets closed for 4 days. High stress but not systemic failure.
Dot-com crash trough
Oct 2002
24 – 36
Nasdaq fell 78% over 2.5 years. Prolonged stress, not acute crisis.
GFC — Lehman collapse
Sep–Nov 2008
8 – 18
Systemic global banking crisis. One of two readings below 20 in the dataset.
GFC — deepest trough
Feb–Mar 2009
6 – 16
Credit markets nearly frozen. The worst period in the modern dataset before COVID.
Eurozone crisis peak
Jul–Aug 2011
28 – 40
Greece/Italy/Spain sovereign debt stress. Serious but contained by ECB.
Mid-2010s expansion
2014–2019
60 – 78
The longest bull market on record. Low volatility, steady growth, no systemic shocks.
COVID crash
Mar 2020
5 – 16
Fastest bear market ever. Simultaneous shock across all 5 domains. VIX hit 82.
COVID recovery peak
Jun 2021
60 – 72
Vaccine rollout driving optimism; markets near all-time highs.
Peak rate-hike stress
Jun–Oct 2022
28 – 40
Fed raised rates 525bps in 16 months. Equity drawdowns, credit tightening, consumer squeeze.

Honest Limitations

What this is not

We built AugurPulse to be genuinely useful, which means being honest about what it can't do.

It's not a prediction. The score reflects current conditions, not where conditions are heading. A score of 35 today doesn't mean tomorrow will be 30. Signal Velocity shows the direction of momentum, but this is not a forecasting system.
It's US and Western-centric. Most live data sources (NewsAPI, Reddit, Google Trends, X/Twitter, FRED indicators) reflect US and English-language media. Events in non-English-speaking countries may be underweighted unless they reach major wire services.
The HRE and live scores use different methodologies. The HRE uses macro-financial proxies; the live system uses AI analysis of news. A client-side calibration offset is applied in the chart to align them visually, but they are inherently different instruments measuring related things. Compare trends, not exact numbers.
Slow-moving monthly data creates lag. Unemployment, consumer sentiment, and policy uncertainty are reported monthly and forward-filled in the HRE. A sudden shift in these indicators may take weeks to appear in the historical score.
The AI can be wrong. Claude performs web searches and analyzes sources, but it can miss important context, misweight signals, or fail to detect coordinated manipulation of search trends. High confidence is warranted when multiple Tier 1 sources converge; lower confidence should be assumed when primary signal comes from trending topics or single outlets.
6-hour update frequency has inherent latency. In fast-moving situations (market crashes, sudden geopolitical events), the score is at most 6 hours stale. If the scan age indicator shows "STALE," check primary news sources directly for breaking developments.

The Brief

"So what does this actually mean?"

Every scan generates 4–5 plain-English insights that answer the question AugurPulse was built for: not what happened, but what it means and why it matters. These appear at the top of the dashboard under "The Brief."

Each insight has three parts: a domain (which area of the world it covers), a headline that answers "what does this mean" in plain language, and two sentences — one stating the core fact, one answering why it matters or what to watch.

The instructions given to Claude for this section: Write like a smart friend who just read the news for you. Never restate headlines. Be direct: if something is alarming, say so; if it's being overstated, say that too.