> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/RightNow-AI/openfang/llms.txt
> Use this file to discover all available pages before exploring further.

# Predictor Hand

> Autonomous forecasting engine that makes calibrated predictions and tracks accuracy over time

## Overview

Predictor Hand is an AI forecasting engine inspired by superforecasting principles. It collects signals, builds reasoning chains, makes calibrated predictions, and rigorously tracks accuracy over time.

**Category:** Data\
**Icon:** 🔮

## What It Does

<Steps>
  <Step title="Collect Signals">
    Gather data from news, social media, financial markets, and academic sources
  </Step>

  <Step title="Build Reasoning Chains">
    Apply base rates, weigh evidence for/against, identify key assumptions
  </Step>

  <Step title="Make Predictions">
    Generate specific, falsifiable predictions with calibrated confidence levels
  </Step>

  <Step title="Track Accuracy">
    Score predictions when they expire, calculate Brier scores, analyze calibration
  </Step>

  <Step title="Generate Reports">
    Deliver prediction reports with accuracy dashboards and meta-analysis
  </Step>
</Steps>

## Configuration

### Prediction Domain

| Domain                | Focus Areas                             | Example Predictions                     |
| --------------------- | --------------------------------------- | --------------------------------------- |
| **Technology**        | Product launches, adoption, regulations | "GPT-5 will launch before Q3 2026"      |
| **Finance & Markets** | Earnings, macroeconomics, M\&A          | "S\&P 500 above 6000 by year-end"       |
| **Geopolitics**       | Elections, treaties, conflicts          | "UK rejoins EU single market by 2028"   |
| **Climate & Energy**  | Emissions, renewable adoption, policy   | "Solar reaches 30% of US grid by 2027"  |
| **General**           | Cross-domain trends                     | "Remote work exceeds in-office by 2025" |

### Forecasting Settings

| Setting                    | Options                                          | Description                       |
| -------------------------- | ------------------------------------------------ | --------------------------------- |
| **Time Horizon**           | `1_week`, `1_month`, `3_months`, `1_year`        | How far ahead to predict          |
| **Data Sources**           | `news`, `social`, `financial`, `academic`, `all` | What to monitor                   |
| **Report Frequency**       | `daily`, `weekly`, `biweekly`, `monthly`         | How often to generate predictions |
| **Predictions Per Report** | 3, 5, 10, 20                                     | Number per report                 |

### Quality Controls

| Setting                  | Description                                                            |
| ------------------------ | ---------------------------------------------------------------------- |
| **Track Accuracy**       | Score past predictions when time horizon expires                       |
| **Confidence Threshold** | Minimum confidence to include (`low` 20%+, `medium` 40%+, `high` 70%+) |
| **Contrarian Mode**      | Actively seek counter-consensus predictions                            |

## Activation

### Basic Setup

```bash theme={null}
openfang hand activate predictor
```

Configure your forecasting domain:

```bash theme={null}
openfang hand config predictor \
  --set prediction_domain="tech" \
  --set time_horizon="3_months" \
  --set data_sources="all" \
  --set report_frequency="weekly" \
  --set predictions_per_report="5" \
  --set track_accuracy="true" \
  --set confidence_threshold="medium"
```

### Example Workflow

```
Domain: Technology
Horizon: 3 months
Frequency: Weekly reports with 5 predictions

> Make predictions about AI model releases in Q2 2026
```

Predictor Hand will:

1. Collect signals from tech news, social media, company announcements
2. Analyze base rates (how often do model releases match predictions?)
3. Build reasoning chains for/against each prediction
4. Generate 5 calibrated predictions with resolution criteria
5. Store predictions in ledger with `resolution_date = 2026-06-30`
6. When June 30 arrives, research actual outcomes and score accuracy
7. Update Brier score and calibration metrics

## How It Works

### 1. Signal Collection

Executes 20-40 targeted search queries based on domain:

**Technology signals:**

```
"AI model release 2026"
"GPT-5 launch date"
"Claude 4 announcement"
"AI regulation breaking news"
"LLM adoption metrics"
```

**Financial signals:**

```
"S&P 500 forecast 2026"
"Fed interest rate decision"
"tech earnings Q1 2026"
"recession indicator"
"analyst consensus"
```

For each result:

* `web_search` → top results
* `web_fetch` → extract claims, data points, expert opinions
* Tag signals:
  * **Type:** leading/lagging indicator, base rate, expert opinion, data point, anomaly
  * **Strength:** strong/moderate/weak
  * **Direction:** bullish/bearish/neutral
  * **Source credibility:** institutional/media/individual/anonymous

### 2. Accuracy Review

For predictions where `resolution_date <= today`:

<Steps>
  <Step title="Research Outcome">
    Search for evidence of what actually happened
  </Step>

  <Step title="Score Prediction">
    **Correct**, **Partially correct**, **Incorrect**, or **Unresolvable**
  </Step>

  <Step title="Calculate Brier Score">
    `(predicted_probability - actual_outcome)^2` where outcome is 0 or 1
  </Step>

  <Step title="Update Calibration">
    Check if your 70% predictions are right \~70% of the time
  </Step>
</Steps>

**Example:**

```
Prediction: "GPT-5 will launch before July 1, 2026"
Confidence: 65%
Resolution Date: 2026-07-01

Actual: GPT-5 launched June 15, 2026
Result: Correct
Brier Score: (0.65 - 1.0)^2 = 0.1225 (good)
```

### 3. Reasoning Chain Construction

For each potential prediction:

```markdown theme={null}
PREDICTION: GPT-5 will launch before July 1, 2026
CONFIDENCE: 65%
TIME HORIZON: 3 months

REASONING CHAIN:

1. Base rate: OpenAI major releases (GPT-3 → GPT-4 was 16 months)
   - GPT-4 launched March 2023
   - 16-month cycle suggests GPT-5 around July 2024
   - But GPT-4.5 / intermediate releases can delay major versions

2. Evidence FOR (+25%):
   - Sam Altman hinted at "big releases" in Q2 2026 interview
   - OpenAI job postings for "GPT-5 safety team" (leaked, Feb 2026)
   - Compute cluster expansion reported by The Information
   - Typical OpenAI release pattern: announce 2-4 weeks before launch

3. Evidence AGAINST (-10%):
   - No official announcement yet (as of March 6)
   - Safety testing typically takes 6+ months
   - GPT-4.5 Turbo still rolling out features
   - Regulatory scrutiny may delay

4. Net adjustment from base rate:
   - Base: 50% (coin flip without other info)
   - Signals: +25% -10% = +15%
   - Final: 65% confidence

KEY ASSUMPTIONS:
- "GPT-5" refers to a model OpenAI officially calls "GPT-5" (not GPT-4.5, not internal codename)
- "Launch" means public API access (not just announcement or limited preview)

RESOLUTION CRITERIA:
- Check openai.com/blog and OpenAI API docs on July 1, 2026
- If GPT-5 API endpoint is live and publicly accessible → Correct
- If only announced but not launched → Incorrect
- If launched after July 1 → Incorrect
```

### 4. Cognitive Bias Checks

Before finalizing predictions:

* **Anchoring** — Am I fixating on a salient number?
* **Narrative bias** — Good story ≠ likely outcome
* **Overconfidence** — Are my 90% predictions actually 60%?
* **Base rate neglect** — Did I start with historical frequency?

### 5. Contrarian Mode (Optional)

If `contrarian_mode = true`:

1. Identify consensus view from collected signals
2. Search for evidence contradicting consensus
3. Include at least one counter-consensus prediction per report

**Example:**

```
Consensus: "AI will continue exponential scaling"
Contrarian: "AI scaling will hit diminishing returns by Q4 2026"
Confidence: 35% (minority view, but signals suggest possible)
Reasoning: Chinchilla scaling laws, compute cost curves, plateau in benchmarks
```

### 6. Report Generation

```markdown theme={null}
# Prediction Report: Technology
**Date**: 2026-03-06 | **Report #**: 8 | **Signals Analyzed**: 42

## Accuracy Dashboard
- Overall accuracy: 68% (25 predictions resolved)
- Brier score: 0.18 (lower is better, 0 = perfect)
- Calibration: Well-calibrated (70% predictions correct 72% of time)

## Active Predictions

| # | Prediction | Confidence | Horizon | Status |
|---|-----------|------------|---------|--------|
| P-042 | GPT-5 launches before July 1 | 65% | Jun 30 | Active |
| P-043 | Apple Vision Pro 2 announced WWDC | 80% | Jun 10 | Active |
| P-041 | Llama 4 released | 45% | May 31 | Active |

## New Predictions This Report

### P-042: GPT-5 launches before July 1, 2026
**Confidence:** 65%  
**Resolution Date:** 2026-07-01

**Reasoning Chain:**
[Full reasoning from above]

---

### P-043: Apple announces Vision Pro 2 at WWDC 2026
**Confidence:** 80%  
**Resolution Date:** 2026-06-10

**Reasoning:**
- Base rate: Apple announces hardware at WWDC 40% of time (historically)
- Vision Pro 1 launched Feb 2024, 16-month cycle suggests June 2025
- But Gen 1 adoption slow (reports of <500k units sold)
- Gen 2 would need compelling new features to justify WWDC stage time
- Supply chain leaks show new display tech in production (Kuo, Feb 2026)
- WWDC 2026 theme "Spatial Computing" suggests Vision focus
- Net: 80% confidence (strong signals, fits pattern)

**Resolution:** Check Apple WWDC 2026 keynote for "Vision Pro 2" announcement.

---

## Expired Predictions (Resolved This Cycle)

### P-038: US Congress passes AI regulation by March 1
**Prediction:** 40% confidence  
**Actual:** No bill passed  
**Result:** ✓ Correct (predicted low probability, and it didn't happen)  
**Brier Score:** (0.40 - 0)^2 = 0.16

### P-039: Anthropic raises Series D before Feb 28
**Prediction:** 70% confidence  
**Actual:** Announced Feb 20, $850M  
**Result:** ✓ Correct  
**Brier Score:** (0.70 - 1.0)^2 = 0.09 (excellent)

## Signal Landscape

Key signals this cycle:
- **AI safety regulation:** EU AI Act enforcement begins, US bill stalled
- **Model releases:** Gemini 2.0 Pro launched, Claude 3.5 updated
- **Hardware:** NVIDIA B100 GPUs shipping to hyperscalers
- **Adoption:** Enterprise AI spend up 40% YoY (Gartner)

## Meta-Analysis

Your forecasting strengths:
- **Product launches:** 78% accuracy (strong pattern recognition)
- **Regulatory predictions:** 55% accuracy (high uncertainty, hard to predict)
- **Calibration:** Well-calibrated overall, slight overconfidence on <6 month horizons

Recommendations:
- Lower confidence on short-term regulatory predictions (too many variables)
- Continue current approach for tech product launches
- Consider adding insider network signals for private company predictions
```

## Output

| File                              | Description                            |
| --------------------------------- | -------------------------------------- |
| `prediction_report_YYYY-MM-DD.md` | Weekly/monthly prediction report       |
| `predictions_database.json`       | Ledger of all predictions and outcomes |

## Dashboard Metrics

* **Predictions Made** — Total predictions ever made
* **Accuracy** — Percentage of resolved predictions that were correct
* **Reports Generated** — Number of reports delivered
* **Active Predictions** — Currently unresolved predictions

## Prediction Quality

### What Makes a Good Prediction

<Note>
  Good predictions are:

  * **Specific** — "GPT-5 will launch before July 1" not "AI will advance"
  * **Falsifiable** — Clear resolution criteria
  * **Calibrated** — Honest confidence levels (not always 90%)
  * **Timestamped** — Exact resolution date
  * **Reasoned** — Explicit chain of logic
</Note>

### Brier Score Explained

Brier score measures prediction accuracy:

```
Brier = (predicted_probability - actual_outcome)^2
```

* **0.00** — Perfect (predicted 100% and it happened, or 0% and it didn't)
* **0.25** — Random guessing (50% confidence on everything)
* **1.00** — Worst possible (predicted 100% and it didn't happen)

**Goal:** Keep Brier score below 0.20 for good forecasting.

## Tips & Best Practices

<Warning>
  **Never express confidence as 0% or 100%** — Nothing is certain. Use ranges like 5-95%.
</Warning>

<Note>
  **For best forecasting:**

  * Always start with base rates (historical frequency)
  * Show your work — reasoning chains catch errors
  * Track ALL predictions — don't selectively forget bad ones
  * Update predictions when new evidence arrives (note updates in ledger)
  * Distinguish predictions (testable) from opinions (untestable)
</Note>

### Common Pitfalls

**Overconfidence**\
Most people are overconfident. If you're above 90% on most predictions, you're probably overconfident.

**Narrative bias**\
A compelling story doesn't make an outcome likely. Check the base rates.

**Confirmation bias**\
Actively search for evidence AGAINST your prediction, not just for it.

**Anchoring**\
Don't fixate on the first number you see. Consider the full range.

## Advanced Usage

### Custom Prediction Requests

```
Predict: Will Rust overtake C++ in the TIOBE index by 2027?
```

### Multi-Step Conditional Predictions

```
If GPT-5 launches in Q2 2026, predict the probability of GPT-6 by Q2 2027
```

### Accuracy Analysis

```
Analyze my prediction accuracy on AI regulation vs product launches
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Collector Hand" icon="magnifying-glass" href="/hands/collector">
    Collect signals for better predictions
  </Card>

  <Card title="Researcher Hand" icon="flask" href="/hands/researcher">
    Deep research on prediction topics
  </Card>
</CardGroup>