Collector Hand

Autonomous intelligence collector that monitors targets continuously with change detection

Overview

Collector Hand is an AI-powered intelligence collection engine that monitors any target (company, person, technology, market) 24/7, building a living knowledge graph and detecting changes over time.

Category: Data
Icon: 🔍

What It Does

1. Define Target

   Specify what to monitor (company, person, technology, market, or topic)

2. Continuous Collection

   Run scheduled sweeps across web sources, news, social media, and documents

3. Build Knowledge Graph

   Extract entities and relationships, building a structured intelligence database

4. Detect Changes

   Compare each cycle against previous state, identifying new developments

5. Generate Reports

   Deliver intelligence reports highlighting key changes and trends

Configuration

Target Settings

Setting	Description	Example
Target Subject	What to monitor	Tesla, Elon Musk, Rust language, EV market
Focus Area	Analysis lens	market, business, competitor, person, technology
Collection Depth	How thorough	surface, deep, exhaustive

Collection Schedule

Setting	Options	Use Case
Update Frequency	hourly, every_6h, daily, weekly	How often to collect
Max Sources Per Cycle	10, 30, 50, 100	Budget vs thoroughness

Intelligence Features

Setting	Description
Alert on Changes	Publish event when significant changes detected
Track Sentiment	Analyze and track sentiment trends over time
Report Format	markdown, json, html

Activation

Basic Setup

openfang hand activate collector

Configure your target:

openfang hand config collector \
  --set target_subject="Tesla" \
  --set focus_area="business" \
  --set collection_depth="deep" \
  --set update_frequency="daily" \
  --set alert_on_changes="true"

Example Workflow

Monitor: Anthropic (company)
Focus: Business intelligence
Frequency: Daily updates
Depth: Deep (full articles + sources)

> Monitor Anthropic for product launches, partnerships, and leadership changes

Collector Hand will:

1. Build initial target profile and query set
2. Run daily collection sweeps
3. Extract entities (people, products, events, partnerships)
4. Store in knowledge graph
5. Compare against yesterday’s snapshot
6. Generate collector_report_2026-03-06.md with changes
7. Alert you if critical changes detected

How It Works

1. Target Initialization

Parses the target subject to determine type:

Target Type	Collection Strategy
Company	Products, leadership, funding, partnerships, news
Person	Publications, talks, job changes, social activity
Technology	Releases, adoption, benchmarks, competitors
Market	Trends, players, reports, regulations
Competitor	Product launches, pricing, reviews, hiring

Builds initial query set (10-20 queries tailored to target type):

// Example for company target "Anthropic"
const queries = [
  "Anthropic news",
  "Anthropic product launch",
  "Anthropic partnership",
  "Anthropic funding",
  "Anthropic leadership changes",
  "Anthropic Claude API",
  "Anthropic vs OpenAI",
  "Anthropic latest 2026"
]

2. Collection Sweep

For each query (up to max_sources_per_cycle):

1. Search

   web_search(query) to find recent results

2. Fetch

   web_fetch(url) promising articles, press releases, reports

3. Extract

   Extract entities: people, companies, products, dates, numbers, events

4. Tag

   Attach source URL, timestamp, confidence level, relevance score

Source quality heuristics:

Source Type	Confidence	Examples
Official sources	High	Company website, SEC filings, press releases
Established media	Medium-High	Reuters, Bloomberg, TechCrunch
Blog posts, social	Medium	Personal blogs, Twitter, LinkedIn
Forums, anonymous	Low	Reddit, HN comments (without verification)

3. Knowledge Graph Construction

For each data point:

// Add entities
knowledge_add_entity({
  type: "person",
  name: "Dario Amodei",
  role: "CEO",
  company: "Anthropic",
  last_seen: "2026-03-06"
})

knowledge_add_entity({
  type: "product",
  name: "Claude 3.5",
  company: "Anthropic",
  category: "LLM",
  launch_date: "2024-06-20"
})

knowledge_add_entity({
  type: "event",
  description: "Series C funding round",
  date: "2025-01-15",
  amount: "$750M",
  participants: ["Google", "Spark Capital"]
})

// Add relationships
knowledge_add_relation({
  from: "Dario Amodei",
  to: "Anthropic",
  type: "founded"
})

knowledge_add_relation({
  from: "Anthropic",
  to: "Claude 3.5",
  type: "launched"
})

knowledge_add_relation({
  from: "Google",
  to: "Anthropic",
  type: "invested_in",
  metadata: {amount: "$300M", date: "2025-01-15"}
})

4. Change Detection

Compare current snapshot vs collector_knowledge_base.json:

Identified changes:

Change Type	Significance	Example
New entities	Critical/Important	New executive hire, new product launch
Changed attributes	Critical	Person changed company, new funding round
New relationships	Important	New partnership, acquisition
Disappeared entities	Minor	No longer mentioned in sources

Scoring changes:

function scoreChange(change) {
  // Critical changes (trigger alerts)
  if (change.type === "leadership_change") return "critical"
  if (change.type === "acquisition") return "critical"
  if (change.type === "major_funding") return "critical"
  if (change.type === "product_launch") return "critical"
  
  // Important changes
  if (change.type === "new_partnership") return "important"
  if (change.type === "hiring_surge") return "important"
  if (change.type === "pricing_change") return "important"
  
  // Minor changes
  return "minor"
}

If alert_on_changes = true and critical changes found:

event_publish({
  type: "collector_alert",
  target: "Anthropic",
  changes: [
    "New partnership with Google Cloud announced",
    "Dario Amodei interviewed on AI safety"
  ]
})

5. Sentiment Tracking (Optional)

If track_sentiment = true:

For each source, classify:

• Positive — Praise, success stories, growth
• Negative — Criticism, failures, controversy
• Neutral — Factual reporting

Track trend vs previous cycle:

const sentiment = {
  current: {positive: 60%, neutral: 30%, negative: 10%},
  previous: {positive: 40%, neutral: 50%, negative: 10%},
  trend: "sentiment_improving"
}

6. Report Generation

Markdown format:

# Intelligence Report: Anthropic
**Date**: 2026-03-06 | **Cycle**: 42 | **Sources Processed**: 28

## Key Changes Since Last Report

### Critical
- **Partnership announced**: Google Cloud strategic partnership for enterprise Claude API
- **Product launch**: Claude 3.5 Sonnet now supports 200K context window

### Important
- Dario Amodei interviewed on Lex Fridman podcast about AI safety
- Hiring surge: 15 new ML research positions opened

## Intelligence Summary

Anthropic continues aggressive expansion in enterprise market with Google Cloud
partnership announced March 5. The deal includes preferential pricing for GCP
customers and integration with Vertex AI. CEO Dario Amodei emphasized safety
and reliability in podcast appearance, positioning against OpenAI's rapid
deployment strategy.

Product development remains focused on context length and safety. Claude 3.5
Sonnet update brings 200K context (vs 100K previously), maintaining quality
at extended lengths. Internal benchmarks show 15% improvement on long-document
tasks.

## Entity Map

| Entity | Type | Status | Confidence |
|--------|------|--------|------------|
| Dario Amodei | Person | CEO, active | High |
| Claude 3.5 Sonnet | Product | Updated | High |
| Google Cloud | Company | New partner | High |
| Vertex AI | Product | Integration | Medium |

## Sources

1. [Anthropic announces Google Cloud partnership](https://anthropic.com/news) — confidence: high — Official press release, March 5
2. [Lex Fridman podcast #412](https://youtube.com/...) — confidence: high — Dario Amodei interview
3. [TechCrunch: Anthropic expands context window](https://techcrunch.com/...) — confidence: medium-high — March 6

## Sentiment Trend
Positive: 60% | Neutral: 30% | Negative: 10%  
Trend: ↑ improving (up from 40% positive last cycle)

Saved as: collector_report_2026-03-06.md

Output

File	Description
collector_report_YYYY-MM-DD.*	Intelligence report for each cycle
collector_knowledge_base.json	Cumulative knowledge graph

Dashboard Metrics

• Data Points — Total intelligence items collected
• Entities Tracked — Unique entities in knowledge graph
• Reports Generated — Number of reports delivered
• Last Update — Timestamp of most recent collection

Use Cases

Competitive Intelligence

openfang hand config collector \
  --set target_subject="Competitor Inc" \
  --set focus_area="competitor" \
  --set update_frequency="daily" \
  --set alert_on_changes="true"

Monitor:

• Product launches
• Pricing changes
• Customer reviews and sentiment
• Hiring patterns
• Partnerships

Person Tracking

openfang hand config collector \
  --set target_subject="Sam Altman" \
  --set focus_area="person" \
  --set collection_depth="deep"

Track:

• Public appearances
• Interviews and talks
• Social media activity
• Company changes
• Investments

Market Intelligence

openfang hand config collector \
  --set target_subject="AI agent market" \
  --set focus_area="market" \
  --set collection_depth="exhaustive"

Analyze:

• Market size and trends
• New players
• Regulatory changes
• Technology shifts
• Investment flows

Tips & Best Practices

For best results:

• Start with Daily frequency — Hourly can be noisy for most targets
• Use Deep collection depth for active targets, Surface for monitoring
• Enable sentiment tracking for brand monitoring
• Review knowledge graph monthly to prune outdated entities
• Combine with Predictor Hand to forecast based on collected trends

Collector Hand only uses publicly available information. Never attempt to collect data behind login walls or from restricted sources.

Next Steps

Predictor Hand

Make predictions based on collected intelligence

Researcher Hand

Deep-dive research on discovered topics