Current Collection Status

Live Data Collection Progress

50,000+ AI Repositories Tracked
131/257 Topic Categories Complete
51.0% Collection Progress
12 Data Sources

Last updated: September 17, 2025 | Collection runtime: 7+ hours

Data Collection Process

Our intelligence platform aggregates data from 12 primary sources to provide comprehensive insights into AI development trends. Here's how we collect, process, and analyze the data:

1

Topic Discovery

257 AI-related keywords and phrases

2

Repository Search

GitHub API queries with rate limiting

3

Data Enrichment

Stars, contributors, languages, releases

4

Cross-Platform Analysis

Reddit, NPM, Stack Overflow correlation

5

Intelligence Generation

Trend analysis and insights

Data Sources

We collect data from 12 distinct sources to ensure comprehensive coverage of the AI development ecosystem:

GitHub Repositories

50,000+ AI repositories with metadata including stars, contributors, languages, and release patterns.

NPM Package Registry

JavaScript/Node.js AI packages with download statistics and dependency analysis.

PyPI Package Registry

Python AI packages including machine learning frameworks and data science tools.

Reddit Communities

11 AI-focused subreddits for community sentiment and discussion trends.

Hacker News

Tech news coverage and developer discussion around AI developments.

Stack Overflow

Developer Q&A activity revealing implementation challenges and pain points.

AI Industry News

Company announcements, funding rounds, product launches, and strategic partnerships.

Docker Hub

Container registry for AI deployment and production usage patterns.

BLS Employment Data

Bureau of Labor Statistics correlation with AI job market trends.

Cloud Marketplaces

AWS, Google Cloud, and Azure AI/ML service adoption metrics.

Content Platforms

YouTube tutorials, Medium articles, and educational content tracking.

Innovation Signals

Patent filings, early-stage startup mentions, and funding announcements.

Rate Limiting & Ethics

Responsible Data Collection

We implement comprehensive rate limiting and ethical data collection practices:

  • GitHub API: 1.2s between search calls, 0.2s between detail calls to respect API limits
  • Public Data Only: All collected data is publicly available information
  • No Personal Information: We do not collect or store personal user data
  • Cache Optimization: Intelligent caching reduces redundant API calls
  • Error Handling: Graceful degradation when APIs are unavailable or rate-limited

Statistical Methods

Data Processing & Analysis

Our analysis framework employs several statistical methods to ensure accuracy:

  • Deduplication: Advanced algorithms remove duplicate repositories across topics
  • Confidence Intervals: 95% confidence intervals for trend predictions
  • Seasonality Filtering: Removes temporal noise from growth signals
  • Cross-Platform Correlation: Multi-source validation reduces false signals
  • Anomaly Detection: Automated flagging of unusual patterns
  • Historical Baseline: Long-term trend analysis for context

Current Limitations & Data Maturity

  • Collection Phase: Currently 51% complete - full dataset expected within 24 hours
  • Historical Data: Limited to September 2025 launch - trend analysis improves over time
  • Sample Bias: GitHub-centric view may underrepresent closed-source development
  • Language Coverage: Stronger coverage of English-language projects and communities
  • API Dependencies: Data quality dependent on third-party API availability
  • Real-time Lag: 2-6 hour delay between events and detection in our system

Data Quality Assurance

Validation & Verification

Multiple validation layers ensure data quality and reliability:

  • Schema Validation: All data conforms to predefined structures
  • Range Checks: Statistical outliers flagged for manual review
  • Cross-Source Validation: Trends validated across multiple platforms
  • Historical Consistency: New data checked against established patterns
  • Manual Spot Checks: Random sampling for quality verification

Transparency Commitment

We believe in complete transparency about our data collection and analysis methods. This methodology page is updated regularly to reflect any changes in our approach.

Questions about our methodology? Contact us with specific questions about data sources, statistical methods, or collection processes.

← Back to Intelligence Dashboard