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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| title: AI News - Data Collection | ||
| description: A bounty to develop a comprehensive data collection and analysis system for tracking AI industry news, research developments, and market movements | ||
| tags: | ||
| - data-engineering | ||
| - etl | ||
| - bounty | ||
| - ai-news | ||
| - market-research | ||
| - technology-tracking | ||
| function: 🛠️ Tooling | ||
| status: Open | ||
| date: 2024-11-13 | ||
| --- | ||
|
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| ## TL;DR | ||
|
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| **Objective**: Build a comprehensive data collection and analysis system to track AI industry news, research developments, product launches, and market movements, providing timely insights into the rapidly evolving AI landscape. | ||
|
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| ## Key Points | ||
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| - Multi-source AI news collection | ||
| - Research paper tracking | ||
| - Product launch monitoring | ||
| - Market impact analysis | ||
| - Regulatory development tracking | ||
| - Technical breakthrough detection | ||
| - Industry movement analysis | ||
| - Implementation trend tracking | ||
|
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| ## Core Requirements | ||
|
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| The system should aggregate AI-related news and developments from various sources to create a comprehensive view of the AI landscape, tracking everything from academic research to product launches and regulatory changes. | ||
|
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| ## Technical Specifications | ||
|
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| ### Collection Scope | ||
|
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| - Research paper repositories | ||
| - Tech news websites | ||
| - Company blogs | ||
| - Academic institutions | ||
| - Patent filings | ||
| - Conference proceedings | ||
| - Social media discussions | ||
| - Industry journals | ||
| - Government announcements | ||
| - Regulatory frameworks | ||
| - Open source projects | ||
| - Model releases | ||
| - Dataset publications | ||
|
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| ### Data Points to Track | ||
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| - Technical breakthroughs | ||
| - Model capabilities | ||
| - Research directions | ||
| - Product launches | ||
| - Company movements | ||
| - Regulatory changes | ||
| - Dataset releases | ||
| - Performance metrics | ||
| - Implementation patterns | ||
| - Industry applications | ||
| - Ethical considerations | ||
| - Resource requirements | ||
|
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| ## Privacy Measures | ||
|
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| - Use only public information | ||
| - Follow platform terms of service | ||
| - Implement rate limiting | ||
| - Store aggregated insights | ||
| - Hash sensitive identifiers | ||
|
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| ## Implementation Details | ||
|
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| ### ETL Pipeline | ||
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| The ETL pipeline will: | ||
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| - Collect AI news from multiple sources | ||
| - Categorize developments | ||
| - Track research progress | ||
| - Monitor product launches | ||
| - Analyze market impact | ||
| - Generate intelligence reports | ||
|
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| ### Key Features | ||
|
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| - Automated news collection | ||
| - Research paper analysis | ||
| - Product launch tracking | ||
| - Impact assessment | ||
| - Trend analysis | ||
| - Citation tracking | ||
| - Application mapping | ||
| - Performance benchmarking | ||
|
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| ## Processing Stages | ||
|
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| ### Discovery | ||
| - Source monitoring | ||
| - Development detection | ||
| - Impact assessment | ||
| - Pattern tracking | ||
|
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| ### Processing | ||
| - Content categorization | ||
| - Impact analysis | ||
| - Technical assessment | ||
| - Application mapping | ||
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| ### Analysis | ||
| - Trend identification | ||
| - Pattern recognition | ||
| - Impact evaluation | ||
| - Future projection | ||
|
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| ### Reporting | ||
| - News summaries | ||
| - Trend analysis | ||
| - Impact assessment | ||
| - Pattern visualization | ||
|
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| ## Deliverables | ||
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| - Data collection system with multi-source support | ||
| - Processing pipeline for news analysis | ||
| - Research tracking system | ||
| - Visual dashboard showing: | ||
| - Latest developments | ||
| - Research trends | ||
| - Product launches | ||
| - Market impacts | ||
| - Regulatory changes | ||
| - Performance metrics | ||
| - Implementation patterns | ||
| - Documentation covering: | ||
| - System architecture | ||
| - Collection methodology | ||
| - Analysis algorithms | ||
| - Classification criteria | ||
| - API documentation | ||
|
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| ## Success Metrics | ||
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| - Coverage of AI ecosystem | ||
| - Data freshness | ||
| - Classification accuracy | ||
| - Trend detection speed | ||
| - System uptime | ||
| - Collection success rates | ||
| - Insight quality | ||
| - Resource efficiency | ||
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| ## Additional Considerations | ||
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| - Handle multiple AI domains | ||
| - Adapt to new developments | ||
| - Scale with industry growth | ||
| - Provide real-time updates | ||
| - Support custom analyses | ||
| - Generate actionable insights | ||
| - Track emerging capabilities | ||
| - Identify breakthrough signals | ||
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| ## Conclusion | ||
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| This bounty aims to create a comprehensive system for understanding AI industry developments through data-driven analysis. The focus should be on providing actionable insights about technical progress and market impacts while maintaining high standards for data quality and collection practices. |
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earn/community-sentiment-analysis-data-integration-and-ai-bounty.md
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| --- | ||
| title: Community Sentiment Analysis - Data Integration and AI | ||
| description: A bounty to develop an AI-powered system for analyzing community interactions and extracting actionable insights across multiple platforms | ||
| tags: | ||
| - data-engineering | ||
| - ai | ||
| - llm | ||
| - bounty | ||
| - community-analysis | ||
| - sentiment-tracking | ||
| function: 🛠️ Tooling | ||
| status: Open | ||
| date: 2024-11-13 | ||
| --- | ||
|
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| ## TL;DR | ||
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| **Objective**: Develop an AI-powered system that analyzes community interactions across Discord and social media platforms to understand sentiment, needs, and trends within our community, providing actionable insights for community engagement and product development. | ||
|
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| ## Key Points | ||
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| - Multi-platform community data integration | ||
| - Real-time sentiment tracking | ||
| - Topic clustering | ||
| - Engagement pattern analysis | ||
| - Feature request identification | ||
| - Issue detection | ||
| - Community health metrics | ||
| - Trend forecasting | ||
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| ## Core Requirements | ||
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| The system should collect and analyze community interactions from Discord and social media platforms using LLMs to understand sentiment, identify patterns, and extract actionable insights about community needs and preferences. | ||
|
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| ## Technical Specifications | ||
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| ### Collection Scope | ||
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| - Discord messages | ||
| - Channel interactions | ||
| - Twitter mentions | ||
| - LinkedIn engagement | ||
| - GitHub discussions | ||
| - Reddit threads | ||
| - Blog comments | ||
| - Community events | ||
| - Feature requests | ||
| - Support tickets | ||
| - Bug reports | ||
| - Feature usage | ||
|
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| ### Data Points to Analyze | ||
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| - Message content | ||
| - Interaction patterns | ||
| - Response times | ||
| - Topic frequencies | ||
| - Feature discussions | ||
| - Problem reports | ||
| - Success stories | ||
| - Suggestions | ||
| - Questions | ||
| - Engagement levels | ||
| - User journeys | ||
| - Community growth | ||
|
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| ## Privacy Measures | ||
|
|
||
| - Use only public interactions | ||
| - Follow platform terms of service | ||
| - Implement rate limiting | ||
| - Store aggregated insights | ||
| - Hash user identifiers | ||
| - Respect user privacy settings | ||
|
|
||
| ## Implementation Details | ||
|
|
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| ### LLM Pipeline | ||
|
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| The LLM pipeline will: | ||
|
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| - Collect community interactions | ||
| - Process conversation context | ||
| - Analyze sentiment patterns | ||
| - Identify key topics | ||
| - Extract actionable insights | ||
| - Generate trend reports | ||
|
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| ### Key Features | ||
|
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| - Real-time data collection | ||
| - Context-aware analysis | ||
| - Sentiment tracking | ||
| - Topic extraction | ||
| - Pattern recognition | ||
| - Trend identification | ||
| - Impact assessment | ||
| - Early warning system | ||
|
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| ## Processing Stages | ||
|
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| ### Collection | ||
| - Platform integration | ||
| - Message streaming | ||
| - Context preservation | ||
| - Metadata gathering | ||
|
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| ### Analysis | ||
| - Sentiment evaluation | ||
| - Topic clustering | ||
| - Pattern detection | ||
| - Trend identification | ||
|
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| ### Processing | ||
| - Context aggregation | ||
| - Impact assessment | ||
| - Insight generation | ||
| - Recommendation creation | ||
|
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| ### Reporting | ||
| - Sentiment visualization | ||
| - Topic mapping | ||
| - Trend tracking | ||
| - Action items | ||
|
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| ## Deliverables | ||
|
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| - Data integration system with platform support | ||
| - LLM processing pipeline | ||
| - Sentiment analysis system | ||
| - Interactive dashboard showing: | ||
| - Community sentiment | ||
| - Topic clusters | ||
| - Engagement patterns | ||
| - Feature requests | ||
| - Problem areas | ||
| - Success stories | ||
| - Growth metrics | ||
| - Documentation covering: | ||
| - System architecture | ||
| - Integration methodology | ||
| - Analysis algorithms | ||
| - LLM prompt engineering | ||
| - API documentation | ||
|
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| ## Success Metrics | ||
|
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| - Data coverage | ||
| - Sentiment accuracy | ||
| - Topic relevance | ||
| - Pattern detection | ||
| - Response time | ||
| - Insight quality | ||
| - Prediction accuracy | ||
| - Resource efficiency | ||
|
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| ## Additional Considerations | ||
|
|
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| - Handle multiple languages | ||
| - Adapt to community growth | ||
| - Scale with interaction volume | ||
| - Provide real-time insights | ||
| - Support custom analyses | ||
| - Generate actionable recommendations | ||
| - Track emerging issues | ||
| - Identify success patterns | ||
|
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| ## Conclusion | ||
|
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| This bounty aims to create a comprehensive system for understanding community sentiment through AI-powered analysis. The focus should be on providing actionable insights about community needs and trends while maintaining high standards for privacy and data quality. |
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