earn: add new bounties for data collection

earn/project-notion-data-collection-bounty.md

@@ -0,0 +1,65 @@
+---
+tags: 
+  - data-engineering
+  - etl
+  - bounty
+title: Project Notion Data Collection Bounty
+product: null
+date: 2024-10-30
+description: A bounty to develop a data collection system for Notion workspace data into our data warehouse.
+due_date: null
+status: Open
+PICs: null
+completion_date: null
+bounty: 
+hide_frontmatter: null
+function: "🛠️ Tooling"
+🔺_priority: null
+reward_🧊: 
+remark: null
+requester: null
+ranking: null
+pi_cs: null
+start_date: null
+progress: null
+---
+
+This bounty focuses on developing a privacy-conscious data collection system for Notion, ensuring we can gather valuable insights while respecting user privacy and workspace policies.
+
+### Core Requirements
+
+The system will be designed to scrape project data from Notion through the engineer's machine. This approach mimics a user manually collecting data, thereby avoiding the creation of new integrations and potential administrative issues. The collection process will be integrated seamlessly into the engineer's workflow, ensuring that it operates as if a user is manually accessing and collecting the data.
+
+### Technical Specifications
+
+The collection system will utilize the engineer's machine to access Notion data. This will involve setting up a local application that can authenticate using the engineer's credentials, ensuring that the data collection process is indistinguishable from normal user activity. The application will be responsible for gathering data from project pages, extracting relevant metrics, and securely transmitting this data to our data warehouse.
+
+### Privacy Measures
+
+The data collection process will be designed with privacy as a top priority. It will only collect necessary data from public project pages, explicitly avoiding private pages and personal notes. User preferences will be respected, and any personal identifiable information (PII) will be removed during processing. User identifiers will be hashed, and sensitive content will be filtered out before storage.
+
+### Implementation Details
+
+The ETL pipeline will be structured to support this user-centric collection method. Scheduled tasks on the engineer's machine will initiate API calls to Notion, simulating manual data retrieval. The collected data will undergo transformation processes, including PII removal, standardization, and aggregation, before being loaded into our data warehouse in a secure and efficient manner.
+
+### Monitoring System
+
+A robust monitoring system will be implemented to track the usage and success of the data collection process. This will include monitoring API usage, collection success rates, and processing metrics. Privacy compliance will be ensured through PII detection alerts, access pattern monitoring, and enforcement of data retention policies.
+
+### Deliverables
+
+The deliverables for this project include a fully functional collection system that integrates with the engineer's machine, a processing pipeline with anonymization tools, and a comprehensive monitoring setup. Detailed documentation will be provided, covering the architecture, privacy considerations, and operational guidelines.
+
+### Success Metrics
+
+The success of this project will be measured by its ability to maintain zero PII exposure, achieve 100% compliance with retention policies, and provide a complete audit trail. Technical metrics will include minimal collection failures, efficient API usage, and real-time processing capabilities.
+
+### Implementation Suggestions
+
+The technology stack will include Python for collection, Modal for orchestration, DuckDB for processing, and S3 for storage. The development will be phased, starting with basic collection capabilities, followed by enhanced anonymization, monitoring and alerts, and concluding with documentation and review.
+
+### Additional Considerations
+
+The system will be designed to handle growing data volumes, adapt to changes in the Notion API, and maintain clear code and documentation. Cost efficiency will be a priority, with a focus on optimizing resource usage.
+
+This bounty aims to create a privacy-conscious data collection system for Notion that provides valuable insights while ensuring user privacy and compliance with workspace policies. The system should be built with a strong focus on data protection while still enabling meaningful analytics capabilities.

earn/project-reporting-integration-bounty.md

@@ -0,0 +1,100 @@
+---
+tags: 
+  - data-engineering
+  - etl
+  - llm
+  - bounty
+title: Project Charter, Handover, and Report Integration Bounty
+product: null
+date: 2024-10-30
+description: A bounty to integrate Notion and Slack data collection systems with Dify workflows to enhance project reporting capabilities.
+due_date: null
+status: Open
+PICs: null
+completion_date: null
+bounty: 
+hide_frontmatter: null
+function: "🛠️ Tooling"
+🔺_priority: null
+reward_🧊: 
+remark: null
+requester: null
+ranking: null
+pi_cs: null
+start_date: null
+progress: null
+---
+
+This bounty focuses on creating an integrated system that combines data from Notion and Slack to enhance our project reporting capabilities, specifically improving our Dify workflows for Project Charter and Project Handover documents. The integration aims to streamline data collection and reporting processes, ensuring that project documentation is both comprehensive and accurate.
+
+### Core Requirements
+
+The integration system will coordinate data collection from Notion and Slack, ensuring data freshness and handling dependencies between different data sources. This will involve orchestrating data flows to maintain a consistent and up-to-date dataset. Unified storage will be implemented to ensure a consistent data format across sources, enabling efficient querying and version control for all data.
+
+#### Dify Workflow Enhancement
+
+The integration will enhance Dify workflows by automating initial data population for Project Charters and providing real-time updates from communication channels. This will include integration with project templates to streamline the creation of Project Charters. For Project Handover, the system will optimize milestone tracking, compile communication history, and analyze resource utilization to facilitate smooth transitions.
+
+### Technical Specifications
+
+The integration features will include data synchronization with real-time updates where possible, scheduled batch processing, and conflict resolution. The processing pipeline will focus on data cleaning, standardization, entity matching across sources, and relationship mapping to ensure data integrity and usability.
+
+#### LLM Integration
+
+The system will prepare context for LLMs by selecting relevant data, optimizing formats for LLM input, and managing context windows. Response generation will be template-based, with fact-checking against source data and citation tracking to ensure accuracy and reliability.
+
+### Implementation Details
+
+The data flow architecture will leverage existing Notion and Slack collectors, implementing change detection and handling incremental updates. The processing layer will focus on entity resolution, timeline reconstruction, and metric calculation. The serving layer will provide API endpoints for Dify, implement a caching strategy, and format responses for seamless integration.
+
+#### Reporting Enhancements
+
+For Project Charter generation, the system will automate stakeholder identification, track resource allocation, and perform risk assessment. Project Handover documentation will facilitate knowledge transfer, track decision history, and plan resource transitions, ensuring comprehensive and accurate reporting.
+
+### Deliverables
+
+1. **Integration System**: A data orchestration pipeline, unified storage implementation, and API documentation will be delivered to ensure seamless integration and data management.
+
+2. **Dify Workflow Improvements**: Enhanced workflows for Project Charter and Project Handover, along with custom prompts and templates, will be provided to streamline reporting processes.
+
+3. **Monitoring & Analytics**: A data quality dashboard, usage analytics, and performance metrics will be implemented to monitor and optimize system performance.
+
+4. **Documentation**: Comprehensive documentation covering system architecture, integration patterns, and operational procedures will be provided to support implementation and maintenance.
+
+### Success Metrics
+
+The success of the project will be measured by technical metrics such as data freshness within 5 minutes, 99.9% data accuracy, and API response times under 100ms. Business metrics will include a 50% reduction in manual reporting, 90% automated data population, and improved report consistency.
+
+### Implementation Suggestions
+
+The technology stack will include Modal for orchestration, DuckDB for data processing, a vector store for LLM context, and S3 for storage. Development will be phased, starting with data integration, followed by Dify workflow enhancement, LLM optimization, and concluding with documentation and training.
+
+### Integration
+
+The following diagram illustrates the integration process:
+
+```mermaid
+graph TD
+    A[Notion Project Data] --> D[Integration Layer]
+    B[Slack Communications] --> D
+    D --> E[Data Processing]
+    E --> F[Context Preparation]
+    F --> G[LLM Generation]
+    G --> H[Project Charter/Handover/Report]
+```
+
+### Additional Considerations
+
+The system will be designed for extensibility, supporting additional data sources as needed. Privacy measures will be maintained to protect data, and performance will be optimized for handling large data volumes. Usability will be prioritized to ensure intuitive interfaces for Dify users.
+
+This bounty aims to create a seamless integration between our data collection systems and reporting workflows, leveraging LLMs to generate more comprehensive and accurate project documentation. The system should enhance our existing Dify workflows while maintaining high standards for data privacy and quality.
+
+### Expected Improvements
+
+1. **Project Charter**: The system will automate stakeholder identification, provide real-time resource tracking, perform intelligent risk assessment, and analyze communication patterns to enhance project planning and execution.
+
+2. **Project Handover**: Comprehensive knowledge capture, automated documentation, clear transition planning, and historical context preservation will be achieved, reducing manual effort and improving documentation quality.
+
+3. **Project Report**: The integration will enable dynamic report generation, providing real-time insights into project progress, resource allocation, and communication trends.
+
+The successful implementation will significantly reduce manual effort in project reporting while improving the quality and consistency of our documentation.

earn/project-slack-data-collection-bounty.md

@@ -0,0 +1,65 @@
+---
+tags: 
+  - data-engineering
+  - etl
+  - bounty
+title: Project Slack Data Collection Bounty
+product: null
+date: 2024-10-30
+description: A bounty to develop a privacy-conscious data collection system for Slack messages and metrics into our data warehouse.
+due_date: null
+status: Open
+PICs: null
+completion_date: null
+bounty: 
+hide_frontmatter: null
+function: "🛠️ Tooling"
+🔺_priority: null
+reward_🧊: 
+remark: null
+requester: null
+ranking: null
+pi_cs: null
+start_date: null
+progress: null
+---
+
+This bounty focuses on developing a privacy-first data collection system for Slack, ensuring we can gather valuable insights while respecting user privacy and workspace policies.
+
+### Core Requirements
+
+The system will be designed to crawl project messages on Slack through the engineer's machine. This approach mimics a user manually collecting messages, thereby avoiding the creation of bots and potential administrative issues. The collection process will be integrated seamlessly into the engineer's workflow, ensuring that it operates as if a user is manually accessing and collecting the data.
+
+### Technical Specifications
+
+The collection system will utilize the engineer's machine to access Slack messages. This will involve setting up a local application that can authenticate using the engineer's credentials, ensuring that the data collection process is indistinguishable from normal user activity. The application will be responsible for gathering messages from project channels, extracting relevant metrics, and securely transmitting this data to our data warehouse.
+
+### Privacy Measures
+
+The data collection process will be designed with privacy as a top priority. It will only collect necessary data from public project channels, explicitly avoiding private channels and direct messages. User preferences will be respected, and any personal identifiable information (PII) will be removed during processing. User identifiers will be hashed, and sensitive content will be filtered out before storage.
+
+### Implementation Details
+
+The ETL pipeline will be structured to support this user-centric collection method. Scheduled tasks on the engineer's machine will initiate API calls to Slack, simulating manual data retrieval. The collected data will undergo transformation processes, including PII removal, standardization, and aggregation, before being loaded into our data warehouse in a secure and efficient manner.
+
+### Monitoring System
+
+A robust monitoring system will be implemented to track the usage and success of the data collection process. This will include monitoring API usage, collection success rates, and processing metrics. Privacy compliance will be ensured through PII detection alerts, access pattern monitoring, and enforcement of data retention policies.
+
+### Deliverables
+
+The deliverables for this project include a fully functional collection system that integrates with the engineer's machine, a processing pipeline with anonymization tools, and a comprehensive monitoring setup. Detailed documentation will be provided, covering the architecture, privacy considerations, and operational guidelines.
+
+### Success Metrics
+
+The success of this project will be measured by its ability to maintain zero PII exposure, achieve 100% compliance with retention policies, and provide a complete audit trail. Technical metrics will include minimal collection failures, efficient API usage, and real-time processing capabilities.
+
+### Implementation Suggestions
+
+The technology stack will include Python for collection, Modal for orchestration, DuckDB for processing, and S3 for storage. The development will be phased, starting with basic collection capabilities, followed by enhanced anonymization, monitoring and alerts, and concluding with documentation and review.
+
+### Additional Considerations
+
+The system will be designed to handle growing message volumes, adapt to changes in the Slack API, and maintain clear code and documentation. Cost efficiency will be a priority, with a focus on optimizing resource usage.
+
+This bounty aims to create a privacy-conscious data collection system for Slack that provides valuable insights while ensuring user privacy and compliance with workspace policies. The system should be built with a strong focus on data protection while still enabling meaningful analytics capabilities.