To achieve the "Data Enrichment Layer" in your pipeline, you should focus on augmenting the raw Devpost project and user data with additional, high-signal information pulled from external sources and AI-driven analysis. Here’s how you can approach this, tailored to your use case and leveraging insights from your files and previous discussions: ## Concise Summary - Enrich Devpost data by integrating external signals (GitHub, LinkedIn, YouTube). - Use semantic search and entity extraction APIs (like Perplexity) to automate eligibility checks, detect cheating, and extract project insights. - Generate structured, confidence-scored flags and summaries for downstream AI analysis and judge brief generation. --- ## Detailed Steps for the Data Enrichment Layer **1. External Data Fetching & Linking** - Automatically pull public data from GitHub (repo activity, commit history, README content), LinkedIn (employment, affiliations), and YouTube (demo video transcripts). - Match Devpost users to external profiles using heuristics (name, email, project links) to create a unified project/user profile. **2. Semantic Analysis & Feature Extraction** - Use Perplexity’s API or similar LLM-powered tools to: - Parse and summarize README files and video transcripts. - Extract entities (team members, tech stacks, affiliations, prior hackathon wins). - Detect reused templates or off-the-shelf solutions by comparing code and descriptions across submissions. **3. Automated Eligibility & Integrity Checks** - Cross-reference LinkedIn/Twitter for disallowed affiliations (e.g., employees of sponsor companies, prior winners). - Flag suspicious activity (e.g., GitHub repos created just before the hackathon, copy-pasted code). - Assign confidence scores to each flag, allowing for human review and override. **4. Feature Engineering for Downstream AI** - Produce structured, enriched data objects: - Project summary - Team composition and affiliations - Code originality metrics - Video/demo highlights - Eligibility/integrity flags with explanations **5. Output Aggregated Dataset** - The result is a rich, machine-readable dataset ready for AI analysis, shortlisting, and judge brief generation. --- ## What’s Valid for Your Use Case - **Automated Integrity Screening**: This is the highest-impact feature, as manual eligibility and cheat detection are the biggest pain points for organizers. - **Semantic Enrichment**: LLM-powered extraction and summarization of project artifacts (README, code, video) directly supports both flagging and judge briefing. - **Cross-platform Data Linking**: Validates participant eligibility and project originality, which is critical for hackathon integrity. - **Confidence Scoring & Human-in-the-loop**: Ensures that automated flags are actionable and can be reviewed, reducing false positives. - **Scalability**: The enrichment pipeline should be modular, supporting additional data sources or checks as needed. --- ## Example Enrichment Pipeline | Step | Input | Output | |-----------------------------|----------------------------------|---------------------------------------------| | Fetch GitHub data | Devpost project URL | Repo activity, README, commit history | | Fetch LinkedIn data | User names/emails | Employment, affiliations, prior wins | | Fetch YouTube transcripts | Demo video links | Text transcript, key highlights | | Semantic parsing | README, transcript | Summary, tech stack, originality score | | Eligibility flagging | Aggregated profile | Flags (ineligible, suspicious, clean) | | Output enriched dataset | All above | Structured JSON for AI/shortlisting | --- ## 🧠 BRAIN MODE: Expert Insights - The most robust enrichment layers in hackathon judging tools combine rule-based (regex, heuristics) and AI-driven (LLM, embedding search) approaches. - For your MVP, focus on automating the highest-friction checks: employment eligibility (LinkedIn), code originality (GitHub diffing/semantic similarity), and prior participation (cross-referencing Devpost/LinkedIn). - Use LLMs to generate concise, human-readable summaries and flag explanations—judges and organizers value clarity and auditability. - As you scale, consider integrating more advanced features like plagiarism detection, sentiment analysis on demo videos, and real-time alerts for suspicious patterns. --- ## Multiple Perspectives - **Technical**: Prioritize data normalization and entity resolution for reliable cross-platform linking. - **Operational**: Build the enrichment layer as a stateless microservice, so it can be reused across different hackathons and integrated with various platforms. - **Business**: Position the enrichment layer as a core differentiator—few competitors offer deep, automated integrity screening and judge-ready summaries. --- ### In summary, your Data Enrichment Layer should combine external data fetching, semantic analysis, and automated eligibility checks to transform raw Devpost data into a rich, actionable dataset for downstream AI analysis and judge briefing. This approach directly addresses the biggest pain points in hackathon judging and positions your tool as a must-have for organizers.