Welcome to the Proof of Usefulness Hackathon spotlight, curated by HackerNoon’s editors to showcase noteworthy tech solutions to real-world problems. Whether you’re a solopreneur, part of an early-stage startup, or a developer building something that truly matters, the Proof of Usefulness Hackathon is your chance to test your product’s utility, get featured on HackerNoon, and compete for $150k+ in prizes. Submit your project to get started! Welcome to the Proof of Usefulness Hackathon spotlight, curated by HackerNoon’s editors to showcase noteworthy tech solutions to real-world problems. Whether you’re a solopreneur, part of an early-stage startup, or a developer building something that truly matters, the Proof of Usefulness Hackathon is your chance to test your product’s utility, get featured on HackerNoon, and compete for $150k+ in prizes. Submit your project to get started! Proof of Usefulness Hackathon spotlight Proof of Usefulness Hackathon spotlight Proof of Usefulness Hackathon Proof of Usefulness Hackathon $150k+ Submit your project to get started Submit your project to get started We sat down with Tombri Bowei to discuss CodeGraph, a tool that instantly turns any GitHub repository into an explorable, live knowledge graph. Built for developers onboarding to new codebases, the project uses GraphRAG and Claude AI to quickly deliver context-aware answers to complex structural questions. CodeGraph What does CodeGraph do? And why is now the time for it to exist? CodeGraph turns any GitHub repository into an interactive knowledge graph—paste a URL and instantly see every function, class, file, and dependency as a live, explorable graph powered by Neo4j AuraDB. Ask questions in plain English and get precise answers via GraphRAG: the system retrieves the relevant code subgraph from Neo4j and passes it to Claude AI for context-aware responses. Built for developers who need to understand unfamiliar codebases fast, without reading every file. Now’s a good time for CodeGraph to exist because software projects are growing exponentially more complex, and developers need smarter, context-aware AI tools that drastically reduce the time spent trying to understand unfamiliar architecture linearly. Who does your CodeGraph serve? What’s exciting about your users and customers? Primary users: software engineers onboarding to new codebases who currently spend 1–2 weeks reading files manually before becoming productive. Secondary users: open-source contributors who need to understand a project's architecture before submitting a PR. Tertiary users: technical interviewers who want to ask specific questions about a candidate's submitted code. The core pain is universal — reading code is slow, non-linear, and mentally exhausting. CodeGraph turns a 2-week ramp-up into a 30-second graph exploration. What technologies were used in the making of CodeGraph? And why did you choose the ones most essential to your tech stack? CodeGraph was built natively around Neo4j to leverage its powerful graph data structuring, which perfectly matches the inherently interconnected architecture of code. To complement the database, the tech stack integrates FastAPI, tree-sitter for parsing, and Cytoscape.js alongside Next.js for an interactive frontend, while utilizing OpenAI embeddings and Claude AI to drive highly accurate GraphRAG capabilities. What is the traction to date for CodeGraph? Originally developed as a hackathon project, CodeGraph is currently in active development with a fully designed schema on Neo4j AuraDB and a public GitHub repository. While currently in its early stages, the underlying market demand is strongly validated by highly valued comparable tools like Sourcegraph, and initial public traction is planned through upcoming Show HN and HackerNoon launch features. CodeGraph scored a 56 proof of usefulness score (https://proofofusefulness.com/report/codegraph) CodeGraph scored a 56 proof of usefulness score (https://proofofusefulness.com/report/codegraph) https://proofofusefulness.com/report/codegraph What excites you about this CodeGraph's potential usefulness? The thing that genuinely excites me is that GraphRAG over a code knowledge graph solves a problem that vector-search-based tools fundamentally cannot. When you ask, "Who calls validate_token and why does it matter?" a naive RAG system retrieves chunks of text that mention the function name. CodeGraph retrieves the actual subgraph—the 2-hop neighborhood of that function in Neo4j—and gives Claude the structural truth: exactly which functions call it, which files they live in, which classes they belong to, and what external modules they depend on. The answer is grounded in the real architecture, not a text similarity score. What makes this scalable is that every developer on earth has this problem. The moment you join a new team, clone a library to debug an issue, or inherit someone else's codebase, you are lost. Current solutions are either too slow (read every file) or too shallow (GitHub search, grep). A knowledge graph is the right data structure for code—code IS a graph, and Neo4j is the right tool to store and traverse it. The graph also compounds in value: once a repo is indexed, you can ask increasingly sophisticated questions—"What are the most central functions by PageRank?" "Which modules have the most external dependencies?" "Show me every function that touches the database layer." These are questions no file reader can answer. That is genuine, lasting utility. Meet our sponsors Meet our sponsors Meet our sponsors Bright Data: Bright Data is the leading web data infrastructure company, empowering over 20,000 organizations with ethical, scalable access to real-time public web information. From startups to industry leaders, we deliver the datasets that fuel AI innovation and real-world impact. Ready to unlock the web? Learn more at brightdata.com. Bright Data: Bright Data is the leading web data infrastructure company, empowering over 20,000 organizations with ethical, scalable access to real-time public web information. From startups to industry leaders, we deliver the datasets that fuel AI innovation and real-world impact. Ready to unlock the web? Learn more at brightdata.com. Bright Data: brightdata.com Neo4j: GraphRAG combines retrieval-augmented generation with graph-native context, allowing LLMs to reason over structured relationships instead of just documents. With Neo4j, you can build GraphRAG pipelines that connect your data and surface clearer insights. Learn more. Neo4j: GraphRAG combines retrieval-augmented generation with graph-native context, allowing LLMs to reason over structured relationships instead of just documents. With Neo4j, you can build GraphRAG pipelines that connect your data and surface clearer insights. Learn more. Neo4j: more Storyblok: Storyblok is a headless CMS built for developers who want clean architecture and full control. Structure your content once, connect it anywhere, and keep your front end truly independent. API-first. AI-ready. Framework-agnostic. Future-proof. Start for free. Storyblok: Storyblok is a headless CMS built for developers who want clean architecture and full control. Structure your content once, connect it anywhere, and keep your front end truly independent. API-first. AI-ready. Framework-agnostic. Future-proof. Start for free. Storyblok: Start for free . Algolia: Algolia provides a managed retrieval layer that lets developers quickly build web search and intelligent AI agents. Learn more. Algolia: Algolia provides a managed retrieval layer that lets developers quickly build web search and intelligent AI agents. Learn more. Algolia: Learn more

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CodeGraph Earns a 56 Proof of Usefulness Score by Building an Interactive Knowledge Graph for GitHub Repositories

CodeGraph Earns a 56 Proof of Usefulness Score by Building an Interactive Knowledge Graph for GitHub Repositories

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