Huu Huynh@borebus

CodeMap is a web application that helps developers understand large and unfamiliar codebases through interactive visualizations. Upload a GitHub repository, ZIP archive, or paste individual files — CodeMap parses every function and class using tree-sitter AST analysis, generates AI-powered explanations of each component, and renders the result as an interactive graph you can explore. It has 3 different visualizations which are Block view (expandable function cards), Dependency graph (edge-focused layout), and Mind-map (radial/hierarchical overview). Each function gets a plain-English breakdown of its purpose, inputs, outputs, and internal logic (powered by OpenAI) for users to actually understand the codebases too with the visualizations.

This project is an autonomous AI task routing agent designed to solve a diverse set of natural language tasks while attempting to minimize the number of Fireworks AI tokens consumed. Rather than sending every request to a cloud model, the agent intelligently decides whether a task can be completed locally using a lightweight GGUF language model or whether it requires a more capable Fireworks AI model. This approach significantly reduces cloud inference costs while maintaining a high level of accuracy. To improve reliability, the system includes an answer validation stage. Local responses are checked for structural correctness, formatting requirements, and category-specific constraints before being accepted. If a response fails validation or the task is determined to be too complex for the local model, the agent automatically falls back to the most appropriate Fireworks AI model selected from the list of models provided at runtime. This allows the system to preserve accuracy while minimizing unnecessary cloud usage.

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🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.

To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. ADP members can access AI tutorials, experts, and community support. In addition, new members can claim a free month of DeepLearning.ai pro, $100 in AMD GPU cloud credits, and $50 in Fireworks AI API credits. All hackathon participants will receive on hackathon launch additional compute and API credits for their projects. Ready to build what's next on AMD? Sign up with AMD below to secure your spot.