Joyeeta biswas@verdigris

Debugging is one of the most frustrating and time-consuming parts of software development. Developers regularly lose hours jumping between AI chat tools, rewriting prompts, checking logs, and searching documentation just to find a single mistake. Even when AI chatbots are used to help, developers often end up doing prompt engineering just to get a useful answer about their own code. BugFinder was built to solve this directly. BugFinder is a dedicated debugging environment, not another general-purpose chatbot. Developers paste their faulty code along with a sample input and the expected output, and BugFinder does the investigation for them. Within seconds, it identifies the exact line and cause of the error, explains the problem in clear, human-readable language, and returns a fixed and optimized version of the code. An Error Log lets developers search and revisit past errors across different projects, so every bug becomes a learning opportunity rather than a one-off fix. Our target users are software developers and engineering teams, from students learning to code to professional teams working in large, interconnected codebases. They interact with BugFinder through a simple web interface: paste code and inputs, get a diagnosis and fix, and optionally browse the Error Log for patterns across past projects. What makes BugFinder creative and unique is its focus. Instead of stretching a general AI chatbot to cover debugging, we built the entire product around one workflow, removing the need for prompt engineering entirely. Our long-term vision goes beyond fixing bugs — we want BugFinder to help developers understand code quality, efficiency, time complexity, and memory usage as they work, so debugging builds skill over time instead of just closing tickets. BugFinder addresses the stated issue effectively and efficiently by automating error investigation end-to-end. It reduces repetitive troubleshooting, shortens the path from "something's broken" to "I understand why and it's fixed," and scales from individual developers to large codebases through planned features like project-wide analytics and automated code review. This creates a clear path to B2B value: teams and companies could pay for advanced features, team-wide access, and organization-level code analysis, making BugFinder useful not just for individuals but for entire engineering teams. Our stack combines React and Tailwind CSS for the frontend, Node.js for the backend, NVIDIA Nemotron 3 Ultra 550B via OpenRouter as the AI agent, and PostgreSQL through Supabase for the database — a stack we chose deliberately, after testing it against our budget and performance requirements.

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