Tracekit

Inspiration

Students often receive compiler and runtime errors without a clear starting point. Tracekit is a debugging-copilot prototype designed to turn an error message and nearby code into a focused investigation path.

What it does

A student selects a language and supplies an error plus the relevant snippet. The implemented pipeline is designed to classify the failure with NVIDIA Nemotron Nano, search for references using a short pattern-redacted error signature, and ask Nemotron Super to explain a likely cause and propose a focused change. The interface presents stage progress, returned references, a line-level diff, and manual checks for the student.

How we built it

Tracekit uses Next.js and TypeScript, with provider requests handled in server-side routes. The code integrates Nebius Token Factory with Nemotron Nano and Super and uses the Tavily Search API for reference lookup. It includes input-size and origin checks, streamed stage updates, cancellation, source validation, and local rate limiting. The account-visible Nano and Super model IDs were confirmed through model listing.

Challenges we ran into

Keeping the search useful while limiting what leaves the app required a redacted error signature and a clear separation between Tavily search data and the code sent to Nebius. Provider access also has to respect the account budget. Nebius showed a $0.00 balance and metered inference pricing, so I did not send an inference request or claim generated results.

Accomplishments that we're proud of

The prototype connects a structured debugging workflow to a readable result view with references, a proposed diff, and explicit student verification. It does not execute submitted code or claim that a proposed fix passed. Local lint and production build checks pass. One basic Tavily search returned a Stack Overflow result within the free monthly allowance.

What we learned

A helpful debugging answer needs evidence boundaries as well as a proposed fix: search should receive less data than the model stage, sources should be traceable, and a human should still run and verify the change. The prototype still needs a live, budget-approved inference check before its model output can be evaluated.

What's next for Tracekit

Complete a live analysis check only when free inference credit is available, review the five built-in examples, publish the source and demo, and collect measured feedback without claiming unverified accuracy or time savings.

Built With

• nebius
• next.js
• nvidia
• react
• tavily
• typescript