HackTrace

Execution Intelligence for Developers

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Inspiration

Debugging runtime failures is still one of the hardest parts of software development.
Logs are noisy, stack traces are incomplete, and understanding how execution actually failed often takes more time than fixing the bug itself.

HackTrace was inspired by this gap.

Instead of treating failures as isolated errors, HackTrace captures execution traces, visualizes how code runs, and learns from failures over time. The goal is to help developers move from guessing to understanding.

What it does

  • Captures runtime execution traces from applications
  • Visualizes execution flow as an execution graph, making root causes easy to spot
  • Detects whether a failure is new or a known recurring pattern
  • Provides technical AI explanations and suggested fixes, without replacing developer judgment

HackTrace treats AI as assistance, not a dependency — the system continues to work even if AI services are unavailable.

How we built it

  • A Node.js backend captures and stores execution traces
  • Traces are normalized and converted into execution graphs
  • A pattern learning layer classifies failures as known or new anomalies
  • A React + TypeScript frontend visualizes traces, graphs, and insights
  • AI APIs are used to generate technical explanations and fixes in a fail-safe manner

The architecture is intentionally modular, allowing visualization, learning, and AI reasoning to evolve independently.

Challenges we ran into

  • Designing an execution graph that scales without clipping or visual noise
  • Evolving the database schema as learning features were added
  • Handling external API failures gracefully (AI and voice services)
  • Ensuring the system remained reliable under free-tier cloud constraints

These challenges shaped HackTrace into a more robust and production-minded system.

What we learned

  • Designing developer tools requires clarity over cleverness
  • Execution visualization provides far more insight than raw logs
  • AI integrations must be resilient and optional, not blocking core functionality
  • Schema evolution and backend–frontend alignment are critical in real systems
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