This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
BugProof is a tool designed to make software bugs reproducible, portable, and easier to debug.
Every developer has experienced this frustrating situation: a bug appears on one machine, disappears on another, and refuses to reproduce in the environment where someone is trying to fix it.
The usual bug report contains a stack trace, a few screenshots, and a message saying, "It works on my machine." But reproducing the issue often requires much more context: source state, execution details, environment metadata, and the conditions that triggered the failure.
BugProof addresses this problem through portable .bug artifacts that capture the context needed to investigate and replay failures.
The idea is simple: instead of sharing only a description of a bug, share an artifact that helps another developer reproduce it.
The potential users include:
- Freelance developers who need to investigate bugs reported by clients.
- Small engineering teams that spend too much time reproducing issues.
- Open-source maintainers who receive incomplete bug reports.
- Teams using AI coding agents that need structured debugging context.
The commercial opportunity is straightforward: developers and small teams pay for engineering time saved. BugProof aims to reduce the time spent gathering missing information, reproducing failures, and communicating debugging context.
Demo
🌐 Website: https://bugproof.pages.dev/
🎬 Video demo: https://vimeo.com/1197136689
Code
💻 GitHub repository: https://github.com/sidinsearch/BugProof
BugProof is being developed around the idea that debugging context should be portable and reproducible rather than trapped in screenshots, terminal logs, and chat conversations.
Check the repository for the current implementation, setup instructions, and supported functionality.
How I Built It
BugProof's core concept is built around a portable .bug artifact that captures the information needed to investigate a failure.
The workflow is centered on three stages:
- Capture: Collect relevant source state, execution context, environment metadata, and a failure fingerprint.
- Share: Package the debugging context into a portable artifact that can be transferred between developers or environments.
- Replay and investigate: Use the captured context to help reproduce the original failure and narrow down the cause.
The next layer is AI-assisted debugging.
Rather than treating an LLM as a generic chatbot, I want to give it structured, task-specific debugging context. An AI system can use the failure information to explain likely causes, identify relevant environment differences, suggest investigation steps, and help developers understand why a failure occurred.
Open-weight models and local inference are particularly interesting for this workflow because source code, stack traces, configuration, and execution logs can contain sensitive information.
The architectural direction is to keep the debugging artifact as the source of truth and make the AI layer replaceable. That means the debugging workflow does not have to depend on a single model provider.
Why Does Open Innovation Matter?
Debugging tools should not require developers to hand over their codebase to an external AI service just to understand a failure.
Open innovation creates several useful possibilities for BugProof.
1. Privacy by design
Source code, environment variables, stack traces, and logs can expose proprietary implementation details or sensitive information. Running a compatible open-weight model locally can allow AI-assisted analysis without uploading that context to a third-party inference API.
Local inference alone does not guarantee privacy, so the implementation must also handle artifact contents, secrets, telemetry, and network requests carefully.
2. Freedom to choose the model
Different debugging tasks have different requirements. A smaller model may be enough to summarize an exception, while a stronger coding model may be more useful for analyzing a complicated failure.
An open-model architecture makes it possible to evaluate and swap models based on quality, latency, hardware requirements, and licensing.
3. Predictable operating costs
A debugging workflow can generate many model requests while analyzing logs, comparing environments, and investigating repeated failures.
Local inference can eliminate per-request inference charges after the initial hardware and setup costs, making usage more predictable for developers who already have suitable hardware.
4. More control over the debugging workflow
The model should work with structured debugging artifacts rather than dictate how the entire product operates. This makes it easier to change prompts, add specialized analysis steps, experiment with different models, and integrate with developer tools.
5. Less dependence on a single provider
If a hosted API changes its pricing, rate limits, or availability, a provider-independent architecture gives developers more options.
The goal isn't to claim that open models always outperform closed models. Hosted models can be stronger at certain tasks, and local inference comes with hardware and maintenance trade-offs.
The advantage is control over where the data goes, which model runs, and how the system evolves.
That is particularly valuable in developer tooling, where the data being analyzed can be the most sensitive part of the application.
My Agent Session
I plan to use DevRelay to capture an agent session showing the implementation workflow, including how BugProof's debugging context can be used to investigate a failure.
I'll add the session link here once it is available.
Prize Categories
Open-source AI and developer tooling.
I'll update this section to reflect the applicable partner categories for the final submission.
Building BugProof has reinforced an idea I care about: debugging should be a reproducible engineering process, not a guessing game.
With portable failure artifacts and a flexible AI layer, I want to make it easier for developers to share failures, understand what went wrong, and spend less time asking, "Can you reproduce it on your machine?"
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