BugByBug

Inspiration

Traditional Application Performance Monitoring (APM) is a noise machine. Developers are drowning in stack traces that tell them something is wrong but never how to fix it. We built BugByBug to close the diagnostic gap, turning every error report into a resolved ticket automatically.

What it does

BugByBug is an AI-native observability platform that doesn't just log errors; it heals them. When an exception occurs, our system triggers an asynchronous diagnostic pipeline that builds a forensic picture of the failure, identifies the root cause using deep code reasoning, and generates a syntax-highlighted fix in the native language

How we built it

We leveraged a multi-stage architecture powered by Gemini 3: Instant Triage: Gemini Flash handles high-volume error streams for immediate classification. Deep Reasoning: Gemini Pro performs heavy-lift root cause analysis, utilizing long-context windows to ingest stack traces, user breadcrumbs, and performance metrics simultaneously. Context Injection Engine: We built a custom layer to feed Gemini the exact environmental metadata needed to prevent hallucinations and ensure production-ready code fixes.

Challenges we ran into

Balancing the latency of deep analysis with the need for real-time alerts was our biggest hurdle. We solved this by decoupling the alerting layer from the diagnostic layer, allowing our asynchronous workers to provide deep insights seconds after an error is detected without slowing down the initial notification.

Accomplishments that we're proud of

We successfully moved the industry needle from Observe to Resolve. Seeing the system correctly diagnose a complex race condition and propose a thread-safe fix felt like witnessing the future of DevOps.

What we learned

Context is King: We learned that the quality of a fix is directly proportional to the breadth of context provided. Gemini 3's large context window allowed us to feed in entire stack traces and surrounding code files that would have choked previous models. Reasoning vs. Retrieval: We discovered that Gemini 3 excels at reasoning through logic flows rather than just retrieving patterns, which is critical for identifying non-obvious root causes in distributed systems. Orchestration Efficiency: Building BugByBug taught us how to effectively orchestrate different model sizes using the speed of Flash for initial filtering and the deep intelligence of Pro for complex architectural analysis. The Hallucination Guardrail: We learned that grounding the AI with real-time system metadata is the only way to generate production-safe code snippets, moving AI from a creative tool to a reliable engineering partner.

What's next for BugByBug

We are expanding our private beta to include automated PR generation, allowing BugByBug to not just suggest fixes, but to proactively open GitHub Pull Requests for human approval, completing the self-healing loop.