Nishil Patel
Debugging in the Dark
#1about 1 minute
The hidden costs of traditional debugging processes
Vague bug reports lacking context and reproducibility steps lead to significant time waste and friction between developers and QA.
#2about 1 minute
Core requirements for effective bug resolution
Developers need complete context, clarity on the failure, and reliable reproducibility to move beyond "it works on my machine" arguments.
#3about 3 minutes
Capturing comprehensive bug reports with a tool
A demonstration shows how to record a user session to automatically capture video, system information, console logs, network requests, and user steps.
#4about 1 minute
A unified view for developers and QA
Bug reports are automatically created in tools like Jira, providing a shared screen with all captured data and integrations for backend logs.
#5about 2 minutes
Using AI for root cause analysis and fixes
An AI debugger analyzes logs to provide root cause analysis, suggest solutions, generate test cases to prevent regressions, and offer code fixes.
#6about 1 minute
Reducing bug resolution time by 75 percent
The tool aims to shorten the bug resolution lifecycle significantly, offering a free tier for small teams to get started immediately.
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+1
Matching moments
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Key takeaways and Q&A on debugger internals
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Why debuggers are an essential developer tool
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Providing security by automating critical bug coverage
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The high cost of debugging flaky tests
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Debugging challenges in the cloud's black box environment
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Use developer observability for safe production debugging
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