Show HN: CaptureFlow – LLM codegen/bugfix powered by live application context
github.comHi Hacker News,
As a dev extensively using GPT-4 for coding, I've realized its effectiveness significantly increases with richer context (e.g., code samples, execution state - props to DevinAI for famously console.logging itself).
This inspired me to push the idea further and create CaptureFlow. This tool equips your coding LLM with a debugger-level view into your Python apps, via a simple one-line decorator.
Such detailed tracing improves LLM coding capabilities and opens new use cases, such as auto-bug fix and test case generation. CaptureFlow-py offers an extensible end-to-end pipeline for refactoring your code with production data samples and detailed implementation insights.
As a proof of concept, we've implemented an auto-exception fixing feature, which automatically submits fixes via a GitHub bot.
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Support is limited to Only OpenAI API and GitHub API. interesting, I wonder what are the odds of introducing new bugs like not closing connections etc. I can imagine many tests passing after such change but actual failure happening on production. Is it something embedded context can help to address? How it handles edge cases in Python that aren't as straightforward? We have no good benchmark to estimate the bugfixing ability, it was mostly zero-short "in this case it works" example.