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Show HN: CaptureFlow – LLM codegen/bugfix powered by live application context (github.com)

6 points by chaoz_ ↗ HN
Hi 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.

3 comments

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veronkek
How it handles edge cases in Python that aren't as straightforward?
chaoz_
We have no good benchmark to estimate the bugfixing ability, it was mostly zero-short "in this case it works" example.
azeevg
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?