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Open-source dev agent dev who tackles tech debt

Hacker News

Open-source dev agent dev who tackles tech debt

Hi HN, I built a dev agent to reduce code debt in Python repositories. For now, it does one thing very well: adding docstrings to Python classes and methods. It also adds unit tests, though this feature is still a work in progress. # Why? As AI becomes more integrated into development workflows, code debt will only accumulate faster. Refactoring takes time, and no one has time to focus solely on reducing code debt. # Approach While there are many developer tools available, our approach combines language models with deterministic tools to minimize the risk of hallucinations. Specifically, we use: - LibCST: A reliable parser and serializer for Python, ensuring that Ambrogio always generates a valid Python syntax tree. - Interrogate: A docstring coverage tool that helps verify improvements and maintain high standards. - Coverage and Pytest: We identify uncovered lines and test the generated unit tests until they run successfully. The goal is to make it so robust and stable that it can automatically pick and resolve tech debt tasks. # Try It If you have a GitHub open-source repo, you can simply visit https://ambrogio-platform.streamlit.app/ and submit your repository. In a few minutes, you’ll receive a pull request directly on GitHub. (no API key needed or registration) Installation - pip install ambrogio Docstring generation: ambrogio Unit tests generation: ambrogio --mode coverage It supports all models provided by LiteLLM. # Feedback & Discussion I’d love to hear your thoughts on this approach. Do you see pitfalls? What features would be most helpful for you? How do you currently handle tech debt refactoring? Thanks for any feedback you can provide! — Raoul Scalise

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, models · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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