Source URL: https://simonwillison.net/2025/Jun/21/my-first-open-source-ai-generated-library/#atom-everything
Source: Simon Willison’s Weblog
Title: My First Open Source AI Generated Library
Feedly Summary: My First Open Source AI Generated Library
Armin Ronacher had Claude and Claude Code do almost all of the work in building, testing, packaging and publishing a new Python library based on his design:
It wrote ~1100 lines of code for the parser
It wrote ~1000 lines of tests
It configured the entire Python package, CI, PyPI publishing
Generated a README, drafted a changelog, designed a logo, made it theme-aware
Did multiple refactorings to make me happier
The project? sloppy-xml-py, a lax XML parser (and violation of everything the XML Working Group hold sacred) which ironically is necessary because LLMs themselves frequently output “XML" that includes validation errors.
Claude’s SVG logo design is actually pretty decent, turns out it can draw more than just bad pelicans!
I think experiments like this are a really valuable way to explore the capabilities of these models. Armin’s conclusion:
This was an experiment to see how far I could get with minimal manual effort, and to unstick myself from an annoying blocker. The result is good enough for my immediate use case and I also felt good enough to publish it to PyPI in case someone else has the same problem.
Treat it as a curious side project which says more about what’s possible today than what’s necessarily advisable.
Via @mitsuhiko.at
Tags: armin-ronacher, open-source, python, xml, ai, generative-ai, llms, ai-assisted-programming, claude, claude-code
AI Summary and Description: Yes
Summary: The text discusses an open-source project that leverages generative AI to automate the creation of a Python library. This is particularly relevant for AI and software security professionals interested in understanding the capabilities and implications of AI-assisted programming.
Detailed Description:
The content highlights an experimental project by Armin Ronacher, which revolves around leveraging large language models (LLMs) for coding tasks. The implementation of such AI-generated libraries is crucial for security and compliance professionals to ponder the ramifications of AI in software development.
Key Points:
– **Project Overview**: The project is named “sloppy-xml-py,” an XML parser characterized humorously by its imperfections. The context signifies an acknowledgment of limitations when using LLMs, even in generating code.
– **AI Assistance**:
– The LLM, named Claude, performed the following tasks entirely:
– Wrote approximately 1,100 lines of code for the parser.
– Created about 1,000 lines of tests to ensure functionality.
– Configured the Python package, including CI and PyPI publishing processes.
– Generated additional components like README documentation, a changelog, and a logo.
– **Significance of Results**:
– This project illustrates the current capabilities of generative AI in real-world applications, showcasing how software development processes can be expedited.
– The final product is deemed sufficient for immediate use, reflecting the practical contributions of AI-generated solutions while also hinting at the need for careful evaluation concerning quality and adherence to standards.
– **Implications for Security**:
– The experiment raises considerations about the security of AI-generated code, specifically:
– The potential for vulnerabilities in automatically generated code.
– The need for rigorous testing and validation of AI-assisted software to ensure compliance with security standards.
– Understanding biases and inaccuracies in outputs, as noted with the LLM’s handling of XML.
This type of exploration into AI capabilities is vital for security professionals who must understand how to manage and mitigate risks associated with AI-assisted tools in software development and deployment.