Source URL: https://simonwillison.net/2025/Jun/2/kenton-varda/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Kenton Varda
Feedly Summary: It took me a few days to build the library [cloudflare/workers-oauth-provider] with AI.
I estimate it would have taken a few weeks, maybe months to write by hand.
That said, this is a pretty ideal use case: implementing a well-known standard on a well-known platform with a clear API spec.
In my attempts to make changes to the Workers Runtime itself using AI, I’ve generally not felt like it saved much time. Though, people who don’t know the codebase as well as I do have reported it helped them a lot.
I have found AI incredibly useful when I jump into other people’s complex codebases, that I’m not familiar with. I now feel like I’m comfortable doing that, since AI can help me find my way around very quickly, whereas previously I generally shied away from jumping in and would instead try to get someone on the team to make whatever change I needed.
— Kenton Varda, in a Hacker News comment
Tags: ai-assisted-programming, generative-ai, cloudflare, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the author’s experience using AI to accelerate the development of code libraries and navigate complex codebases. It highlights the effectiveness of AI in facilitating programming tasks, making it particularly relevant for professionals in AI and cloud computing.
Detailed Description: The author reflects on their experience with using AI technologies in software development, specifically in the context of cloud infrastructure and API standardization. Here are the key insights:
– **Time Efficiency**: The author reports a significant reduction in time spent developing a library on Cloudflare’s platform, estimating that what would have typically taken weeks or months was accomplished in just a few days with AI assistance.
– **Use Case Suitability**: The author emphasizes the ideal conditions where AI excels—implementing well-known standards on platforms with clear API specifications. This suggests that the effectiveness of AI in programming is situational and context-dependent.
– **Limited Impact on Core Code Changes**: There is a noted limitation in AI’s assistance when it comes to making changes to the Workers Runtime itself, indicating that familiarity with the specific codebase is essential for efficiency, which AI may not always enhance.
– **Benefits for Codebase Navigation**: The author finds AI particularly useful when engaging with unfamiliar or complex codebases. This functionality enables programmers to effectively explore and understand code quickly, which traditionally required team collaboration for knowledge transfer.
– **Increased Confidence**: The experience has led to a greater sense of confidence in tackling new codebases independently, which could lead to increased productivity and reduced reliance on team members.
Overall, the text provides insight into AI’s practical applications in software security, development speed, and the management of complexity in code environments, making it a valuable read for professionals involved in AI, cloud computing, and software security.