Source URL: https://simonwillison.net/2025/May/13/luke-kanies/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Luke Kanies
Feedly Summary: I did find one area where LLMs absolutely excel, and I’d never want to be without them:
AIs can find your syntax error 100x faster than you can.
They’ve been a useful tool in multiple areas, to my surprise. But this is the one space where they’ve been an honestly huge help: I know I’ve made a mistake somewhere and I just can’t track it down. I can spend ten minutes staring at my files and pulling my hair out, or get an answer back in thirty seconds.
There are whole categories of coding problems that look like this, and LLMs are damn good at nearly all of them. […]
— Luke Kanies, AI Is Like a Crappy Consultant
Tags: ai-assisted-programming, llms, ai, generative-ai
AI Summary and Description: Yes
**Summary:** The text highlights the advantages of using Large Language Models (LLMs) in programming, particularly in identifying syntax errors more efficiently than traditional methods. This is significant for professionals in AI development and software security as it showcases the utility of leveraging AI tools in coding, improving both productivity and accuracy.
**Detailed Description:** The provided text emphasizes the remarkable capabilities of LLMs in the context of programming, specifically pointing out their efficiency in detecting syntax errors. This showcases a practical application of AI in software development. Here are the notable points:
– **Efficiency Improvement:** LLMs can identify syntax errors significantly faster than human programmers, enhancing productivity.
– **Error Tracking:** Instead of spending extensive time debugging, programmers can receive instant feedback on their coding issues, which benefits overall workflow.
– **Broad Applicability:** While the text focuses on syntax errors, it implies that LLMs can assist in various coding problems, indicating their versatility in the programming domain.
– **Tool Utilization:** Encouraging the integration of AI tools in software development environments to support programmers in their tasks.
This insight into LLMs’ capabilities presents opportunities for software security professionals to adopt AI technologies in code review and assurance processes, potentially leading to more robust and secure software development practices. The collaboration between AI tools and developers can create a synergistic effect in programming, encouraging faster release cycles while maintaining high-quality standards in code integrity.