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The Dark Side of AI-Assisted Code Generation: A Case Study of Cursor

2025-05-30

This article critically assesses the effectiveness of AI-assisted code generation tools. Using a code modification suggestion showcased on the Cursor editor's homepage as a case study, the author demonstrates how AI-generated code can not only fail to improve productivity but can introduce errors and inefficiencies, such as useless length validation and questionable string sanitization. The author argues that a good AI tool should identify and avoid these issues, providing programmers with the context needed to make informed decisions rather than simply offering a potentially flawed solution. Current AI code generation tools, as exemplified, fall short of this goal, resulting in a net negative impact on productivity.

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Cache-Aware Programming in Python: A Surprisingly Significant Performance Difference

2025-04-05

This post investigates the impact of cache-aware programming on Python performance through experiments. Results show that random access to list elements in Python is consistently slower than sequential access, especially when data size exceeds CPU cache. This suggests that even in interpreted environments, cache-aware programming can improve Python program performance. Experiments also compare the performance difference between native Python lists and NumPy arrays, showing NumPy arrays have a significant performance advantage due to their more compact memory layout.