7 hours ago
- The author, an experienced programmer, uses LLMs like ChatGPT to accelerate coding by reducing time spent on documentation, learning complex APIs, and creating disposable programs.
- LLMs are often described as a 'stupid but all-knowing savant'; they excel at providing knowledge but struggle with complex reasoning, making them most useful for high-level tasks in Python rather than system programming in C.
- Examples include using GPT4 to write Python scripts for data analysis, convert between machine learning frameworks (e.g., Keras to PyTorch), and create quick utilities like CSV report analyzers.
- LLMs fail at tasks requiring deep reasoning, such as implementing a Bloom filter correctly or reverse-encoding quantization formats, often producing flawed code that requires expert intervention.
- The author argues that LLMs are valuable for modern programming which often involves repetitive code (e.g., API boilerplate) rather than novel algorithms, but warns that their current limitations mean they cannot assist in system programming effectively.
- The article concludes that using LLMs is a practical skill that improves problem description abilities and saves time, but their benefits are skewed toward those already skilled in programming.