Author compares LLM coding assistants to autotune

The author argues that using LLMs as coding assistants without preserving one's own unprocessed voice risks falling into a trap similar to autotune, where a lossy codec smooths out thinking and removes the friction of self-correction. By comparing LLM context to a codec and prompting to a decompression key, the piece explores how persistent agent memory systems and agent harnesses attempt to preserve developer individuation against model sycophancy. Open article →