Cursor coding with LLMs—where AI assists you line-by-line as you type—feels intuitive but comes with subtle challenges, as Niraj points out. While the cursor follows your lead, the model doesn’t always grasp the bigger picture, often generating code that looks right but isn’t functionally sound. It can struggle with multi-file context, long-term logic, or remembering previous decisions. Niraj emphasizes that relying on cursor-based suggestions can lead to code that’s syntactically neat but semantically off. To truly benefit, developers must stay engaged, review critically, and steer the AI thoughtfully. Cursor coding is powerful, but it works best when paired with human intuition and oversight.