CsvPath Framework developers noticed JSONL's quirks while updating FlightPath Data for editing. The format lacks consistent header order, allows lines to change structure, and mixes arrays with dictionaries. This forces developers to read entire files just to understand data shape, creating headaches for anyone building tools to visualize or modify these files.

Building a grid editor for JSONL demands painful compromises. Headers can become an ever-growing, sorted list, or you can hide them entirely and lose context. FlightPath Data split the difference: it offers a grid view without headers, saving edits as CSV, plus a raw JSON editor for full control but lower productivity.

Despite these headaches, JSONL excels at handling non-tabular or wildly fluctuating data. It provides clean, key-based access per line, avoiding index-based lookups and offering self-documenting structure at the row level. In sparse, wide datasets, it can even save space compared to CSV despite being more verbose overall.

For developers needing structure over JSONL's chaos, CsvPath Framework and FlightPath Data offer a middle ground. JSONL suits programmatic workflows but frustrates purists demanding validity and referenceability. If grid views and strict schemas matter more than line-level flexibility, sticking with CSV or structured databases might save headaches down the road.