AI, quant and large-file work
For people inspecting training corpora, market histories, research exports and multi-gigabyte files—not just a small table that already fits comfortably in a spreadsheet.
AI training & data engineering
Pretraining shards, nested conversations and preference pairs. Inspect JSONL, Parquet and Arrow without turning the entire corpus into a desktop table.
AI training data workflowsQuantitative research
Large market-data exports, trade histories and parameter sweeps. Narrow symbols and time ranges, read exact values and trace findings to source records.
Market data & backtest workflowsLarge-file analysis
GB-scale CSV, JSONL and columnar files. Start with a useful page, query supported sources and save reviewed edits as a separate copy.
Large-file viewing & editingCheck which shard contains a malformed conversation, whether a field changed type, or what a preference pair actually contains. Keep the split, source file and original nested record with the finding.
Inspect SFT and preference dataInspect the full result population, confirm how costs are represented and follow a candidate into chronological evidence. Use source-level queries for the full file, not search within one loaded page.
Investigate a large result exportDiscover a shard folder, narrow supported partitions and project the fields that matter. Treat continuation cursors as part of a bounded result and plan scratch space for compressed sources.
Browse a pretraining corpusInspect complete values, stage supported edits, review changes and save a separate copy. Reopen the output and verify values and types before handing it to the next pipeline stage.
View and edit a large CSV