Honestly I couldn't f*ckin believe it actually happened Stanford data research team just leaked the secret way they process data your coding pipeline is waiting 2 seconds per row for a model to say "refund" or "bug" that's what happens when you send a frontier LLM to label a table. it reads, it thinks, it writes a paragraph, and you parse one word out of it. They stopped doing that. They use Jev. Jev doesn't write. it picks from the labels you gave it, returns a probability, and moves to the next row. someone benchmarked it on 500 examples from Stanford's SST-2 dataset against Claude Sonnet 5: -> accuracy: 95.4% vs 95.6% -> median latency: 376 ms vs 2,076 ms -> throughput: 18.2 rows/s vs 3.5 rows/s -> cost per 1,000 rows: $0.015 vs $0.80 same answer quality. ~5x faster. ~50x cheaper. and it runs where your data already lives: -> jev4pg: 41 Jev operators inside PostgreSQL. filter, extract, rank and match rows in SQL, with parallel execution and repeated values sharing one judgment github.com/Sheltercosmo/jev4… -> duckdb-jev: a native DuckDB extension that packs up to 1,000 judgments into one request and streams them straight from a query github.com/prasanthj/duckdb-… -> jevframe: a .jev accessor for pandas and Polars. ask a question about every row, get a normal column back github.com/ktaletsk/jevframe the database still does the math. Jev only takes the part where something has to be understood. the catch: it's not magic on every dataset. on 4-topic news it scored 84% vs Claude's 90%, and a fine-tuned BERT beat both. measure it on your own data first. > let SQL do the joins. > let Jev do the judgment. > let the frontier model handle what's actually hard. bookmark this before your next labeling job eats a weekend to process.