@motherduckText classification in MotherDuck just got ~50x faster at ~1% of the cost. prompt_jev() is a SQL function powered by Jev, TypeSafe's new system one model. 100k rows: 40s, $0.50, frontier-LLM accuracy. The LLM took 32 min and $37. Read on: https://motherduck.com/blog/motherduck-supports-jev/
@sh_reyaAI-powered operators in SQL are having their day in the sun@motherduckText classification in MotherDuck just got ~50x faster at ~1% of the cost. prompt_jev() is a SQL function powered by Jev, TypeSafe's new system one model. 100k rows: 40s, $0.50, frontier-LLM accuracy. The LLM took 32 min and $37. Read on: https://motherduck.com/blog/motherduck-supports-jev/@sh_reyaAI-powered operators in SQL are having their day in the sun@motherduckText classification in MotherDuck just got ~50x faster at ~1% of the cost. prompt_jev() is a SQL function powered by Jev, TypeSafe's new system one model. 100k rows: 40s, $0.50, frontier-LLM accuracy. The LLM took 32 min and $37. Read on: https://motherduck.com/blog/motherduck-supports-jev/@sh_reyaAI-powered operators in SQL are having their day in the sun@motherduckText classification in MotherDuck just got ~50x faster at ~1% of the cost. prompt_jev() is a SQL function powered by Jev, TypeSafe's new system one model. 100k rows: 40s, $0.50, frontier-LLM accuracy. The LLM took 32 min and $37. Read on: https://motherduck.com/blog/motherduck-supports-jev/@sh_reyaAI-powered operators in SQL are having their day in the sun—
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