43:57
2026-10-08
Mehdi Ouazza uses MotherDuck's prompt_jev() to turn text columns into columns you can GROUP BY. He classifies 100,000 consumer complaints, then walks through five use cases on real data: AI agent logs, Hacker News sentiment, sales calls, and two kinds of job-ad analysis.
Stream
MotherDuck Features
AI ML and LLMs
51:58
2026-10-01
Jacob Matson walks through a working data chat agent he built: MCP tools, an agentic loop, a tuned system prompt, hard-coded read-only guardrails, and telemetry. He demos it live against a real dataset, walks through the actual codebase, then shows the same backend re-platformed into a Slack bot.
Stream
MotherDuck Features
AI ML and LLMs
Data Pipelines
49:02
2026-09-29
MotherDuck's new prompt_jev() function runs text classification directly from SQL using Jev, a decision-focused model from typesafe.ai. In a live demo, Jacob Matson, Dumky de Wilde, and Hamilton Ulmer showed it filtering, disambiguating, and re-ranking real data without leaving SQL. The number they kept coming back to: 100,000 rows classified at 89% accuracy in 40 seconds, compared to 32 minutes and $37.58 for a comparable LLM call.
Stream
MotherDuck Features
AI ML and LLMs
Data Pipelines
1:00:10
2026-02-25
You know the pattern: someone asks a question, you write a query, share the results — and a week later, the same question comes back. Watch this webinar to see how MotherDuck is rethinking how questions become answers, with AI agents that build and share interactive data visualizations straight from live queries.
Webinar
AI ML and LLMs
MotherDuck Features