EXPLAIN ANALYZE

AI and data news, digested by veterans from the field. Less hype. More signal. Get all show notes here . Podcast by MotherDuck

EXPLAIN ANALYZE is a technology podcast hosted by MotherDuck, with 8 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

MotherDuck hosts EXPLAIN ANALYZE, a technology show with 8 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #8

  AI codes with outdated libraries. Why is this unsolved?

  Aug 7, 202650 minS1

  ​ @mehdio and Dumky work through a round of data and AI news, starting with Analytics Tycoon, a browser game where you run a data team on a fixed budget and lose trust points every time a cron job breaks. Then the uncomfortable stuff: Python 3.15 is at release candidate while 3.10 is still the number two download and goes end of life in two months, your agent keeps writing 3.11 because it does not know what day it is, Stripe is reportedly circling OpenRouter for 10 billion dollars, and data modeling turns out to be very much alive once you try to replace it with a context layer. Subscribe + all links: motherduck.com/podcast Hosts: Mehdi Ouazza (@mehd-io) and Dumky de Wilde (@dumkydewilde) CHAPTERS 00:00 Cold open 00:33 Analytics Tycoon: the data team as a strategy game 06:46 Python 3.15 is coming and 3.10 is about to die 14:07 If nobody blogs about it, will your model know? 15:20 Agent Craft and Agents View: maps for your agents 19:18 Stripe in talks to acquire OpenRouter 23:20 Google's next chapter and the shrinking AI moat 30:19 Is data modeling dead? Daniel Beach weighs in 35:10 Hard to eval is a product smell 39:57 Cloudflare OS and the return of internal tools 43:02 Supabase publishes public evals for AI agents 44:21 Superlogical: Hashimoto builds a multiplexer 48:47 That is a wrap #ExplainAnalyze #MotherDuck #DataEngineering #DuckDB #Python #AIAgents #LLMEvals #DataModeling #OpenRouter #CloudflareOS #Tmux #DataPodcast

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  Episode #7

  The Safest Bet in AI Is Hardware !

  Jul 28, 202651 minS1

  Is the safest bet in AI the hardware underneath it? Show notes: https://motherduck.com/explain-analyze Mehdi Ouazza and Jacob Matson unpack Meta's reported $10B compute lease talks with Anthropic, why AI labs are becoming infrastructure customers, and what it means for data and AI builders. Then they get into graph-shaped context for agents, removing AI slop from writing, reproducible development environments, AMD's Anthropic bet, Domo's acquisition and the future of standalone BI, lakehouse vs. warehouse, leadership bottlenecks, tldraw's isRecord saga, and loop engineering. EXPLAIN ANALYZE is the MotherDuck podcast where the hosts bring data and AI stories the others have not seen, then work through them together. 00:00 Welcome to EXPLAIN ANALYZE 01:02 Meta's potential $10B compute lease with Anthropic 04:13 Graph context for agents 10:23 Removing AI slop from writing 18:33 Why “it worked on my machine” still happens 25:45 AMD commits up to $5B to Anthropic 27:26 Domo acquired: the end of standalone BI? 31:29 Where data engineering is heading 33:15 Lakehouse vs. warehouse 37:30 Leadership becomes the bottleneck 41:10 tldraw and the isRecord saga 46:07 Loop engineering: goals, stop criteria, and timers 50:03 Thanks for watching

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  Episode #6

  AI is relearning everything databases already knew ft. Stephanie Wang

  Jul 1, 202648 minS1

  AI is moving inside the database, and it keeps hitting problems databases already solved. Stephanie Wang (staff engineer at MongoDB, founding engineer at MotherDuck) joins Mehdi and Jacob Matson to dig into Snowflake's Cortex AISQL and why AI inside SQL is really a query-optimization and cost problem, the compute crunch pushing RAM and inference prices up, RadAgents and Sakana's Fugu model router, open weights vs open development, and the "elephant and goldfish" method for designing before you let AI write the code. All links and full show notes: motherduck.com/explain-analyze 00:00 Intro and guest intro (Stephanie Wang) 01:35 Cortex AISQL: AI operations native to SQL 10:14 SpaceX rents out idle compute for AI inference 12:11 Compute is the new database engine for AI 13:43 DRAM price hikes and the margin squeeze 18:40 Local AI models and sandboxing agents 22:39 RadAgents: multi-agent reasoning for radiology 27:34 Sakana Fugu and dynamic model routing 30:12 Open weights vs open development 33:43 The elephant and goldfish: design before you code 42:07 MPP: machine-to-machine payments for agents 46:56 Wrap-up

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  Episode #5

  AI is turning all data viz into JavaScript

  Jun 19, 202650 minS1

  Yan Holtz, the data viz educator behind data-to-viz.com and the R/Python/D3 graph galleries, joins Mehdi and Alex for a full episode on data visualization in the age of AI. AI has basically solved the "how do I write the code" problem, which makes the "what makes a good chart" problem matter more than ever - and it's quietly pushing all serious data viz toward JavaScript and D3, even for R and Python people. We get into picking the right chart before writing a line of code, whether dashboards are dying in favor of agents, why D3.js went from the hardest tool to the default, an agent that grades its own charts against the Tufte test, why Shiny and Streamlit may no longer be the right call, the token cost of data work, and whether a chat box is even the right tool to design a visualization. Subscribe + all links: motherduck.com/explain-analyze Yan's website : https://www.data-to-viz.com/ Yan's social : https://www.linkedin.com/in/yan-holtz-2477534a/ Chapters 00:00 Welcome + guest intro (Yan Holtz) 01:29 data-to-viz.com : choosing the right chart 07:58 BI is dead, long live BI: dashboards vs agents 17:09 ninejs: interactive ggplot2 charts in Python 20:01 Teaching an AI agent to make beautiful charts 29:20 D3.js: infinite flexibility, now AI-writable 36:24 Shiny: the right tool in the age of AI? 42:02 Token economics: data is token-hungry 46:33 Data visualization in the age of AI 49:37 Outro #DataViz #DataVisualization #D3js #JavaScript #BI #DuckDB #AI #MotherDuck #ExplainAnalyze

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  Episode #4

  Data infra is printing money. Is it sustainable?

  Jun 13, 202645 minS1

  Infrastructure keeps winning the funding while SaaS bleeds — but is it sustainable when AI agents spin up databases that die in days? Mehdi and Jacob Matson dig into Supabase's Series F, local AI on personal chips, DuckDB turning up everywhere (MariaDB, MondayDB HTAP), Anthropic's "data is not software" claim, and a wave of people building software just for themselves. Show notes & links : https://motherduck.com/explain-analyze ➡️ Follow Us LinkedIn: https://linkedin.com/company/motherduck X/Twitter : https://twitter.com/motherduck Blog: https://motherduck.com/blog/ 00:00 — Intro: who's on today 00:48 — Supabase's Series F: SaaS vs infrastructure 05:14 — Local AI: NVIDIA's chips for personal computers 09:28 — MondayDB 3: HTAP for a trillion-table system 14:10 — A DuckDB storage engine for MariaDB 18:14 — Anthropic: "data is not software" 24:12 — Delba on feedback loops and less agent babysitting 29:20 — Tristan Handy: I built a (very small) agent swarm 32:54 — Home-cooked apps: software just for you 38:01 — DoomBench: can your database run Doom? 40:41 — A C documentary: languages in the AI era 44:28 — Wrap-up

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