Super Data Science: ML & AI Podcast with Jon Krohn

In Episode #1025, Dr. Luis Serrano (Founder of Serrano Academy) joins Jon Krohn to explain the paper he co-authored on the curved spacetime of transformer architectures, in which attention stops being a lookup table and becomes something closer to gravity: words bend the space around them, and the embedding of "bank" visibly curves toward "river" as it travels through the layers of the network. In this episode, he recreates Eddington’s 1919 eclipse experiment inside a transformer, draws the line between an LLM workflow and an actual agent, explains why agent evaluation is a step harder than evaluating an essay, and gives the cleanest account of GRPO you will hear.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1025⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:

(00:10:53) What changed, and what survived, between the two editions of Grokking Machine Learning

(00:27:48) Word gravity: how attention pulls "bank" toward "river"

(00:42:12) Why RAG is an LLM workflow rather than an agent

(00:51:23) The two-by-two that explains why GRPO powers reasoning models

In ICYMI Episode #1024, Jon Krohn tracks the gap between AI investment and AI return, from the technology side to the people side. Hear from Pete Johnson, Jerry Yurchisin, Priyanka Vergadia and Tristan Handy, discussing why four out of five organizations have the structures for AI success in place while only one in five sees the returns, which decisions should never be handed to a language model however confident it sounds, how to structure Claude skills so that your output stops being slop and why the semantic layer matters more, not less, now that analytics agents are the ones asking the questions.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1024⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:

(00:56) Vector Search, Agentic Memory and Effective RAG

(09:20) Mathematical Optimization in the Agentic AI Era

(17:30) Anyone Can Write Code Now, So What Gets You Hired?

(27:14) How dbt Won Analytics Engineering

In Episode #1023, Aishwarya Srinivasan (Co-Founder of The Gen Academy) joins Jon Krohn to work out where a competitive moat comes from once anything you can build in ten minutes, somebody else can build in ten minutes too. Ash came to teaching through Illuminate AI, the mentorship community she started in 2020, and now trains senior engineers and leaders to ship agentic AI in production; she is blunt that vibe coding lowers the floor without touching the engineering judgment that production demands. In this episode, she explains what a whole-system eval covers that a model eval misses, traces reinforcement learning from the algorithm she patented at IBM to its resurgence in agentic fine tuning and lays out the MIND framework from her TED Talk for living with AI.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1023⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:

(00:10:10) Why cheap code shifts the software engineering job rather than ending it

(00:15:50) What a whole-system eval covers that a model eval misses

(00:36:11) Why reinforcement learning came roaring back for agentic AI

(00:41:23) The one skill Ash says matters more than any hard skill

In Episode #1022, Jon Krohn tackles the art of steering AI agents, deciding where your instructions should live so they get followed reliably without bloating every request. A sequel to Episode #1020 (where model size and effort set an agent’s horsepower), this one is about direction: the seven ways to deliver instructions, why a hook beats a prompt, the industry-wide agents.md standard, and three practical takeaways you can apply whatever your stack.

Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1022⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:

(02:52) The seven ways to deliver instructions to an agent

(06:42) Why a hook is a guarantee and an instruction is only a probability

(13:00) Three takeaways for organizing your instructions

In Episode #1021, Tristan Handy (Founder and CEO of dbt Labs) joins Jon Krohn to explain how a study of about a hundred companies in 2016 became analytics engineering, and then became a tool that over a hundred thousand data teams rely on. Tristan coined the term, chose SQL when Spark was the fashionable answer, and spent a decade turning down acquisition offers because none of them were good for the people using dbt. He is now merging dbt Labs with Fivetran and taking on the presidency of the combined company, the first deal he says cleared that bar. In this episode, Tristan walks through what dbt does to your raw data, argues that the semantic layer matters more once analytics agents are asking the questions, explains the type safety behind the Fusion engine, and details how a 12-kilobyte skill file collapses a million-dollar migration into six weeks.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1021⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:

(00:07:22) Why Tristan chose SQL over Spark, and what progressive complexity means

(00:10:44) How a dbt project turns raw data into modeled tables

(00:17:50) Why a decade of acquisition offers kept failing his one test

(00:40:47) How 12-kilobyte skill files cut year-long migrations to six weeks