How multimodal pipelines differ from analytics

> It’s the agent writing the code. And it’s the development loop of writing the code, building testing, write the code, build test and iterating. And so I do think we’ll see for many types of software, a shift away from Python towards other programming languages. I think Go is probably the best language for those like other types of software projects. And like I said, I haven’t written a line of Go code in my life.

– Wes McKinney (creator of pandas Principal Architect at Posit),

Wes McKinney, Marcel Kornacker, and Alison Hill join Hugo to talk about the architectural shift for multimodal AI, the rise of “agent ergonomics,” and the evolving role of developers in an AI-generated future.

We Discuss:

* Agent Ergonomics: Optimize for agent iteration speed, shifting from human coding to fast test environments, potentially favoring languages like Go;

* Adversarial Code Review: Deploy diverse AI models to peer-review agent-generated code, catching subtle bugs humans miss;

* Multimodal Data Verbs: Make operations like resizing and rotating native to your database to eliminate data-plumbing bottlenecks;

* Taste as Differentiator: Value “taste”—the ability to curate and refine the best output from countless AI-generated options—over sheer execution speed;

* 100x Software Volume: Embrace ephemeral, just-in-time software; prioritize aggressive generation and adversarial testing over careful planning for quality.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript of the workshop & fireside chat here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

This was a fireside chat at the end of a livestreamed workshop we did on building multimodal AI systems with Pixeltable. Check out the full workshop below (all code here on Github):

Links and Resources

* Wes McKinney on LinkedIn

* Marcel Kornacker on LinkedIn

* Alison Hill on LinkedIn

* Spicy Takes

* Palmer Penguins

* Pixeltable

* Posit

* Positron

* Building Multimodal AI Systems Workshop Repository

* Pixeltable Docs: LLM Tool Calling with MCP Servers

* Pixeltable Docs: Working with Pydantic

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

* Join the final cohort of our Building AI Applications course in March, 2026 (25% off for listeners)

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgfs

What people said during the workshop

“I think the interface looks amazing/simple. Strong work! 🦾” — @goldentribe

“This is quite amazing. Watching this I felt the same way when I first leant pandas, NumPy and scikit and how well i was able to manipulate and wrangle data. PixelTable feels seamless and looks as good as those legendary frameworks but for Multimodal Data.” — @vinod7

“This is all extremely cool to see, I love the API and the approach.” — @steveb4191

“Thanks so much, Hugo! That was very insightful! Great work Alison and Marcel!” — @vinod7

“Just wrapped up watching a replay of the Pixeltable workshop. So cool!! Love the notebooks and working examples. The important parts were covered and worked beautifully 🕺” — @therobbrennan

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈