Build a workspace of AI agents

03/08/25 • 47 min

3 Listeners

How can every single person build a personal AI protégé and then accumulate (and share) a host of other assistants? In this episode, we dive into the world of no-code AI with Scott Meyer from Chipp.ai. We discuss AI tooling for people that can't code, the cultural shift that needs to happen for widespread AI adoption in businesses, and the predicted growth trajectory of AI assistant that you can own.

Featuring:

Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Scott Meyer - LinkedIn, X

Sponsors:

Domo – The AI and data products platform. Strengthen your entire data journey with Domo’s AI and data products.

Show Notes:

Featuring:

Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Scott Meyer - LinkedIn, X

Sponsors:

Domo – The AI and data products platform. Strengthen your entire data journey with Domo’s AI and data products.

Show Notes:

Previous Episode

GenAI hot takes and bad use cases

It seems like all we hear about are the great use cases for GenAI, but where should you NOT be using the technology? On this episode Chris and Daniel share their hot takes and bad use cases. Some may surprise you!

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Sponsors:

Domo – The AI and data products platform. Strengthen your entire data journey with Domo’s AI and data products.
Fly.io – The home of Changelog.com — Deploy your apps close to your users — global Anycast load-balancing, zero-configuration private networking, hardware isolation, and instant WireGuard VPN connections. Push-button deployments that scale to thousands of instances. Check out the speedrun to get started in minutes.

Featuring:

Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X

Show Notes:

Something missing or broken? PRs welcome!

Next Episode

Optimizing for efficiency with IBM’s Granite

We often judge AI models by leaderboard scores, but what if efficiency matters more? Kate Soule from IBM joins us to discuss how Granite AI is rethinking AI at the edge—breaking tasks into smaller, efficient components and co-designing models with hardware. She also shares why AI should prioritize efficiency frontiers over incremental benchmark gains and how seamless model routing can optimize performance.

Featuring: