Chong Shen from Flower Labs joins us to discuss what it really takes to build production-ready federated learning systems that work across data silos. We talk about the Flower framework and it's architecture (supernodes, superlinks, etc.), and what makes it both "friendly" and ready for real enterprise environments. We also  explore how the generative Generative AI boom is reshaping Flower’s roadmap.

Featuring:

• Chong Shen Ng – LinkedIn
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Episode links:

• The future of AI training is federated
• DeepLearning.ai short course on Federated Learning with Flower
• Flower Monthly
• Federated Learning in Automotive
• Federated AI in Finance
• Federated Learning in Healthcare
• Federated AI on IoT Systems
• FlowerTune LLM Leaderboard
• Flower Intelligence
• GitHub
• Slack
• Flower Discuss

Check out upcoming webinars!

Sponsors:

• NordLayer is toggle-ready network security built for modern businesses—combining VPN, access control, and threat protection in one platform that deploys in under 10 minutes with no hardware required. It's built on Zero Trust architecture with granular access controls, so only the right people access the right resources, and it scales effortlessly as your team grows. Get up to 32% off yearly plans with code practically-10 at nordlayer.com/practicalai - 14-day money-back guarantee included.

In this first of a two part series of episodes on federated learning, we dive into the evolving world of federated learning and distributed AI frameworks with Patrick Foley from Intel. We explore how frameworks like OpenFL and Flower are enabling secure, collaborative model training across silos, especially in sensitive fields like healthcare. The conversation touches on real-world use cases, the challenges of distributed ML/AI experiments, and why privacy-preserving techniques may become essential for deploying AI to production.

Featuring:

• Patrick Foley – LinkedIn
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Links:

• Intel
• OpenFL

Sponsors:

• NordLayer is a toggle-ready network security platform built for modern businesses. It combines VPN, access control, and threat protection in one easy-to-use platform. No hardware. No complex setup. Just secure connection and full control—in less than 10 minutes. Up to 22% off NordLayer yearly plans plus 10% on top with the coupon code practically-10.

Loïc Houssier, Head of Engineering at Superhuman, joins us to discuss how AI and LLMs are reshaping the email experience. He highlights challenges related to the variability of user prompts and infrastructure optimization. Loïc emphasizes that a deep focus on user experience and real human workflows is key to building AI tools people actually love to use.

Featuring:

• Loïc Houssier – LinkedIn
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Links:

• Superhuman
• Referral Code for a Free month

Sponsors:

• Outshift by Cisco – AGNTCY is an open source collective building the Internet of Agents. It's a collaboration layer where AI agents can communicate, discover each other, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for inter-agent communication, and modular components to compose and scale multi-agent workflows. 

In this episode, Daniel and Chris unpack the Model Context Protocol (MCP), a rising standard for enabling agentic AI interactions with external systems, APIs, and data sources. They explore how MCP supports interoperability, community contributions, and a rapidly developing ecosystem of AI integrations. The conversation also highlights some real-world tooling such as FastAPI-MCP.

Featuring:

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

Links:

• Protocol website
• Anthropic blog post
• Blog post - Model Context Protocol (MCP) an overview
• FastAPI-MCP
• How to Use FastAPI MCP Server: A Complete Guide 
• Candle (Rust framework)

In this episode, we explore the intersection of AI, machine learning, and healthcare through the lens of neuroimaging and epilepsy diagnosis. Dr. Gavin Winston shares insights from his work using MRI data and machine learning to uncover subtle abnormalities in brain function. We discuss the cultural and ethical barriers to AI adoption in medicine, how predictive data analysis could transform the diagnostic workflow, and what the future holds for medical imaging in a world increasingly shaped by intelligent systems.

Featuring:

• Gavin Winston – LinkedIn, Website
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Links:

• Detection of Epileptogenic Focal Cortical Dysplasia Using Graph Neural Networks: A MELD Study
• Machine Learning in Neuroimaging across Disciplines
• Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS
• Literature review and protocol for a prospective multicentre cohort study on multimodal prediction of seizure recurrence after unprovoked first seizure
• Deep learning in neuroimaging of epilepsy
• Non-parametric combination of multimodal MRI for lesion detection in focal epilepsy
• Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data

Vibe coding, agentic workflows, and AI-assisted pull requests? In this episode, Daniel and Chris chat with Robert Brennan and Graham Neubig of All Hands AI about how AI is transforming software development—from senior engineer productivity to open source agents that address GitHub issues. They dive into trust, tooling, collaboration, and what it means to build software in the era of AI agents. Whether you're coding from your laptop or your phone on a morning walk, the future is hands-free (and All Hands).

Featuring:

• Robert Brennan – LinkedIn, X
• Graham Neubig – LinkedIn, X
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Links:

• All Hands
• All Hands on GitHub
• All Hands on Hugging Face

In this episode, Daniel sits down with Pavel Veller, EPAM’s Chief Technologist, to explore the practical challenges of orchestrating many AI agents and managing connections to disparate systems/tools. Pavel shares insights from his hands-on work with agentic architectures and internal tools like "DIAL". Pavel also helps us understand things like MCP servers and why connecting assistants via APIs is easy—but making them useful is hard. 

Featuring:

• Pavel Veller – LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Links:

• EPAM
• DIAL
• SWE-bench results

How do you enable AI acceleration (at both the hardware and software layers) that stays ahead of rapid industry shifts? In this episode, Dhananjay Singh from Groq dives into the evolving landscape of AI inference and acceleration. We explore how Groq optimizes the serving layer, adapts to industry shifts, and supports emerging model architectures. 

Featuring:

• Dhananjay Singh – LinkedIn, X
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Links:

• Groq

Sponsors:

• Augment Code - Developer AI that uses deep understanding of your large codebase and how you build software to deliver personalized code suggestions and insights. Augment provides relevant, contextualized code right in your IDE or Slack. It transforms scattered knowledge into code or answers, eliminating time spent searching docs or interrupting teammates.

Kyle Daigle, COO of GitHub, joins the hosts to discuss the evolving role of AI in software development, GitHub Copilot’s impact, and the challenges of AI-assisted coding. The conversation covers licensing concerns, ethical considerations, and how developers can navigate these complexities. Kyle also shares his vision for ambient AI, which seamlessly integrates into workflows to enhance productivity and innovation, shaping the future of developer tools. 

Featuring:

• Kyle Daigle – LinkedIn
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Sponsors:

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

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:

• Kate Soule – LinkedIn
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Links:

• IBM Granite
• IBM Granite on Hugging Face
• IBM Expands Granite Model Family with New Multi-Modal and Reasoning AI Built for the Enterprise