Embedded AI Podcast

Episode 8: AI-Powered Pipelines with Joe Schneider

Fri, 06 Feb 2026 00:00:00 +0000

In this episode, we sit down with Joe Schneider, founder of Dojo5 and creator of the EmbedOps framework, to explore how AI is transforming embedded development pipelines. We discuss the practical applications of AI in CI/CD workflows—from summarizing build outputs and triaging static analysis results to enabling smarter hardware-in-the-loop testing through visual analysis.

Joe shares his perspective on where AI adds real value: condensing complex data, identifying anomalies, and helping teams move faster without sacrificing quality. We also tackle the challenges: the brittleness of traditional testing approaches, the difficulty of tracking dependencies in embedded systems, and the risks of over-automation. Throughout the conversation, we explore the balance between deterministic tools and AI-assisted workflows, and why human judgment remains essential—especially when it comes to security updates and edge cases that no test script would catch.

Episode 7: Embedd, and using AI safely, with Michael Lazarenko

Fri, 09 Jan 2026 00:00:00 +0000

In this episode, Ryan and Luca sit down with Michael Lazarenko, co-founder of Embedd, to discuss the real-world challenges of using AI in embedded systems development.

Michael shares his journey from manufacturing physical devices to building AI-powered tools that parse datasheets and generate hardware abstraction layers. The conversation dives deep into when AI should—and critically, shouldn’t—be used in embedded development.

Michael offers a refreshingly pragmatic perspective on AI adoption, explaining how Embedd uses AI to extract information from messy, unstandardized PDFs and technical manuals, while deliberately avoiding AI where deterministic approaches work better. The discussion covers the technical challenges of building RAG systems for embedded documentation, the importance of creating stable intermediate representations, and why accuracy matters more than speed when generating safety-critical code.

Episode 6: integrating AI into embedded products with Souvik Pal

Fri, 19 Dec 2025 00:00:00 +0000

In this episode, Ryan and Luca welcome their first proper guest, Souvik Pal, Chief Product Officer at FyeLabs. Souvik shares his eight years of experience helping customers bring embedded AI projects to life, walking us through two fascinating case studies that highlight the real challenges of deploying AI in resource-constrained environments.

We explore a wearable safety device that needed to run computer vision on an ESP32 (spoiler: it didn’t work), and a smart door system that had to juggle facial recognition, voice authentication, gesture detection, and 4K video streaming—all while fitting behind a door frame. Souvik breaks down the practical considerations that drive hardware selection, from power budgets and thermal management to the eternal struggle with Bluetooth connectivity. The conversation reveals how different constraints—whether it’s battery life, space, or compute power—fundamentally shape what’s possible with embedded AI.

Episode 5: Context Management

Fri, 05 Dec 2025 00:00:00 +0000

In this episode, Ryan and Luca explore one of the most practical aspects of working with LLMs: context management. They discuss what tokens are, how context windows work, and why managing context often matters more than crafting perfect prompts. The conversation covers the challenges of context window limitations, the phenomenon of “recency bias” where LLMs pay more attention to information at the beginning and end of their context, and practical strategies for keeping your AI assistant focused on the right things.

Episode 4: Crossover with the Agile Embedded Podcast

Fri, 21 Nov 2025 00:00:00 +0000

In this special crossover episode, Luca brings together his two podcasting worlds: the Agile Embedded Podcast with Jeff Gable and the Embedded AI Podcast with Ryan Torvik. What starts as Jeff admitting he’s still a “noob” with LLMs turns into a practical deep-dive on how to actually use AI tools without coding yourself off a cliff.The three explore the real challenges of working with LLMs: managing context windows that behave more like human memory than computer memory, the critical importance of test-driven development (even more so with AI), and why you absolutely cannot let go of the reins.

Episode 3: Agentic Workflow Shootout: How We Actually Code With AI (Autumn 2025 Edition)

Thu, 13 Nov 2025 00:00:00 +0000

In this episode, Ryan and Luca dive into their real-world AI coding workflows, sharing the tricks, tools, and hard-learned lessons from their daily development work. They compare their approaches to using AI agents like Claude Code and discuss everything from prompt management to context hygiene.

Luca reveals his meticulous TDD approach with multiple AI instances running in parallel, while Ryan shares his more streamlined VS Code-based workflow.The conversation covers practical topics like managing AI forgetfulness, avoiding the pitfalls of over-mocking in tests, and the importance of being strict with AI-generated code. They also explore the addictive, game-like nature of AI-assisted coding and why it feels like playing Civilization - always “just one more turn” until the sun comes up. This is an honest look at what actually works (and what doesn’t) when coding with AI assistants.

Episode 2: RAG for Embedded Systems Development: When Retrieval Augmented Generation Makes Sense (and When It Doesn't)

Fri, 07 Nov 2025 00:00:00 +0000

Ryan and Luca explore Retrieval Augmented Generation (RAG) and its practical applications in embedded development. After Ryan’s recent discussions at the Embedded Systems Summit, we dive into what RAG actually is: a system that chunks documents, stores them in vector databases, and allows AI to query specific information without hallucinating. While it sounds perfect for handling massive datasheets and documentation, the reality is more complex.

We discuss the critical challenge of chunking - breaking documents into the right-sized pieces for effective retrieval. Too big and searches become useless; too small and you lose context. Luca shares his hands-on experience trying to make RAG work with datasheets, revealing the gap between theory and practice. With modern LLMs offering larger context windows and better document parsing capabilities, we question whether RAG has missed its window of usefulness for most development tasks. The conversation covers when RAG still makes sense (legal contexts, parts catalogs, private LLMs) and explores alternatives like having LLMs use grep and other Unix tools to search documents directly.

Episode 1: From Fog Computing to Vibe Coding

Fri, 10 Oct 2025 00:00:00 +0000

Episode Description

Welcome to the Embedded AI Podcast! In our inaugural episode, Ryan and Luca introduce the podcast and dive into what it means to use AI in embedded systems.

We discuss:

AI on embedded devices: Traditional machine learning, edge computing, and predictive maintenance (including a cool example of acoustic monitoring)
AI for developers: Using LLMs and AI tools in embedded development workflows
Real-world applications: From aerospace conferences to rocket launch coordination
Practical challenges: Getting LLMs to write STM32 code, the importance of TDD, and staying in control of AI-generated code

This is a conversation about learning together - expect frank discussions about what works, what doesn’t, and plenty of mistakes to learn from.

About

Wed, 01 Jan 2025 00:00:00 +0000

About the Podcast

Welcome to the Embedded AI Podcast - exploring AI in embedded systems with a pragmatic, no-hype approach.

We cover AI in embedded systems from two angles:

AI on embedded devices - traditional ML, edge computing, predictive maintenance, IoT applications
AI for embedded developers - using LLMs and AI tools effectively in your development workflow

This podcast is about learning together. We’ll share what we’ve learned (often the hard way), bring on guests who’ve figured things out, and give you a clear-eyed view of what’s actually happening in this space.

About the Podcast

Wed, 01 Jan 2025 00:00:00 +0000

About the Podcast

Welcome to the Embedded AI Podcast - exploring AI in embedded systems with a pragmatic, no-hype approach.

We cover AI in embedded systems from two angles:

AI on embedded devices - traditional ML, edge computing, predictive maintenance, IoT applications
AI for embedded developers - using LLMs and AI tools effectively in your development workflow

Meet Luca

Wed, 01 Jan 2025 00:00:00 +0000

Luca Ingianni

I’m an aerospace engineer by training, but the embedded systems world won me over. I’ve spent the major part of my career working with embedded systems across aerospace, automotive, medical, and industrial sectors.

My eye twitches when people say “vibe coding,” but I acknowledge AI is here to stay, and you would be a fool to ignore or reject it. The question isn’t whether to use these tools, but how to use them effectively and pragmatically.

Meet Ryan

Wed, 01 Jan 2025 00:00:00 +0000

Ryan Torvik

I’m a software engineer who fell into the world of cybersecurity and embedded systems - and stayed there for over two decades.

I’ve spent my career at the intersection of software, hardware, and security. From military communications systems to vulnerability research, I’ve seen what happens when code meets the real world. Sometimes it’s elegant. Often it’s messy. Always it’s interesting.

From Defense Contractor to Founder

I spent nearly 20 years in the defense and aerospace industry - first at Harris Corporation (now L3Harris) as a software engineer, then at Raytheon Intelligence & Space where I progressed from cyber engineer to engineering manager and principal engineer.

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Wed, 01 Jan 2025 00:00:00 +0000

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