Embedded AI Podcast
A podcast about using AI in embedded systems, either as part of a product or during development.
Episodes
E17: Switching Providers - Insulating Yourself from AI Vendor Lock-in
Ryan and Luca tackle a challenge many AI users are facing: what happens when your AI provider starts acting up? Drawing from recent experiences with Anthropic's capacity issues, secret billing practices, and model degradation, we explore practical strategies for avoiding vendor lock-in.We discuss the three layers of complexity: the model itself, the harness (like Claude Code or GitHub Copilot), an
E16 Running LLMs Locally: Privacy, Performance, and Practical Trade-offs
We explore what it really means to run AI models locally instead of relying on cloud providers like OpenAI or Anthropic. From powerful desktop setups with dual NVIDIA RTX 3090s to tiny models running on embedded systems, we cover the full spectrum of local AI deployment.Luca shares hands-on experience running local models for client work, explaining the hardware requirements (spoiler: you need fas
E15 Julia Imlauer on introducing AI into organizations
We talk with Julia Imlauer, Director for AI Strategy and Development, about the practical realities of introducing Gen AI tools into engineering organizations. Julia shares her experience training engineers and management across multiple companies, revealing what actually works—and what doesn't—when rolling out LLM-based coding assistants.The conversation covers the adoption curve from early enthu
E14 Kwabena Ageyman on OpenMV
We sit down with Kwabena Ageyman, co-founder of OpenMV, to explore how microcontrollers have evolved from simple 8-bit chips to AI-capable systems that rival desktop computers. Kwabena walks us through OpenMV's journey from the CMU Cam days to their latest products—the OpenMV Cam AE3 and N6—which pack neural network accelerators, image signal processors, and H.264 encoders into single-chip package
E13 Ryan visits Luca (and they talk abut spec-driven development)
For E13 we recorded live in Luca's garden in Munich, with Ryan dropping by ahead of Embedded World week. Ryan and Luca talk about spec-driven development in the AI era: where the discipline came from, what changes when an LLM is doing the typing, and the failure modes that show up over and over again in trainings. The short version: vibe coding will get you something that demos beautifully, but th
E12 Learning AI-powered development with Jacob Beningo
We sit down with Jacob Beningo, a real-time embedded systems consultant with 20 years of experience, to talk about what we've learned teaching engineers to use AI in their development workflows. Turns out, the hard part isn't getting AI to write code—it's all the systems engineering that comes before and after. We discuss common mistakes people make when starting out, like treating AI as a magic c
E11 debugging embedded systems using AI
Ryan and Luca explore practical techniques for using AI to debug embedded systems -- from analyzing breadboard photos to parsing UART output and managing complex debugging workflows. LLMs work best as force multipliers rather than replacements for engineering expertise: they handle tedious tasks like adding printf statements, analyzing logs, and decoding resistor color codes, while the engineer gu
E10 TDD and AI
Is test-driven development still relevant when AI can generate thousands of lines of code from a prompt? Ryan argues TDD was designed for human limitations -- if AI can generate complete systems, why write tests first? Luca pushes back: tests are your only defense against AI assumptions. Five lines of prompt becoming 10,000 lines of code means 9,995 lines of hidden assumptions that need to be made
E09 AI Systems Engineering with Darwin Sanoy
In this episode, Ryan and Luca sit down with Darwin Sanoy from GitLab to explore the intersection of systems engineering, embedded development, and AI. Darwin brings a wealth of experience from his work in ISO 26262 certification and MIT Systems Architecture, helping us understand why embedded systems development differs fundamentally from pure software development.We dig into the core challenge:
E08 - AI-Powered Pipelines with Joe Schneider
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 perspectiv
E07 - Embedd, and using AI safely, with Michael Lazarenko
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
E06 integrating AI into embedded products with Souvik Pal
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
E05 Context Management
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
E04 Crossover with the Agile Embedded Podcast
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
E03 Agentic Workflow Shootout: How We Actually Code With AI (Autumn 2025 Edition)
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 sh
E02 RAG for Embedded Systems Development: When Retrieval Augmented Generation Makes Sense (and When It Doesn't)
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 datasheet
E01 what is Embedded AI all about?
Welcome to the inaugural episode of the Embedded AI Podcast! Ryan and Luca kick things off by exploring the fascinating intersection of AI and embedded systems. From fog computing (AI happening everywhere around us - in coffee shops, traffic lights, and cars) to vibe coding (using AI to generate code through natural language), we dive into what this new landscape looks like for embedded engineers.
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