#007: AI, Open Source, and the Future of Embedded Development: How Much Code Will We Actually Write?

Coredump Sessions · 29 Apr 2025 · 55m · Episode 7

What this episode covers

François Baldassari, CEO of Memfault, and Thomas Sarlandie, Field CTO of Memfault, host a panel with Jonathan Beri, CEO of Golioth, and Dan Mangum, CTO of Golioth, to discuss the intersection of AI, open source, and embedded development. The group analyzes the maturity of edge AI for audio and video processing, the challenges of integrating LLMs into hardware due to rapid model iteration, and the current state of using AI for firmware development. They conclude that while AI is transforming code generation and review, open source remains critical for training models and providing trusted, complex infrastructure like Zephyr and FreeRTOS.

Other shows on the same ground, closest first.

  • The Pragmatic Embedded Podcast · 28m

    AI-enhanced Embedded Development (May 2025 Edition)

    Luca, an embedded software expert, discusses his experience at the Embedded Online Conference and a German conference, highlighting an LLM-only hackathon where participants were forbidden from writing code manually. He explains that effective AI-assisted development requires rigorous context management, treating prompts like version-controlled code, and managing external context files to prevent hallucinations. The conversation covers the economic benefits of AI (approx. $0.01 per line of code), the tightening of feedback loops, and the evolving role of developers from coders to AI supervisors and technical product owners.

  • The Pragmatic Embedded Podcast · 45m

    AI-augmented software development

    Host Jeff Gable interviews Luca Johnny about integrating AI tools into embedded software development workflows. They compare IDE-integrated tools like GitHub Copilot and Cursor with command-line agents like Aider, emphasizing that workflow integration matters more than the underlying LLM. The discussion covers specific use cases for testing, debugging, and learning new frameworks, while warning that AI cannot replace the architectural judgment of experienced engineers.

  • The Pragmatic Embedded Podcast · 55m

    Crossover with Embedded AI Podcast

    Jeff Gable, Luca, and Ryan Torvik discuss integrating Large Language Models (LLMs) into embedded systems engineering workflows, emphasizing that AI must augment rather than replace rigorous engineering discipline. They identify 'vibe coding' as a critical anti-pattern where developers lose control of architectural decisions by letting AI generate too much code at once, leading to unmanageable outputs and hallucinations. The hosts advocate for strict context management, test-driven development (TDD), and small, incremental commits to maintain deterministic quality, particularly in safety-critical domains like medical devices and automotive systems. They conclude that while LLMs accelerate prototyping and boilerplate generation, human code review and ownership remain non-negotiable for reliable production software.

  • OnTrack · 56m

    Embedded Systems Engineer Discusses AI-Driven Testing Revolution

    Ari Mahpour, an Embedded Systems Engineer at Rivian with prior experience at NASA JPL and Virgin Hyperloop, discusses the integration of AI and DevOps into embedded hardware development. He demonstrates 'vibe coding' with an Arduino using voice commands and LLMs, explaining how AI agents can autonomously write, compile, and test firmware. Mahpour also covers the challenges of managing hardware revisions, the importance of Model Context Protocol (MCP) for tool guardrails, and the future of AI in automated PCB design and testing.

  • The Amp Hour · 1h 09m

    AI Tooling with Matt Liberty and Luke Beno

    Matt Liberty of Joulescope and Luke Beno of Tundra Labs and Werewolf.us discuss building custom AI-driven ERP systems to manage small-scale hardware businesses. They detail how AI tools like Claude Code and OpenClaw automate firmware debugging, protocol analysis for Juki pick-and-place machines, and inventory tracking via DigiKey and Mouser APIs. The guests emphasize that while AI accelerates software and operational workflows, it currently struggles with hardware design, arguing that engineers must retain fundamental physical intuition to effectively guide and debug AI-generated code and designs.

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