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COREDUMP #007: AI, Open Source, and the Future of Embedded Development: How Much Code Will We Actually Write?
Chapters
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| Episode Teasers & Welcome
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| Meet the Panel: Memfault x Golioth
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| Why AI at the Edge Isn’t Actually New
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| The Real Use Cases for AI in Embedded Devices
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| How Much Chaos Are You Willing to Introduce?
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| Edge AI vs. Cloud AI: Where It’s Working Today
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| LLMs in Embedded: Promise vs. Reality
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| Why Hardware Can’t Keep Up with AI’s Pace
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| Building Unique Models When Public Datasets Fail
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| Open Source’s Big Moment (and What Comes Next)
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| Will AI Kill Open Source Contributions?
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| How AI Could Change Software Supply Chains
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| How to Stay Relevant as an Engineer in the AI Era
Listen to the Podcast
About the webinar
In today’s Coredump Session, we dive into a wide-ranging conversation about the intersection of AI, open source, and embedded systems with the teams from Memfault and Goliath. From the evolution of AI at the edge to the emerging role of large language models (LLMs) in firmware development, the panel explores where innovation is happening today — and where expectations still outpace reality. Listen in as they untangle the practical, the possible, and the hype shaping the future of IoT devices.
Key Takeaways:
- AI has been quietly powering embedded devices for years, especially in edge applications like voice recognition and computer vision.
- The biggest gains in IoT today often come from cloud-based AI analytics, not necessarily from AI models running directly on devices.
- LLMs are reshaping firmware development workflows but are not yet widely adopted for production-grade embedded codebases.
- Use cases like audio and video processing have seen the fastest real-world adoption of AI at the edge.
- Caution is warranted when integrating AI into safety-critical systems, where determinism is crucial.
- Cloud-to-device AI models are becoming the go-to for fleet operations, anomaly detection, and predictive maintenance.
- Many promising LLM-based consumer products struggle because hardware constraints and cloud dependence create friction.
- The future of embedded AI may lie in hybrid architectures that balance on-device intelligence with cloud support.
Speakers
CTO, Golioth
Dan Mangum
Dan is an experienced engineering leader, having built products and teams at both large companies and small startups. He has a history of leadership in open source communities, and has worked across many layers of the technical stack, giving him unique insight into the constraints faced by Golioth’s customers and the requirements of a platform that enables their success.
CEO & Founder, Golioth
Jonathan Beri
Jonathan Beri is the founder and CEO of Golioth, the instant IoT development platform built for scale. Jonathan has spent more than a decade building IoT solutions at companies like Google, Nest, Particle & WeWork. If you really want to get him going, ask him how he would build a real holodeck.
CEO & Founder, Memfault
François Baldassari
François Baldassari is the CEO and Co-Founder of Memfault. An embedded software engineer by trade, François was driven to start Memfault by his passion for tooling and automation in software engineering. Prior to Memfault, he ran the firmware team at Oculus and built the operating system at Pebble. François has a BSc in Electrical Engineering from Brown.
Field CTO, Memfault
Thomas Sarlandie
Thomas Sarlandie is the Field CTO and embedded Linux expert at Memfault with a passion for leveraging customer experiences to drive product innovation and quality. Thomas previously worked at Fitbit and Pebble as a VP and Director of Software Engineering. Thomas has an MSc in Software Engineering from EPITA, Paris.