Twenty years after its founding in a small Italian café, Arduino has transformed from a simple open-source project into a global technology enabler that's educated an entire generation of makers, developers, and engineers. In this fascinating conversation with Arduino co-founder David Cuartielles, we explore the evolution of this groundbreaking platform and its growing role in the edge AI revolution.

David takes us through Arduino's remarkable journey, sharing how a project named after an Italian king (and a local coffee shop) has grown to become the foundation of countless innovations worldwide. What began as an attempt to democratize basic electronics—making it less expensive and complicated "to just do something that would detect pressing a button and turning on a light"—has evolved into sophisticated platforms supporting edge AI and tiny machine learning applications.

The conversation reveals how Arduino has kept pace with technological advancement, moving from basic microcontrollers to powerful Linux computers that can run complex applications and be updated over the air. We explore the practical applications of edge AI in industrial settings, where Arduino enables cost-effective, incremental improvements that deliver significant impacts. As David explains, sometimes the simplest implementations—like smart lamps or Roomba vacuum robots—prove most effective in the real world.

Particularly inspiring is Arduino's commitment to global education, with David sharing insights from a recent DyniML workshop in Malawi that showcased the extraordinary talent and enthusiasm of participants. From manufacturing facilities in Italy and India to offices in Austin and educational initiatives across continents, Arduino continues to democratize technology access worldwide.

Whether you're a longtime Arduino user, an AI enthusiast interested in edge computing, or simply curious about how small technologies can create massive impact, this episode offers valuable insights into the past, present, and future of one of technology's most influential platforms. Subscribe now and join us as we explore how Arduino continues to empower creators and innovators across the globe.

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At the crossroads of cutting-edge technology and practical innovation stands Ceva, a semiconductor IP powerhouse with a remarkable two-decade legacy. Powering nearly 20 billion devices worldwide and shipping over 2 billion annually, SIVA has emerged as a crucial enabler in the burgeoning edge AI ecosystem.

What distinguishes Ceva in this competitive landscape is their holistic approach to edge computing. Rather than focusing solely on neural processing, they've strategically built solutions around what Chad Lucien describes as the three pillars of edge AI: connectivity, sensing, and inference. This comprehensive vision has positioned them as the industry's leading Bluetooth IP licensor while developing sophisticated DSP solutions and a scalable NPU portfolio that ranges from modest GOPS to an impressive 400 TOPS.

The secret to Ceva's effectiveness lies in their deep integration of hardware and software expertise. "The software is becoming the definition of the product," notes Lucien, explaining how their deep learning applications team directly influences hardware specifications. This software-first perspective has created solutions perfectly tailored for low-power, small form factor devices across diverse applications. From earbuds and health trackers to consumer robots and smart appliances, SIVA's fully programmable solutions handle everything from neural network computation to DSP workloads and control code.

Most exciting is SIVA's leadership in the Audio ML renaissance through their work with the EDGE AI FOUNDATION's Audio Working Group. As audio applications shift from traditional DSP implementations to neural strategies, we're witnessing transformative capabilities in speech enhancement, anomaly detection, sound identification, and edge-based natural language processing.

Discover how Ceva is providing the essential "picks and shovels" for the AI gold rush and why collaboration remains the key to unlocking the full potential of intelligence at the edge. Subscribe to hear more partner stories shaping the future of edge AI!

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The digital landscape is rapidly evolving beyond centralized cloud computing. In this illuminating conversation with David Aronchik, co-founder of Expanso, we explore the growing necessity of processing data right where it's generated—at the edge.

Drawing from his impressive background as the first non-founding PM for Kubernetes at Google and his leadership in open AI strategy at Microsoft, David reveals how these experiences led him to tackle a persistent challenge: how do you leverage container technologies and ML models outside traditional data centers? While cloud platforms excel at centralized workloads, businesses increasingly need computing power in retail locations, manufacturing facilities, and smart city infrastructure.

Expanso's elegantly named Bacalhau project (Portuguese for cod, a clever nod to "Compute Over Data") offers a solution by providing reliable orchestration of workloads across distributed locations. Their lightweight Go binary runs on virtually anything from Raspberry Pis to sophisticated edge servers, managing the delivery and execution of jobs while gracefully handling connectivity disruptions that would cause traditional systems to fail.

David makes a compelling case for edge computing with a simple physical reality: even 100,000 years from now, the speed of light will still impose a 45-millisecond latency between LA and Boston. This unchangeable constraint, combined with data transfer costs and regulatory requirements, makes local processing increasingly essential. For organizations struggling with high telemetry bills, Expanso confidently promises at least 25% cost reduction—or they work for free.

Whether you're managing satellite networks, underwater cameras for aquaculture, or thousands of retail locations, this conversation illuminates how the future of computing involves bringing intelligence to where data lives rather than constantly shipping bytes across networks. Join us to discover how this paradigm shift is making AI more effective in the physical world.

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