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.

Beyond the technical war stories, we discuss what makes AI products actually useful rather than just technically impressive. Souvik emphasizes the importance of keeping humans in control, building trust through transparency, and understanding your power budget before anything else. Whether you're working with microcontrollers or mini PCs, this episode offers practical insights into the messy reality of bringing AI-enabled embedded products from concept to reality.

Key Topics:

• [00:00] Introduction and welcoming first guest Souvik Pal from FyeLabs
• [02:30] Evolution of embedded AI: from cloud-based processing to edge computing
• [04:00] Case study: Wearable safety device with rear-facing camera for threat detection
• [08:00] Attempting to run object detection on ESP32: memory constraints and quantization challenges
• [12:00] Moving to Raspberry Pi Zero: trade-offs between power consumption and capability
• [15:00] Model selection: working with COCO dataset and YOLO for embedded environments
• [20:00] Case study: Smart door system with 4K display, facial recognition, and voice authentication
• [25:00] Running multiple AI models concurrently: video streaming, object detection, voice recognition, and gesture detection
• [30:00] Wake word detection and voice command processing without full transcription
• [35:00] Hardware selection: from ESP32 to Raspberry Pi to mini PCs and thermal management
• [40:00] Linux audio challenges and managing concurrent AI pipelines
• [45:00] Building good AI products: user experience, trust, and keeping humans in control
• [50:00] Design process for AI-enabled products: power budget as the primary consideration
• [55:00] Hardware progression: ESP32, Raspberry Pi Zero, Pi 5, Jetson, and when to use each

Notable Quotes:

"The way I define embedded is where we have constraints, either cost in space or compute or power. And that's where it becomes really challenging to deploy any sort of advanced algorithmic solutions." — Souvik Pal

"A good AI would strike a balance between what it enables the user to do and what it does for itself. I think we should let the human know that they're interacting with an AI, however smart that AI might be." — Souvik Pal

"When I think of an AI solution, it starts with power. That's number one consideration. What is your power budget? That immediately restricts you in terms of what you can do." — Souvik Pal

"You know people worried about AGI... the amount of work you've had to do to replace a doorman in this situation." — Ryan Torvik

Resources Mentioned:

• COCO Dataset - Common Objects in Context dataset - a go-to dataset for object detection with 50+ pre-trained classes
• YOLO (You Only Look Once) - Object detection model well-suited for compute-constrained embedded environments, with recent versions showing promise for edge deployment
• Open Wake Word - Wake word detection engine used for voice-activated systems