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 perspective on where AI adds real value: condensing complex data, identifying anomalies, and helping teams move faster without sacrificing quality. We also tackle the challenges: the brittleness of traditional testing approaches, the difficulty of tracking dependencies in embedded systems, and the risks of over-automation. Throughout the conversation, we explore the balance between deterministic tools and AI-assisted workflows, and why human judgment remains essential—especially when it comes to security updates and edge cases that no test script would catch.

Whether you're skeptical about AI hype or curious about practical applications, this episode offers a grounded look at how AI can strengthen your development pipeline without replacing the engineers who build it.

Key Topics:

• [00:00] Introduction: Meet Joe Schneider and the focus on AI in embedded DevOps
• [02:30] The complexity challenge: Why modern embedded development needs better pipelines
• [05:15] Bob's machine and the problem with manual, hero-driven development
• [08:00] What AI is good at: Summarization, classification, and expansion
• [12:45] Exploratory testing: Can AI fish for bugs more effectively than random testing?
• [18:20] Visual analysis in hardware-in-the-loop testing: Using AI to evaluate screens and physical behavior
• [24:00] Walking through the pipeline: Build stage, static analysis, and AI-assisted triage
• [30:15] Compiler flags and configuration: Where AI can help optimize and catch mistakes
• [35:40] PR review automation: AI as a code reviewer—benefits and limitations
• [42:00] Self-healing pipelines: Automatic dependency updates and security patching
• [48:30] The human-in-the-loop debate: When automation goes too far
• [52:15] Hardware testing challenges: From pixel-perfect comparisons to AI-based visual validation
• [58:00] War story: Debugging a silicon bug that only appeared under specific conditions

Notable Quotes:

"In 2025, there are still many companies that build and release firmware from a folder on a share drive somewhere that says V1.2, or it's Bob's machine. Somebody's literally clicking the build button, and that's just very sad." — Joe Schneider

"If your product is destined for a human user, then you need a human to test it at some point in your stack. Humans are not good at following a test script for the 50th time, but they're great at finding the things you didn't think to test for." — Joe Schneider

"AI is very helpful when you have a bunch of different situations and you can ask it: does this fall into this bucket or that bucket? That classification capability can be extremely useful in analyzing what's happening in your system or pipeline." — Luca Ingianni

"I don't trust the scripts that I write. There's still people clicking buttons and mashing screens to make sure that things are working correctly, because we don't even trust the people that were downstream of us doing this work before." — Ryan Torvik

"I think testing a lot like fishing. You don't just drive your boat a random amount in a random direction and drop the line. Fishermen know where the fish are—over in the weeds, under the dock. AI can learn those signals too." — Joe Schneider

Resources Mentioned:

• Dojo5 - Custom firmware development company founded by Joe Schneider
• EmbedOps - Industry-leading embedded development DevOps framework created by Dojo5
• Zephyr RTOS Security Tool - Tool within Zephyr that evaluates compiler flags and security posture—often underutilized
• PC-Lint - Traditional static analysis tool for C/C++, known for verbose output