The Embedded Frontier copertina

The Embedded Frontier

The Embedded Frontier

Di: Jacob Beningo
Ascolta gratuitamente

Offerte di stagione | 0,99 €/mese per i primi 3 mesi

A seguire 9,99 €/mese – si applicano condizioni. Puoi disdire mensilmente.

The Embedded Frontier, hosted by embedded systems expert Jacob Beningo, is a cutting-edge podcast dedicated to exploring the rapidly evolving world of embedded software and embedded system trends. Each episode delves into the latest technological advancements, industry standards, and innovative strategies that are shaping the future of embedded systems. Jacob Beningo, with his deep industry knowledge and experience, guides listeners through complex topics, making them accessible for both seasoned developers and newcomers alike.

This podcast serves as an educational platform, offering insights, interviews, and discussions with leading experts and innovators in the field. Listeners can expect to gain valuable knowledge on how to modernize their embedded software, implement best practices, and stay ahead in this dynamic and critical sector of technology. Whether you're an embedded software developer, a systems engineer, or simply a tech enthusiast, "The Embedded Frontier" is your go-to source for staying updated and inspired in the world of embedded systems. Join Jacob Beningo as he navigates the intricate and fascinating landscape of embedded technologies, providing a unique blend of technical expertise, industry updates, and practical advice.

© 2026 Beningo Embedded Group, LLC Economia
Episodi
  • #027 - Agentic Firmware Development is Here

    #027 - Agentic Firmware Development is Here
    Oct 14 2026
    Jacob Beningo explains why agentic firmware development is already here and how embedded teams can move beyond ad hoc AI prompting. He covers skills, agents, RAG databases, and MCP servers, shows how NXP's new agentic AI tools put them into practice, and offers practical steps for building an agentic firmware workbench.

    Key Takeaways:

    • In a poll of 50 embedded developers at a conference, everyone used AI, but only two were building skills, agents, or RAG databases, and only one was using MCPs.
    • Ad hoc prompting is not agentic development. Agentic workflows combine skills, agents, RAG, and MCPs to get repeatable results.
    • Encode your company's coding style guide in a RAG database and give an agent access to it so code reviews and driver generation follow your standards.
    • If you use AI for the same task more than two or three times, build a skill for it.
    • Build an agent when you keep orchestrating the same skills and sub-agents, and use RAG for knowledge you keep pasting into prompts.
    • MCP servers give AI "hands" in the physical world, such as debug sessions, power supplies, and oscilloscope or logic analyzer data, so it can work iteratively in a loop.
    • Silicon vendors like NXP are releasing LLM-independent agentic toolkits with around 100 prebuilt skills, MCP connectors, AI-powered documentation, and prompt libraries.
    • As AI produces thousands of lines of code, validation and test harnesses around AI tooling become critical.
    • AI makes mistakes like human engineers do, and iterating on them is similar to agile. Used well, it can handle the boring infrastructure work so developers can focus on innovation.
    • Jacob expects embedded development to change dramatically in the next 6–12 months, so start learning these techniques now rather than falling behind.
    Mostra di più Mostra meno
    23 min
  • #026 - Metrics Dashboards to the Rescue

    #026 - Metrics Dashboards to the Rescue
    Mar 18 2026
    Jacob Beningo concludes his embedded software modernization framework series by discussing the establishment of metrics scoreboards for embedded systems development. He explores various metrics categories including code quality, performance, and security, with a deep dive into cyclomatic complexity as a powerful but underutilized metric among embedded developers.

    Key Takeaways:

    • Metrics scoreboards provide visibility into quality, predictability, and improvement opportunities in embedded development
    • Things that get measured get managed - tracking metrics helps identify root causes of development problems
    • Avoid vanity metrics like commit frequency; focus on metrics aligned with project goals
    • Cyclomatic complexity measures independent paths through functions and should stay below 10 for maintainable code
    • Seven key metrics categories: code quality, reliability/safety, testing, performance, resource utilization, process/productivity, and security
    • AI tools can significantly simplify dashboard creation and metrics analysis automation
    • Code coverage is valuable but can be gamed - ensure tests actually validate functionality, not just execute code
    • Compiler warnings should be eliminated or documented; aim for zero warnings in production code
    • Metrics trends are more important than point-in-time values for identifying code quality decay
    • Modern tools like Segger SystemView and Percepio Tracealyzer enable real-time performance metrics collection
    Mostra di più Mostra meno
    33 min
  • #025 - Modernize Your Embedded System with AI / ML

    #025 - Modernize Your Embedded System with AI / ML
    Mar 4 2026
    Jacob Beningo explores how artificial intelligence and machine learning can modernize embedded systems development, sharing practical examples and dispelling myths about AI's applicability to embedded development. He demonstrates how AI can accelerate development workflows, from requirements gathering to code generation, while emphasizing an iterative, agile approach rather than fully autonomous AI development.

    Key Takeaways:

    • AI can provide 2x performance improvements for embedded development teams through automation and assistance
    • Use AI iteratively in small problem spaces rather than attempting fully autonomous agentic workflows
    • Create specialized AI tools for specific embedded systems domain areas like requirements gathering and code reviews
    • AI can help reduce debugging time, which typically consumes 20-40% of development effort
    • Machine learning inference can run effectively on resource-constrained 16-bit microcontrollers
    • Teams ignoring AI adoption risk being left behind as the technology becomes mainstream in embedded development
    • Focus on identifying repetitive daily tasks that can benefit from AI automation
    • Modern microcontrollers with neural processing units enable sophisticated on-chip machine learning applications
    • AI works best as a productivity multiplier rather than a replacement for embedded developers
    • Start with low-hanging fruit like debugging assistance and code review automation
    Mostra di più Mostra meno
    33 min
adbl_web_anon_alc_button_suppression_t1
Ancora nessuna recensione