Hardware Podcasts

446: World's Best PB&J

Embedded.fm · 30 Mar 2023 · 54m · Episode 446

What this episode covers

Hosts Elecia White and Christopher White interview an AI language model, referred to as 'Christopher White' or ChatGPT, discussing its capabilities and limitations in embedded systems and software development. They explore the use of tools like Compiler Explorer (godbolt.org) and Google Colab for coding tasks, and discuss the complexities of using ArduPilot for drone projects. The conversation highlights the distinction between statistical language models and true artificial intelligence, noting issues with hallucination and lack of reasoning.

Most relevant to

makers and hobbyists

Topics

Mentioned

Chips
STM32, PICs
Tools
Compiler Explorer, Google Colab, Jupyter Notebook
Also
ChatGPT, godbolt.org, Colab, ArduPilot, QGroundControl, Mission Planner, PX4, Flask, NumPy, Matplotlib

Related episodes from other shows

Other shows on the same ground, closest first.

  • The Pragmatic Embedded Podcast · 45m

    AI-augmented software development

    Host Jeff Gable interviews Luca Johnny about integrating AI tools into embedded software development workflows. They compare IDE-integrated tools like GitHub Copilot and Cursor with command-line agents like Aider, emphasizing that workflow integration matters more than the underlying LLM. The discussion covers specific use cases for testing, debugging, and learning new frameworks, while warning that AI cannot replace the architectural judgment of experienced engineers.

    AI & machine learningEmbedded firmware
    6 chapters

  • The Pragmatic Embedded Podcast · 28m

    AI-enhanced Embedded Development (May 2025 Edition)

    Luca, an embedded software expert, discusses his experience at the Embedded Online Conference and a German conference, highlighting an LLM-only hackathon where participants were forbidden from writing code manually. He explains that effective AI-assisted development requires rigorous context management, treating prompts like version-controlled code, and managing external context files to prevent hallucinations. The conversation covers the economic benefits of AI (approx. $0.01 per line of code), the tightening of feedback loops, and the evolving role of developers from coders to AI supervisors and technical product owners.

    Embedded firmwareAI & machine learning
    7 chapters

  • The Pragmatic Embedded Podcast · 49m

    Buzzword Bingo (or: which modern technologies make sense for embedded?)

    Hosts Jeff Gable and Luca Ingianni evaluate modern development buzzwords for embedded systems, discussing the applicability of web technologies like Kubernetes and Docker, the nuances of CI/CD pipelines, and the practical adoption of Rust and modern C++. They highlight specific tools such as Memfault, Mender, and Renode for observability and simulation, and analyze the role of AI in both device-side machine learning and developer productivity.

    Embedded firmwareMicrocontrollers & dev platforms
    8 chapters

  • Coredump Sessions · 55m

    #007: AI, Open Source, and the Future of Embedded Development: How Much Code Will We Actually Write?

    François Baldassari, CEO of Memfault, and Thomas Sarlandie, Field CTO of Memfault, host a panel with Jonathan Beri, CEO of Golioth, and Dan Mangum, CTO of Golioth, to discuss the intersection of AI, open source, and embedded development. The group analyzes the maturity of edge AI for audio and video processing, the challenges of integrating LLMs into hardware due to rapid model iteration, and the current state of using AI for firmware development. They conclude that while AI is transforming code generation and review, open source remains critical for training models and providing trusted, complex infrastructure like Zephyr and FreeRTOS.

    Embedded firmwareAI & machine learning
    9 chapters

  • IoT For All Podcast · 21m

    Can AI Design IoT Hardware? | Flux's Matthias Wagner | Internet of Things Podcast

    Matthias Wagner, Founder and CEO of Flux, explains how his company uses agentic AI to democratize PCB design, allowing non-engineers to create hardware via natural language prompts. He discusses the historical bottlenecks of hardware design, the necessity of iterative back-and-forth for complex devices like smartphones, and the economic advantages of designing custom boards for products like vending machines. Wagner also outlines future integration challenges, including firmware generation and enclosure design, and predicts that lowering the cost of iteration will unlock significant innovation in robotics and IoT.

    AI & machine learningPCB design & layout
    8 chapters

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by Embedded.fm. The episode itself belongs to its publisher.