Browse our library of resources to learn more about embedded software for pcb design.
Build a local‑LLM datasheet extractor to speed IC driver development. Structure PDFs into usable data, automate workflows, and enable faster, smarter firmware creation.
Turn the Analog Discovery 2 into an AI-controlled bench tool. Learn to build an MCP server on WaveForms with FastMCP and avoid common LLM driver pitfalls.
This article reviews the construction of a Model Context Protocol (MCP) server tailored for lab equipment, enabling large language models (LLMs) to autonomously manage devices like power supplies through simple voice commands or plain text instructions. It covers essential aspects such as defining functions with clarity, implementing safety precautions, and the process of wrapping lab instruments to create reliable LLM-assisted procedures. Readers will learn how to enhance their experiments with AI integration while understanding the importance of safety when interfacing with real-world hardware. - Discover how to build an MCP server that allows LLMs to control lab equipment autonomously and safely. - Learn best practices for function definition, testing, and implementing safety guardrails in your setup. - Gain insights into automating lab procedures using your voice or plain text commands, transforming the way you conduct experiments. Continue reading to explore how you can leverage AI to revolutionize your lab processes and enhance your experimental capabilities.
Learn how to shrink Arduino projects by going straight to the Atmega328P chip. Simple steps to faster, cleaner prototyping.
In this article, we explore the transformative impact of artificial intelligence on embedded development, providing practical techniques to optimize your coding workflow. The author addresses common misconceptions surrounding overnight success stories in AI, emphasizing the importance of a solid foundation in app design and marketing. By sharing valuable principles such as planning collaboratively with AI, utilizing hardware abstraction layers, and implementing effective debugging strategies, readers will discover how to enhance their productivity and the quality of their output. Key takeaways include: - Practical strategies to effectively collaborate with AI tools in embedded development. - Techniques for improving code quality and accelerating the development process. - Insights on managing AI workflows to minimize potential pitfalls when working with hardware. Keep reading to unlock your potential and elevate your embedded coding projects with these cutting-edge practices!
Let identical PCBs self-configure at startup. No DIP switches or resistor stuffing needed. Discover how embedded devices assign roles with code.
Complex system bring up behavior can be controlled with a system reset IC.