NaN in Practice: What It Is, When to Use It, and How to Parse It Safely
Practical guide to NaN: IEEE 754 behavior, parsing in different languages, when it helps, and how to protect code from silent bugs.
Embedded
Bootloader vs Firmware vs Operating System: Understanding Embedded Software Layers
Demystify the embedded software stack. Learn the differences between bootloaders, firmware, and OS—and why each layer exists in MCUs and MPUs.
Meshtastic Explained: Long-Range LoRa Mesh Messaging Without Internet
Complete guide to Meshtastic: off-grid mesh messaging with LoRa radios. Hardware setup, software configuration, real-world use cases, and code examples.
Choosing the Right Microcontroller for Your IoT Project: ESP32, STM32, nRF, RP2040, or RISC-V?
Practical MCU selection guide comparing popular families for IoT projects. Understand cost, power consumption, wireless capabilities, and ecosystem before choosing your next microcontroller.
Embedded Linux RAM Requirements: How Much Memory Does Your System Really Need?
A practical guide to sizing RAM for embedded Linux systems: kernel usage, userspace overhead, realistic memory budgets, and optimization techniques for real products.
What Makes a Processor Capable of Running Linux? MMU, RAM, Storage, and Architecture Requirements
Learn what hardware is required to run embedded Linux, including the role of an MMU, practical RAM and storage requirements, why standard Linux does not run on most Cortex-M microcontrollers, and when NOMMU systems make sense.
Why MHz Does Not Equal Performance: CPU Clock Speed vs Real-World MCU Throughput
Understand why CPU clock speed alone cannot predict MCU performance. Learn how IPC, pipelines, memory wait states, caches, buses, compilers, and workload shape real embedded throughput.
CoreMark Benchmark Explained: How to Compare Microcontroller Performance Accurately
A practical guide to the CoreMark benchmark: what it measures, how CoreMark/MHz is calculated, how it compares with DMIPS, and how to use benchmark results without mistaking them for application performance.
CUDA and NPU Explained: GPU Acceleration, AI Inference, and Edge Performance for Embedded and Data Center Systems
A clear, practical guide to CUDA and NPU hardware for AI and ML workloads, including what they are, how they are used, where they shine, and simple examples for GPU and edge inference.
Hardware FPU, VPU, NEON, and SIMD: How They Supercharge CPU Performance in Embedded Systems
Clear explanation of hardware FPU, VPU, NEON, and SIMD instructions and their massive impact on CPU performance. Real benchmarks, use cases, and comparisons showing 10-100x speedups in signal processing, graphics, ML, and math-heavy applications.