30 React.js Interview Questions Covering Core Frontend Concepts

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A technical guide published on DEV Community compiles 30 React.js interview questions aimed at developers preparing for frontend job interviews. The questions span real-world scenarios across topics such as conditional rendering, API calls with useEffect, form validation, and performance optimization. The guide also covers state management strategies, including Context API and Redux, as well as component re-rendering behavior and large-list optimization. Rather than focusing on rote memorization, the resource emphasizes understanding when and how to apply React concepts in practical applications. Developers are advised to use tools like React.memo, useMemo, and useCallback selectively, based on actual profiling results rather than by default.

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Developer runs 180M-parameter MoE language model on a $10 ESP32-P4 microcontroller

A developer named cyfrit published a project called p-for-llm in early August 2026, demonstrating a 180.9-million-parameter mixture-of-experts language model running inference on an ESP32-P4 microcontroller priced between six and ten dollars. The model uses ternary-weight quantization and top-1 expert routing to keep compute costs low, generating around 9 tokens per second within the chip's limited memory. Notably, cyfrit trained the model on roughly 12 billion tokens using a single consumer RTX 5060 Ti GPU, without institutional backing or a compute cluster. The project carries one significant caveat: weights must currently be loaded onto the board via USB from a host machine at startup, meaning the device is not yet fully self-contained. SD card loading, which would make the board fully independent, is listed by the author as planned future work rather than a completed feature.

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ProgrammingDEV Community ·

LLMs Now Run Locally on $8 Microcontrollers at Near-Reading Speed

On July 25, 2026, a developer known as slvDev published esp32-ai, demonstrating a 28.9-million-parameter language model running entirely on an ESP32-S3 microcontroller at 9.88 tokens per second — roughly human reading speed. The feat was made possible not by new hardware but by a memory-hierarchy architecture inspired by Google's Gemma 3n, splitting the model so a small reasoning core sits in RAM while a large embedding table is accessed in tiny slices from flash storage. The project drew coverage from Tom's Hardware, The Register, and others, representing a hundredfold jump from the previous ESP32 parameter record of 260,000. A lesser-noticed follow-up project, p-for-llm, went further on August 5, running a 180.9-million-parameter mixture-of-experts model on the similarly priced ESP32-P4 at around 9 tokens per second, with basic instruction-following and an early tool-calling capability. Together, the two projects mark a significant shift in what edge AI hardware can achieve without any cloud offloading.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

Developer Builds xAgent to Make AI Automation Affordable for Small Teams

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