How Netflix, LinkedIn and Others Use Node.js to Scale at Millions of Users

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Major tech companies like Netflix and LinkedIn have adopted Node.js to handle large-scale, I/O-heavy workloads more efficiently than traditional server architectures. Netflix reduced its application startup time by 70% after migrating away from a Java-based server-side rendering layer, while LinkedIn shrank its server footprint from 30 machines to just 3 while handling the same traffic volume. The performance gains stem from Node.js's non-blocking event loop, which handles thousands of concurrent connections on a single thread without the memory and CPU overhead of thread-per-request models. Running JavaScript across both frontend and backend also reduces coordination costs for engineering teams, allowing shared code and easier movement between layers. However, experts note that Node.js is best suited for I/O-intensive applications and performs poorly on CPU-heavy workloads, making use-case context critical to any adoption decision.

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Java Programming for Raspberry Pi Now Available in Print via Amazon KDP

Author Frank Delporte has released physical softcover and hardcover editions of 'Java Programming for Raspberry Pi: A Hands-On Guide to Electronics and IoT Projects' on Amazon through Kindle Direct Publishing. The book was originally published in print by Elektor in 2020, but publishing rights for a paper edition were later returned to the author. Delporte self-published the new print editions using Amazon's print-on-demand service after manually reformatting the Leanpub manuscript to meet KDP layout and cover requirements. The current edition reflects a major rewrite completed in late 2025 and early 2026, bringing content up to date with Java 25 and Pi4J V4. The Leanpub ebook remains the most frequently updated version, while the Kindle ebook and a pay-as-you-wish digital edition are also available.

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What Large Language Models Actually Are and How They Work

Large language models (LLMs) are computer programs trained on trillions of words of text to predict the next word or token in a sequence, with no true understanding or consciousness involved. Models like GPT-4 are built on neural networks containing an estimated 1.7 trillion parameters, trained on roughly 13 trillion tokens sourced from books, websites, and other internet content. After initial training, LLMs undergo additional fine-tuning to follow instructions and behave as helpful chatbots while refusing harmful requests. While LLMs excel at text generation, summarization, and code assistance, they struggle with accurate math, real-time information, and can confidently produce false information — a phenomenon known as hallucination. Training such models costs between $50 million and $100 million, and ongoing inference costs mean API access for developers typically comes at a price.

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What Are Large Language Models? A Plain-Language Guide to LLM Basics

Large language models (LLMs) like ChatGPT, Claude, and Gemini are neural network programs trained on trillions of words to predict the next word in a sequence, rather than to truly understand language. Models such as GPT-4 are built with an estimated 1.7 trillion parameters and trained on roughly 13 trillion tokens sourced from books, websites, and other internet text. After initial training, LLMs undergo additional fine-tuning to make them behave as helpful, instruction-following chatbots. While they excel at text generation, summarization, and code assistance, they struggle with accurate math, real-time information, and can confidently produce false statements — a phenomenon known as hallucination. Training a large model can cost between $50 million and $100 million, and ongoing inference costs mean API access typically comes with a price tag.

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Why Developers Should Learn JSON-LD Schema for Better Search Visibility

JSON-LD (JavaScript Object Notation for Linked Data) is a standardized format that allows developers to explicitly describe a webpage's content to search engines, rather than leaving them to infer meaning from HTML alone. Unlike traditional SEO tactics such as keyword optimization, JSON-LD is a technical implementation that helps search engines better interpret and display content in results. Google recommends the format for its ease of implementation, and it is applicable across websites, blogs, SaaS products, and ecommerce platforms. As search increasingly shifts toward AI-generated answers and entity-based understanding, structured data is growing in importance. Developers are advised to validate their JSON-LD using tools like Google's Rich Results Test before deployment to catch syntax errors and unsupported properties.

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