<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Streaming Input on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/streaming-input/</link><description>Recent content in Streaming Input on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 17 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/streaming-input/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 10: Performance Optimization &amp;amp; Streaming Input</title><link>https://ai-blog.noorshomelab.dev/mermaid-lint-guide/chapter-10-performance-optimization-streaming/</link><pubDate>Tue, 17 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/mermaid-lint-guide/chapter-10-performance-optimization-streaming/</guid><description>&lt;h2 id="chapter-10-performance-optimization--streaming-input"&gt;Chapter 10: Performance Optimization &amp;amp; Streaming Input&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;Welcome to Chapter 10! So far, we&amp;rsquo;ve meticulously built a robust Mermaid code analyzer, validator, and fixer with a strong emphasis on correctness and maintainability. Our lexer, parser, AST, and rule engine are designed to be strict and deterministic. However, as diagrams grow in complexity and size, performance and memory footprint become critical concerns, especially for a production-grade CLI tool that might process thousands of lines of Mermaid code or be integrated into CI/CD pipelines.&lt;/p&gt;</description></item></channel></rss>