<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Rate Limiting on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/rate-limiting/</link><description>Recent content in Rate Limiting on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 08 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/rate-limiting/index.xml" rel="self" type="application/rss+xml"/><item><title>Performance Tuning and Caching Strategies</title><link>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/performance-caching/</link><pubDate>Tue, 30 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/performance-caching/</guid><description>&lt;h2 id="introduction-to-performance-tuning-and-caching"&gt;Introduction to Performance Tuning and Caching&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 9! So far, you&amp;rsquo;ve mastered the fundamentals of &lt;code&gt;any-llm&lt;/code&gt;, effortlessly switching between various LLM providers and handling different types of AI interactions. That&amp;rsquo;s fantastic! But as your applications grow and user demand increases, you&amp;rsquo;ll inevitably hit a critical crossroads: &lt;strong&gt;performance and cost&lt;/strong&gt;. Every interaction with an LLM provider incurs latency, consumes resources, and often, costs money. Imagine if every user asking the same question triggered a brand new, expensive API call – that would quickly become unsustainable!&lt;/p&gt;</description></item><item><title>Chapter 11: Implementing Robust Security: Rate Limiting, CORS, &amp;amp; RBAC</title><link>https://ai-blog.noorshomelab.dev/scalable-nodejs-api-platform/11-security-rbac/</link><pubDate>Thu, 08 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/scalable-nodejs-api-platform/11-security-rbac/</guid><description>&lt;h2 id="chapter-11-implementing-robust-security-rate-limiting-cors--rbac"&gt;Chapter 11: Implementing Robust Security: Rate Limiting, CORS, &amp;amp; RBAC&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 11 of our Node.js backend journey! In this chapter, we&amp;rsquo;re diving deep into critical security enhancements that are non-negotiable for any production-ready application: Rate Limiting, Cross-Origin Resource Sharing (CORS), and Role-Based Access Control (RBAC). These mechanisms are essential for protecting your API from abuse, enabling secure interactions with frontend applications, and ensuring users only access resources they are authorized to see.&lt;/p&gt;</description></item><item><title>Guided Project 2: Distributed Caching with Rate Limiting</title><link>https://ai-blog.noorshomelab.dev/redis-guide/project-distributed-cache-ratelimit/</link><pubDate>Fri, 07 Nov 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/redis-guide/project-distributed-cache-ratelimit/</guid><description>&lt;p&gt;This project combines two fundamental Redis use cases crucial for scalable web applications:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Distributed Caching&lt;/strong&gt;: Storing frequently accessed data in Redis to reduce the load on primary databases and speed up response times.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Rate Limiting&lt;/strong&gt;: Preventing abuse of APIs or services by restricting the number of requests a user or client can make within a given time window.&lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;We&amp;rsquo;ll build a simplified API-like service that uses Redis for both caching and rate limiting, demonstrated with Node.js and Python.&lt;/p&gt;</description></item></channel></rss>