<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Llm on Mazin Ahmed</title><link>https://mazinahmed.net/tags/llm/</link><description>Recent content in Llm on Mazin Ahmed</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 07 Jul 2026 11:28:35 +0000</lastBuildDate><atom:link href="https://mazinahmed.net/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>Backlog: A Local-First Task and Context Manager for Humans and AI</title><link>https://mazinahmed.net/blog/backlog-project/</link><pubDate>Tue, 07 Jul 2026 11:28:35 +0000</pubDate><guid>https://mazinahmed.net/blog/backlog-project/</guid><description>&lt;p&gt;&#10;&#10;&#10;&#10;&#10;&#10;&#10; &#10; &#10;&lt;figure&gt;&lt;img src="https://mazinahmed.net/uploads/assets/static/backlog-project/backlog-blog-banner.webp" alt="Backlog: a local-first task and context manager for humans and AI" class="mx-auto my-0 rounded-md" /&gt;&#10;&lt;/figure&gt;&#10;&lt;/p&gt;&#10;&lt;p&gt;I&amp;rsquo;ve been working with agentic AI for the past two years, and I kept hitting the same wall: managing the tasks and to-dos it generates. Moving what it learns between sessions. Improving a product without keeping one session open for days.&lt;/p&gt;&#10;&lt;p&gt;Keeping a session open for days builds up context, and I get billed for all of it on every request to Claude. Caching helps, but it&amp;rsquo;s still expensive. The agent forgets what happened, especially the small details, and there&amp;rsquo;s no real way to hold onto it.&lt;/p&gt;</description></item><item><title>Preventing Prompt Injection Attacks at Scale</title><link>https://mazinahmed.net/blog/preventing-prompt-injection-attacks-at-scale/</link><pubDate>Mon, 09 Jun 2025 00:00:00 +0000</pubDate><guid>https://mazinahmed.net/blog/preventing-prompt-injection-attacks-at-scale/</guid><description>&lt;hr&gt;&#10;&lt;p&gt;&#10;&#10;&#10;&#10;&#10;&#10;&#10; &#10; &#10;&lt;figure&gt;&lt;img src="https://mazin.s3.amazonaws.com/public/6ea2bdf1-1f8f-44c8-a164-714f9dbe5db5/Preventing%20Prompt%20Injection%20Attacks%20at%20Scale.jpg" alt="" class="mx-auto my-0 rounded-md" /&gt;&#10;&lt;/figure&gt;&#10;&lt;/p&gt;&#10;&lt;p&gt;Prompt injection attacks are one of the most common LLM security threats we have all seen while reviewing LLM implementations, integrations, and AI-powered systems.&lt;/p&gt;&#10;&lt;p&gt;I have identified Prompt Injection attacks in numerous implementations and applications, and I’ve decided to write a blog post to recommend different methodologies I experimented with that can prevent prompt injection at scale.&lt;/p&gt;&#10;&lt;p&gt;I will also share a LangChain Prompt Injection attack vector, vulnerabilities in example MCP implementations, and practical ways to prevent prompt injection attacks.&lt;/p&gt;</description></item></channel></rss>