<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Anthropic on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/anthropic/</link><description>Recent content in Anthropic on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 21 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/anthropic/index.xml" rel="self" type="application/rss+xml"/><item><title>Your Agent&amp;#39;s Brain: Connecting to Large Language Models</title><link>https://ai-blog.noorshomelab.dev/agentic-ai-guide-2026/llm-as-agent-brain/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/agentic-ai-guide-2026/llm-as-agent-brain/</guid><description>&lt;h2 id="your-agents-brain-connecting-to-large-language-models"&gt;Your Agent&amp;rsquo;s Brain: Connecting to Large Language Models&lt;/h2&gt;
&lt;p&gt;Welcome back, future agent architect! In the previous chapter (we assume you&amp;rsquo;ve covered the basics of what an autonomous agent is), we explored the grand vision of AI agents that can think, act, and learn. But how do these agents actually &lt;em&gt;think&lt;/em&gt;? What gives them the ability to understand complex instructions, reason through problems, and generate coherent responses?&lt;/p&gt;
&lt;p&gt;The answer, for most modern agentic systems, lies with &lt;strong&gt;Large Language Models (LLMs)&lt;/strong&gt;. Think of an LLM as the highly intelligent, incredibly versatile &amp;ldquo;brain&amp;rdquo; of your agent. This chapter will be your deep dive into understanding how LLMs power agent intelligence, how your agent communicates with them, and how to make your very first connection. Get ready to give your agent its first spark of cognitive ability!&lt;/p&gt;</description></item><item><title>Opus 4.7 System Prompt: The Hidden Changes &amp;amp; Your New Strategy</title><link>https://ai-blog.noorshomelab.dev/blog/opus-4-7-system-prompt-hidden-changes-new-strategy/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/blog/opus-4-7-system-prompt-hidden-changes-new-strategy/</guid><description>&lt;p&gt;Claude Opus 4.7 just dropped, promising enhanced capabilities. But beneath the surface, a subtle yet powerful change in its system prompt has profound implications for every developer building with Claude. Are your existing prompts ready for the shift, or are you unknowingly setting your applications up for unexpected behavior?&lt;/p&gt;
&lt;p&gt;The core thesis here is critical: The subtle yet significant changes in Claude Opus 4.7&amp;rsquo;s system prompt fundamentally alter model behavior, demanding developers proactively adapt their prompt engineering strategies to leverage new capabilities and avoid regressions in critical applications. Ignoring these shifts is not an option for production-grade AI systems.&lt;/p&gt;</description></item></channel></rss>