<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Anthropic on Tyler Wells</title><link>https://blog-theta-seven-23.vercel.app/tags/anthropic/</link><description>Recent content in Anthropic on Tyler Wells</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 23 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog-theta-seven-23.vercel.app/tags/anthropic/index.xml" rel="self" type="application/rss+xml"/><item><title>Coordinating Multiple LLM Agents: Cross-Domain Synthesis</title><link>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-3-coordinator/</link><pubDate>Thu, 23 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-3-coordinator/</guid><description>[Multi-Agent Series 3/4] How a coordinator turns specialist findings into cross-domain insights and decision-ready recommendations.</description></item><item><title>Building Specialist LLM Agents: Technical, Risk, Cost, and Timeline Analysis</title><link>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-2-specialists/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-2-specialists/</guid><description>[Multi-Agent Series 2/4] How specialist prompts, shared inheritance, and structured JSON outputs turn one LLM into multiple domain-specific analyzers.</description></item><item><title>Why Multi-Agent Systems Beat Single Agents for Complex Documents</title><link>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-1-why-multi-agent/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-1-why-multi-agent/</guid><description>[Multi-Agent Series 1/4] Why one large prompt breaks down on RFPs and contracts, and how specialist agents plus coordinator synthesis produce better analysis.</description></item><item><title>Building the AI Product Assistant: Context Injection, Multi-Turn Chat, and Cross-Product Retrieval</title><link>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-4-ai-assistant/</link><pubDate>Wed, 15 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-4-ai-assistant/</guid><description>[Retail AI Series 4/5] How to build a Rufus-style AI assistant that answers product questions, suggests complementary gear, and builds camping loadouts — with conversation history, context injection, and dynamic retrieval.</description></item><item><title>Building AI Search for a Retail Website: The Stack and Why</title><link>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-1-stack/</link><pubDate>Sun, 12 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-1-stack/</guid><description>[Retail AI Series 1/5] Building a mock outdoor retail site with AI-powered product search and a Rufus-style assistant. This post covers the architecture, stack decisions, and why RAG matters for e-commerce.</description></item></channel></rss>