Explain the jargon in the Qwen3.8 post, plus a local LLM glossary

app/blog/[slug]/page.js

@@ -10,6 +10,7 @@ import BlogToc from '@/components/BlogToc';
 import BlogShareBar from '@/components/BlogShareBar';
 import NewsletterCTA from '@/components/NewsletterCTA';
 import SponsorCallout from '@/components/SponsorCallout';
+import GlossaryNote from '@/components/GlossaryNote';
 import AuthorSocials from '@/components/AuthorSocials';
 import Comments from '@/components/Comments';
 import { safeJsonLd } from '@/lib/jsonld';
@@ -258,6 +259,8 @@ export default async function BlogPost({ params }) {
 
               <SeriesBanner info={seriesInfo} />
 
+              <GlossaryNote post={post} />
+
               {/* Mobile TOC (desktop has it in the right sidebar) */}
               {toc.length > 2 && (
                 <details className="lg:hidden mb-8 rounded-xl border p-4" style={{ borderColor: 'var(--border-subtle)', background: 'var(--bg-elevated)' }}>

components/GlossaryNote.jsx

@@ -0,0 +1,29 @@
+import Link from 'next/link';
+import { GLOSSARY_SLUG, showsGlossaryLink } from '@/lib/glossary';
+
+/**
+ * One compact line above the post body for jargon-heavy posts. Deliberately not
+ * a banner: it should not push the article below the fold on mobile. Plain
+ * inline text rather than flex, so nothing orphans onto its own line when it
+ * wraps on a narrow screen.
+ */
+export default function GlossaryNote({ post }) {
+  if (!showsGlossaryLink(post)) return null;
+
+  return (
+    <p
+      className="text-sm leading-relaxed mb-8 pl-3 border-l-2"
+      style={{ borderColor: 'var(--accent)', color: 'var(--text-secondary)' }}
+    >
+      New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the{' '}
+      <Link
+        href={`/blog/${GLOSSARY_SLUG}`}
+        className="font-semibold hover:underline"
+        style={{ color: 'var(--accent)' }}
+      >
+        local LLM glossary
+      </Link>
+      .
+    </p>
+  );
+}

content/blog/claude-code-leak-what-the-source-actually-teaches.md

@@ -7,6 +7,7 @@ slug: claude-code-leak-what-the-source-actually-teaches
 author: saiyam-pathak
 cover: /img/blog/claude-code-leak-what-the-source-actually-teaches/0582a05f-42f3-4b97-8512-9c2133603126.svg
 tags: ["typescript", "llm", "ai-agents", "ai-engineering", "claude-code"]
+glossary: false
 cuid: cmng6ranm00wj1qkxcebmdpiy
 ---
 Let me start with the honest version of what happened.

content/blog/docker-mcp-catalog.md

@@ -7,6 +7,7 @@ slug: docker-mcp-catalog
 author: saloni-narang
 cover: /img/blog/docker-mcp-catalog/dbdb9d02-71cb-42b5-b660-68290ac7d695.png
 tags: ["ai", "docker", "docker-images", "docker-desktop", "llm", "mcp"]
+glossary: false
 cuid: cmcd2f66f000u02l45g9529xf
 ---
 As large language models (LLMs) evolve from static text generators to dynamic agents capable of executing actions, there's a growing need for a standardized way to let them interact with external tooling securely. That’s where [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) steps in, a protocol designed to turn your existing APIs into AI-accessible tools. Think of MCP as the missing middleware between LLMs and the real-world functionality you’ve already built. Instead of doing the prompt hacks or building custom plugins for each model, MCP allows you to define your capabilities as structured tools that any compliant AI client can discover, invoke, and interact with safely and predictably. While the protocol is still maturing and the documentation can be opaque, the underlying value is clear: MCP turns your backend into a toolbox for AI agents. Whether you're integrating scraping APIs, financial services, or internal business logic, MCP offers a portable, reusable, and scalable pattern for AI integrations. In this blog, we’ll walk through Docker Desktop's latest MCP client-server feature and explore how you can install an MCP server and use that directly from your LLM tool.  

content/blog/k8sgpt-tutorial-when-kubernetes-meets-ai.md

@@ -7,6 +7,7 @@ slug: k8sgpt-tutorial-when-kubernetes-meets-ai
 author: kunal-verma
 cover: /img/blog/k8sgpt-tutorial-when-kubernetes-meets-ai/cc4d6ad6-1b11-45ef-9e94-9e9ad31251aa.png
 tags: ["ai", "kubernetes", "openai", "llm", "ollama"]
+glossary: false
 cuid: clz9trn19000409mi6km2cpkh
 ---
 We all agree that in recent years, AI has taken the world by storm. With tools like ChatGPT and platforms developed by OpenAI leading the charge — AI is being used in many industries to make work easier, provide helpful insights, and boost productivity. According to [The State of AI 2023 by McKinsey](https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai%20in%202023%20generative%20ais%20breakout%20year/the-state-of-ai-in-2023-generative-ais-breakout-year_vf.pdf), AI adoption has increased by over 60% in the past year alone, with businesses using AI to stay ahead and innovate!