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      <image:caption>Fuel Your Game</image:caption>
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      <image:caption>5-Minute Meal Planning</image:caption>
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      <image:caption>Claude Code interface on MacBook Pro next to vintage CRT monitor displaying old Django Python code</image:caption>
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    <loc>https://www.mosaicmeshai.com/blog/bart-test-part-10-the-stochastic-parrot-and-what-visible-thinking-traces-might-reveal</loc>
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      <image:caption>Comparing biological and artificial cognitive processes - are they more similar than different?</image:caption>
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      <image:title>Blog - The Efficiency Trap: What We Lose When AI Optimizes Everything - Make it stand out</image:title>
      <image:caption>Standing in the tension between AI powered efficiency and retained humanity</image:caption>
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      <image:title>Blog - The Bart Test - Part 9: The Question I Couldn't Answer - Make it stand out</image:title>
      <image:caption>Time to pause the Bart Test experiment? The question I couldn't answer with the data I had.</image:caption>
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      <image:title>Blog - The Bart Test - Part 8: When an Interesting Experiment Might Not Be a Useful Tool (And Why That's Okay) - Make it stand out</image:title>
      <image:caption>Two paths representing novelty and utility diverging, crossing, and diverging again - showing their unpredictable relationship over time</image:caption>
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      <image:title>Blog - The Bart Test - Part 7: The Social Cost I Didn't See Coming - Make it stand out</image:title>
      <image:caption>Social cost visualization showing the tension between positive and negative peer judgment</image:caption>
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    <lastmod>2026-01-16</lastmod>
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      <image:title>Blog - The Bart Test - Part 6: The American Ninja Warrior Problem - Make it stand out</image:title>
      <image:caption>An obstacle course showing how difficulty creates differentiation between competitors</image:caption>
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      <image:title>Blog - The Bart Test - Part 6: The American Ninja Warrior Problem - Make it stand out</image:title>
      <image:caption>The test iteration cycle: analyzing Experiment 04 feedback to refine the approach for Experiment 05</image:caption>
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      <image:title>Blog - The Bart Test - Part 5: Redesigning From Scratch - Make it stand out</image:title>
      <image:caption>The iteration process: from chaos and doubt through collaborative design to a clean structured solution</image:caption>
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      <image:title>Blog - The Bart Test - Part 5: Redesigning From Scratch - Make it stand out</image:title>
      <image:caption>Before and after: from 1,500-word stories with text message feedback taking 50 minutes to 50-100 word scenarios with paper sheets taking 10 minutes</image:caption>
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      <image:title>Blog - The Bart Test - Part 4: When My Teen Judges Ghosted Me - Make it stand out</image:title>
      <image:caption>The shift from casual favor to structured work: what started as friendly feedback became a demanding evaluation task</image:caption>
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    <lastmod>2026-01-09</lastmod>
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      <image:title>Blog - The Bart Test - Part 3: The Zoo-Not-Duck Problem - Make it stand out</image:title>
      <image:caption>The zoo-not-duck problem: AI outputs create overwhelming noise when clear focus is needed</image:caption>
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      <image:title>Blog - The Bart Test - Part 3: The Zoo-Not-Duck Problem - Make it stand out</image:title>
      <image:caption>Experiment 03's two-stage hybrid workflow: AI judges pre-screen, humans validate the top candidates</image:caption>
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    <lastmod>2026-01-07</lastmod>
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      <image:title>Blog - The Bart Test - Part 2: Testing the Overthinking Hypothesis - Make it stand out</image:title>
      <image:caption>Token counts for four experiments, with three approaches increasing overthinking and only style anchor reducing it</image:caption>
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    <lastmod>2026-01-05</lastmod>
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      <image:title>Blog - The Bart Test - Part 1: When AI Does Its Homework Too Well - Make it stand out</image:title>
      <image:caption>Slang density spectrum showing the challenge: too formal, natural sweet spot, and trying too hard</image:caption>
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      <image:title>Blog - The Bart Test - Part 1: When AI Does Its Homework Too Well - Make it stand out</image:title>
      <image:caption>Comparison of rubric optimization versus natural writing approaches</image:caption>
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      <image:title>Blog - The Bart Test - Part 1: When AI Does Its Homework Too Well - Make it stand out</image:title>
      <image:caption>Diagram showing the inverse relationship between planning effort and output naturalness</image:caption>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 8: Lessons Learned - Make it stand out</image:title>
      <image:caption>Six key lessons from fine-tuning Gemma for personality: model comparison, data constraints, iterative workflow, local benefits, subjective testing, and small model capabilities</image:caption>
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      <image:title>Blog - "Fine-Tuning Gemma for Personality - Part 7: From PyTorch to Browser - Make it stand out</image:title>
      <image:caption>Web deployment pipeline: PyTorch model to ONNX, quantization to 4-bit, deployment to browser with WebGPU for client-side inference</image:caption>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 6: Testing Personality (Not Just Accuracy) - Make it stand out</image:title>
      <image:caption>Diagram showing personality testing approach: human judgment, interactive chat testing, and the challenge of testing feel vs correctness</image:caption>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 5: Base Models vs Instruction-Tuned - Make it stand out</image:title>
      <image:caption>Base vs instruction-tuned model comparison showing shared issues and marginal differences]</image:caption>
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  <url>
    <loc>https://www.mosaicmeshai.com/blog/fine-tuning-gemma-for-personality-part-4-when-your-model-learns-too-well</loc>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 4: When Your Model Learns Too Well - Make it stand out</image:title>
      <image:caption>The short response problem - training data patterns, observed behavior, and parameter mitigation results</image:caption>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 3: Training on Apple Silicon - Make it stand out</image:title>
      <image:caption>Training performance metrics showing hardware specs, timing, and loss reduction</image:caption>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 3: Training on Apple Silicon - Make it stand out</image:title>
      <image:caption>GPU utilization during training showing sustained high activity throughout the 5-minute training cycle</image:caption>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 2: Building the Training Dataset - Make it stand out</image:title>
      <image:caption>Training Dataset Structure: Components captured in 111 conversation pairs</image:caption>
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      <image:title>Blog - Fine-Tuning Gemma for Personality - Part 1: Why Fine-Tune a 6-Year-Old? - Make it stand out</image:title>
      <image:caption>The Fine-Tuning Goal: Flow diagram showing Gemma 3 models being trained with 111 Bluey conversation examples</image:caption>
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      <image:title>Blog - Removing Friction: Automating Nano Banana Image Workflows - Make it stand out</image:title>
      <image:caption>Workflow automation: Before: 5-step manual workflow. After: Single automated command.</image:caption>
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      <image:title>Blog - Building an Agentic Personal Trainer - Part 9: Lessons Learned - Make it stand out</image:title>
      <image:caption>Key lessons learned: Six key lessons from building a conversational AI coach</image:caption>
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      <image:caption>Agent debugging layers: Four layers of agent debugging: verbose mode, tool validation, scripted conversations, and tool selection analysis</image:caption>
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      <image:title>Blog - Building an Agentic Personal Trainer - Part 7: LLM Provider Abstraction - Make it stand out</image:title>
      <image:caption>LLM provider abstraction: Factory pattern: one environment variable switches between local, Gemini, and Claude</image:caption>
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      <image:caption>Memory architecture: Two-tier memory: session-level BufferMemory plus persistent SQLite for cross-session continuity</image:caption>
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      <image:caption>2-layer deduplication: Two layers of duplicate detection: fuzzy matching and quality scoring</image:caption>
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      <image:caption>Garmin sync flow: Garmin activities flow from watch to API to normalized SQLite records</image:caption>
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      <image:title>Blog - Building an Agentic Personal Trainer - Part 3: The System Prompt - Make it stand out</image:title>
      <image:caption>System prompt structure: Five layers: role, philosophy, domain knowledge, behaviors, and personality</image:caption>
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      <image:title>Blog - Building an Agentic Personal Trainer - Part 2: The Tool System - Make it stand out</image:title>
      <image:caption>The 8 tools: Eight tools across three categories: injuries, workouts, and scheduling</image:caption>
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      <image:title>Blog - Building an Agentic Personal Trainer - Part 1: Architecture and Philosophy - Make it stand out</image:title>
      <image:caption>Architecture overview: LangChain agent executor connecting user, tools, database, and LLM</image:caption>
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      <image:title>Blog - Building a Local Semantic Search Engine - Part 5: Learning by Building - Make it stand out</image:title>
      <image:caption>Series recap: From text to vectors to search—all running locally</image:caption>
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      <image:title>Blog - Building a Local Semantic Search Engine - Part 4: Caching for Speed - Make it stand out</image:title>
      <image:caption>Caching flow: First run generates embeddings; subsequent runs load from cache</image:caption>
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      <image:title>Blog - Building a Local Semantic Search Engine - Part 3: Indexing and Chunking - Make it stand out</image:title>
      <image:caption>Chunking strategy: Chunking with overlap ensures context isn't lost at boundaries</image:caption>
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      <image:caption>Keyword vs semantic search: Semantic search finds related documents even with different words</image:caption>
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      <image:title>Blog - Building a Local Semantic Search Engine - Part 1: What Are Embeddings? - Make it stand out</image:title>
      <image:caption>Semantic space visualization: Similar concepts cluster together in semantic space</image:caption>
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      <image:title>Blog - Building a Local Semantic Search Engine - Part 1: What Are Embeddings? - Make it stand out</image:title>
      <image:caption>Text to embedding flowchart: Text becomes 768 numbers that capture semantic meaning</image:caption>
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      <image:title>Blog - Building an MCP Agentic Stock Trading System - Part 7: MCP Experimentation Lessons - Make it stand out</image:title>
      <image:caption>Experimentation lessons from building with MCP</image:caption>
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      <image:title>Blog - Building an MCP Agentic Stock Trading System - Part 6: Cloud vs Local vs Rules - Make it stand out</image:title>
      <image:caption>The speed, cost, intelligence triangle</image:caption>
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      <image:title>Blog - Building an MCP Agentic Stock Trading System - Part 5: Backtesting All Three Agents - Make it stand out</image:title>
      <image:caption>Three agents, one architecture, three different results</image:caption>
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      <image:title>Blog - Building an MCP Agentic Stock Trading System - Part 4: When Agents Disagree - Make it stand out</image:title>
      <image:caption>Three agents analyzing the same stock data</image:caption>
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      <image:title>Blog - Building an MCP Agentic Stock Trading System - Part 3: The Agentic Loop - Make it stand out</image:title>
      <image:caption>The Agentic Loop</image:caption>
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      <image:title>Blog - Building an MCP Agentic Stock Trading System - Part 2: The MCP Servers and Tools - Make it stand out</image:title>
      <image:caption>Two MCP Servers</image:caption>
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      <image:title>Blog - Adding nano-banana 3 Support to My CLI Wrapper - Make it stand out</image:title>
      <image:caption>CLI tool usage example</image:caption>
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      <image:caption>Original Photo</image:caption>
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      <image:title>Blog - Adding nano-banana 3 Support to My CLI Wrapper - Make it stand out</image:title>
      <image:caption>Edited image using the nano-banana-experiments CLI tool with the following CLI command “nano-banana generate "change the clothing of the people and dog to have them dressed in traditional Japanese clothes" -i ~/Projects/mosaic-mesh-ai-blog/assets/gottschalk-family-original.jpg -o gottschalk-family-nano-banana-3-edited.jpg --model 3 --resolution 1K”</image:caption>
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