--- title: Why Every AI Model Gets Avalonia Wrong (And How I'm... description: Every AI model gets Avalonia wrong. Not slightly wrong — structurally, fundamentally wrong. Claude, GPT, Qwen — none of them understand how Avalonia layouts... canonical: https://daily.dev/posts/why-every-ai-model-gets-avalonia-wrong-and-how-i-m-fixing-it--obpgvx7ac twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Why Every AI Model Gets Avalonia Wrong (And How I'm Fixing It) | daily.dev og:description: Every AI model gets Avalonia wrong. Not slightly wrong — structurally, fundamentally wrong. Claude, GPT, Qwen — none of them understand how Avalonia layouts... og:url: https://daily.dev/posts/why-every-ai-model-gets-avalonia-wrong-and-how-i-m-fixing-it--obpgvx7ac og:image: https://api.daily.dev/og/posts/OBpGVX7aC.png og:image:alt: Why Every AI Model Gets Avalonia Wrong (And How I'm Fixing It) og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Why Every AI Model Gets Avalonia Wrong (And How I'm Fixing It) **[AngelSix](https://daily.dev/sources/angelsix)** · 30 min read · 0 upvotes · 0 comments ## Summary Every AI model gets Avalonia wrong. Not slightly wrong — structurally, fundamentally wrong. Claude, GPT, Qwen — none of them understand how Avalonia layouts actually work. They've been trained on TypeScript, CSS, and HTML. They have no visual brain for XAML. So I'm building one from scratch. This is the first video in a series documenting my journey to create an open-weight AI model that writes real, production-grade Avalonia code. We start exactly where every training pipeline should: making the model fail on purpose — so we know precisely what to fix. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🧠 WHAT YOU'LL LEARN ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ► Why every current AI model is structurally broken for Avalonia UI ► The synthetic data bootstrapping technique for targeted model training ► AI Mecha — my custom multi-agent harness application and how it works ► The Universal UI Layout Loss framework (inner positioning, finite vs infinite space, state selectors) ► How to intentionally break a model to reveal exactly what to fix ► The full roadmap: basic markdown prompts → RAG → fine-tuning → open-weight model release ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📋 CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00 Why AI Fails at Avalonia 0:39 AI Mecha: The Multi-Agent Workflow App 1:34 From Cloud to Local AI (The Journey) 3:09 The Goal: Training From Scratch 3:46 Making the Model Fail on Purpose 4:49 Synthetic Data Bootstrapping Explained 5:26 Universal UI Layout Loss Framework 7:36 Setting Up the Test Environment 9:06 Testing Llama: First Prompt 12:21 Analysing Llama's Failures 13:34 Refining the Prompt 19:01 Switching to Qwen 22:19 Adding the Compilation Rule 26:00 Iterating on Output Quality 28:26 The Linting Problem 29:31 What Comes Next ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔗 LINKS & RESOURCES ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🌐 AngelSix: https://www.angelsix.com 📂 GitHub Resources: https://github.com/angelsix 📋 Full Series Playlist: https://www.youtube.com/playlist?list=PLrW43fNmjaQUEVov5qPNrLyaBAKDCNcPD ## Full article daily.dev links to this article rather than hosting it. 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