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feat(index): add LOCAL AI badge and hero description
Add "LOCAL AI" meta item to hero badges and new paragraph describing Ollama and OpenAI-compatible local model support.
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@@ -25,6 +25,8 @@ const version = pkg.version ?? "";
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<div class="hero-meta">
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<div class="hero-meta">
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<span class="meta-item">RUNS LOCAL</span>
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<span class="meta-item">RUNS LOCAL</span>
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<span class="meta-divider">/</span>
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<span class="meta-divider">/</span>
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<span class="meta-item">LOCAL AI</span>
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<span class="meta-divider">/</span>
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<span class="meta-item">WHOLE PROJECT CONTEXT</span>
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<span class="meta-item">WHOLE PROJECT CONTEXT</span>
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<span class="meta-divider">/</span>
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<span class="meta-divider">/</span>
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<span class="meta-item">NO CLOUD REQUIRED</span>
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<span class="meta-item">NO CLOUD REQUIRED</span>
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@@ -32,6 +34,9 @@ const version = pkg.version ?? "";
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<p class="hero-description">
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<p class="hero-description">
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Zaguán Blade + zcoderd are built for long-running coding sessions. In our 250-turn benchmark, context compression and selective retrieval reduced token cost by ~31% on average, with higher gains in context-heavy runs. It’s not always cheaper in lean sessions — it’s designed to prevent context bloat when history grows.
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Zaguán Blade + zcoderd are built for long-running coding sessions. In our 250-turn benchmark, context compression and selective retrieval reduced token cost by ~31% on average, with higher gains in context-heavy runs. It’s not always cheaper in lean sessions — it’s designed to prevent context bloat when history grows.
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</p>
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</p>
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<p class="hero-description" style="margin-top: var(--space-sm);">
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Connect to local models via Ollama or any OpenAI-compatible endpoint. Your code, your AI, your hardware — fully private and offline capable.
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</p>
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<div class="hero-actions">
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<div class="hero-actions">
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<a href="#download" class="action-primary">
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<a href="#download" class="action-primary">
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<span class="action-text">RUN AI LOCALLY. START HERE.</span>
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<span class="action-text">RUN AI LOCALLY. START HERE.</span>
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