Update modules/ui/ui.py
Browse files- modules/ui/ui.py +0 -29
modules/ui/ui.py
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@@ -752,35 +752,6 @@ def display_videos_and_info(lang_code, landing_t):
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competitions_text = {
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'en': """
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<div class="content-text">
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<div class="highlight-box">
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Competing for <strong>$950K USD</strong> in prizes across multiple prestigious competitions.
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</div>
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<details class="competition-details">
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<summary class="competition-summary">
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<strong>NVIDIA Inception Demo Pitch – GTC 2026</strong> - March 16–19 in San Jose, California
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</summary>
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<div class="competition-content">
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<p>This is a unique opportunity for startups leveraging NVIDIA technologies in innovative ways to showcase their work.
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By completing the form below, you'll be considered to present a live, 5-minute demo or pitch at GTC,
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highlighting your startup's progress and how NVIDIA technology is accelerating your success.</p>
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<p>More info: https://www.nvidia.com/gtc/?ncid=GTC-NV7HIY70L</p>
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</div>
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</details>
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<details class="competition-details">
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<summary class="competition-summary">
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<strong>Kaggle/Google Tunix Hackathon</strong> - Explainable AI Models
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</summary>
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<div class="competition-content">
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<p>Most open-source or open-weight language models can give you an answer. But they typically don't 'show their work'
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- the steps they went through to arrive at that conclusion in a consistent manner. Here, you'll use Tunix,
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Google's new JAX-native library for LLM post-training, to train a model to show its work by laying out a
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reasoning trace before landing on an answer.</p>
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<p>More info: https://kaggle.com/competitions/google-tunix-hackathon</p>
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</div>
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</details>
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<details class="competition-details">
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<summary class="competition-summary">
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competitions_text = {
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'en': """
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<details class="competition-details">
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<summary class="competition-summary">
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