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@@ -267,9 +267,6 @@ <h2>Endpoints</h2>
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<strong>⚠️ Development Notice:</strong> The Flash API is currently under active development. All endpoints, parameters, and responses are subject to change and improvement. This documentation will be updated as the API evolves.
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</div>
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<h1>Flash GraphQL API</h1>
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<p>This API provides access to Flash's Bitcoin and Lightning Network payment services for the Caribbean region.</p>
<p>The Flash API documentation implementation uses the Absolute Zero methodology for continuous improvement. This revolutionary approach, based on reinforced self-play reasoning with zero data, ensures our API and its documentation evolve with minimal human intervention.</p>
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<divclass="auth-diagram">
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<imgsrc="images/diagrams/absolute-zero.svg" alt="Absolute Zero Method Diagram" style="max-width: 100%;">
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</div>
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<h4>Core Principles</h4>
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<p>The Absolute Zero methodology operates on three fundamental components:</p>
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<ul>
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<li>
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<strong>Task Proposer (πpropose):</strong> This component autonomously generates tasks optimized for learning and improvement, creating challenges specifically designed to enhance the system's capabilities.
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</li>
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<li>
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<strong>Environment (e):</strong> Provides verifiable feedback and ground truth validation, ensuring all improvements are measured against reliable metrics without relying on external validation.
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</li>
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<li>
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<strong>Solution Generator (πsolve):</strong> Solves the self-generated tasks and learns from outcomes, continuously enhancing its capabilities through reinforced self-learning.
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</li>
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</ul>
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<h4>Implementation Benefits</h4>
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<p>By implementing the Absolute Zero method in our documentation system, we achieve:</p>
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<ul>
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<li><strong>Self-Improving Documentation:</strong> The documentation evolves automatically based on usage patterns and identified pain points</li>
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<li><strong>Zero Training Data Requirements:</strong> No need for extensive manually labeled datasets or examples</li>
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<li><strong>Continuous Enhancement:</strong> The system perpetually refines itself through reinforced feedback loops</li>
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<li><strong>Cross-Domain Learning:</strong> Improvements in one area benefit the entire system through knowledge transfer</li>
<p>For more information on the Absolute Zero methodology, refer to the research paper: <ahref="https://arxiv.org/abs/2505.03335" target="_blank">Absolute Zero: Reinforced Self-play Reasoning with Zero Data</a>.</p>
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