Random Number
Delivers true random numbers for large language models by leveraging atmospheric noise from random.org.
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Large language models often struggle to generate truly random numbers, exhibiting biases that can impact applications requiring genuine unpredictability. This tool addresses that limitation by acting as a Model Context Protocol (MCP) server, providing access to high-quality random numbers sourced from atmospheric noise via random.org. It helps developers ensure their LLM-driven applications have access to unbiased randomness for critical functions.
Características Principales
- Generates true random numbers
- Operates as an MCP (Model Context Protocol) server
- 4 GitHub stars
- Mitigates LLM bias in random number generation
- Leverages atmospheric noise from random.org
Casos de Uso
- Enhancing the unpredictability of LLM-generated outputs
- Integrating external high-quality random sources into LLM applications
- Supplying unbiased random numbers to large language models
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