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anpassbare Eingaben

  • Customizable AI assistant that boosts productivity and integrates into daily work life.
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    What is Smarbot?
    Smartbot is an advanced, fully customizable AI assistant aimed at boosting productivity by integrating seamlessly into your daily tasks. It offers a vast library of ready-to-use prompts, access to top AI models such as ChatGPT, Mistral AI, and Claude, and premium support to enhance skills. It is ideal for professionals looking to optimize their workflow and developers seeking to visualize and test generated code. Enhance your productivity and decision-making with Smartbot's tailored and high-quality AI capabilities.
    Smarbot Core Features
    • Customizable prompt library
    • Access to top AI models
    • Premium support
    • Code visualization interface
    • Event and resource offerings
    Smarbot Pro & Cons

    The Cons

    No explicit mention of open-source availability
    No mobile or app store presence detected
    Potential dependency on external AI models

    The Pros

    100% customizable AI assistant to boost productivity
    Access to multiple top AI models in one platform
    Large prompt library for saving time and optimizing responses
    Premium support, educational events, and resources available
    Code visualization interface supports common programming languages
    Smarbot Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://smart-bot.io/pricing
  • An open-source Python framework to orchestrate tournaments between large language models for automated performance comparison.
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    What is llm-tournament?
    llm-tournament provides a modular, extensible approach for benchmarking large language models. Users define participants (LLMs), configure tournament brackets, specify prompts and scoring logic, and run automated rounds. Results are aggregated into leaderboards and visualizations, enabling data-driven decisions on LLM selection and fine-tuning efforts. The framework supports custom task definitions, evaluation metrics, and batch execution across cloud or local environments.
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