Limitations

While Zentris delivers powerful capabilities, it has certain limitations. These arise from factors such as data sources, sentiment analysis accuracy, market prediction uncertainties, and computational resource requirements. Understanding these constraints helps users set realistic expectations and use the platform effectively. Below, we outline the key limitations.

Limitations

1. Data Sources Limitations:

   • Currently, Zentris supports data from Solana and select Web3 projects, but it plans to expand its coverage to other decentralized platforms in the future.

2. Sentiment Analysis Errors:

   • The accuracy of sentiment analysis is dependent on the quality of social platform data. As market sentiment can fluctuate rapidly, there may be occasional misjudgments, especially during periods of extreme market volatility.

3. Market Prediction Uncertainty:

   • Due to the inherent volatility of the cryptocurrency market, predictions generated by Zentris can be impacted by unforeseen events. While predictions offer valuable insights, they cannot be guaranteed to be 100% accurate.

4. Hallucinations:

   • Like other large language models, Zentris may sometimes produce incorrect or misleading information. Users should critically assess the model's output and verify it against reliable sources.

5. Bias and Fairness:

   • Zentris may reflect biases present in the data it was trained on. Continuous work is necessary to minimize biases and ensure fair and equitable results.

6. Data Limitations:

   • Zentris' knowledge is based on the data it was trained with, which may not always include the most current information, especially given the fast-paced nature of the Solana ecosystem.

7. Computational Resources:

   • Running Zentris, particularly for complex tasks or long sequences, can be resource-intensive, requiring significant computational power.