Ember

Ember is a cutting-edge public AI prediction engine that operates on the principle of transparency and accountability. It utilizes three distinct AI models—Claude by Anthropic, Grok by xAI, and Gemini by Google—to independently analyze live Polymarket markets every morning at 7 AM EST. Each model provides its predictions without any collaboration, ensuring that their assessments are based solely on their individual analyses. Divergences of over 10 points from the collective market consensus are flagged as high-conviction signals, providing users with valuable insights into potential mispricings. The entire process is meticulously documented; every prediction is timestamped and publicly available for scrutiny. Ember is ideal for traders, analysts, and anyone interested in improving their betting strategies by leveraging AI-driven insights to make informed decisions. By emphasizing trust through transparency, Ember aims to become the go-to resource for anyone looking to understand market dynamics and enhance their predictive capabilities.

Independent AI Models

Ember operates with three independent AI models—Claude, Grok, and Gemini—that analyze data separately. This approach ensures diverse perspectives and reduces the risk of groupthink, allowing users to benefit from varied insights.

High-Conviction Signals

When any of the AI models diverge from the market consensus by 10 points or more, the prediction is marked as a high-conviction signal. This feature alerts users to potential opportunities in the market that may be overlooked by the crowd.

Real-Time Data Synthesis

Ember synthesizes data from multiple sources, including prediction markets, bookmaker lines, and AI research feeds. This real-time aggregation of information allows for more accurate and timely predictions, enhancing decision-making for users.

Public Accountability

All predictions made by Ember are locked in before outcomes are known, and nothing is edited or deleted after the fact. This commitment to transparency builds trust and allows users to track the performance of the AI models over time.

Improving Betting Strategies

Users can leverage Ember’s high-conviction signals to refine their betting strategies. By identifying discrepancies between AI predictions and market consensus, bettors can make more informed decisions based on reliable data.

Market Analysis for Investors

Investors looking to gain insights into market trends can utilize Ember's comprehensive analysis. The diverse perspectives from three AI models can help identify emerging opportunities or risks in various sectors.

Research and Development Insights

Researchers in AI and market prediction can benefit from Ember's transparent methodology and data. By analyzing the performance and predictions of the AI models, they can gain insights into the effectiveness of different predictive approaches.

Real-Time Competitive Intelligence

Businesses that rely on market forecasts can use Ember to stay ahead of competitors. By accessing real-time predictions and understanding market sentiment, companies can make strategic decisions based on current data.

How does Ember ensure the accuracy of its predictions?

Ember tracks the accuracy of its predictions using Brier scores, which reward both accuracy and confidence. This calibration metric helps users understand the reliability of each model's predictions.

What happens if all three AI models agree on a prediction?

When all three models agree, that consensus is noted but does not generate a divergence signal. However, the collective agreement can still provide valuable insights into market trends.

Can users interact with the predictions in real-time?

Yes, users can access live predictions and signals at 7 AM EST each day. Subscribers receive the information before it is made public, giving them a timing advantage.

Is there a way to track Ember's historical performance?

Yes, Ember maintains a public record of all predictions made over the course of 365 days. This transparency allows users to analyze past performance and assess the reliability of the models over time.