Ember

Ember is a cutting-edge public AI prediction engine that operates on a fundamental principle: trust in AI comes from transparency. Designed for individuals and organizations seeking reliable predictive analytics, Ember employs three distinct AI models—Claude from Anthropic, Grok from xAI, and Gemini from Google—to independently assess live Polymarket markets every day at 7 AM EST. These models do not consult each other, ensuring that their predictions are based solely on their unique analyses. When a model's prediction diverges from the crowd by 10 or more points, it signals a potential high-conviction opportunity. This innovative approach not only tracks accuracy using Brier scores but also emphasizes accountability by documenting every prediction and the outcomes. Ember democratizes access to powerful AI insights, making it an essential tool for bettors, analysts, and anyone interested in data-driven decision-making.

Independent AI Models

Ember utilizes three independent AI models—Claude, Grok, and Gemini—each providing unique perspectives based on their specialized training. This independence ensures diverse opinions and reduces the risk of collective biases that may skew predictions.

High-Conviction Signals

When the predictions of Ember's models diverge from the crowd by 10 or more points, these instances are flagged as high-conviction signals. This feature helps users identify potential betting opportunities that may indicate the crowd's consensus is misaligned with expert predictions.

Timestamped Calls

Every prediction made by Ember is timestamped before the outcome is known, ensuring transparency and accountability. This feature reassures users that all calls are locked in and unaltered, enhancing trust in the system.

Comprehensive Data Synthesis

Before each prediction, Ember synthesizes data from 20 different sources, including prediction markets, bookmaker lines, and AI research feeds. This comprehensive data integration provides a well-rounded foundation for making informed predictions.

Sports Betting

Ember is invaluable for sports bettors looking for an edge in their wagers. By analyzing real-time odds and predictions, bettors can identify discrepancies between expert AI models and public sentiment, enhancing their chances of making profitable bets.

Market Predictions

Businesses and analysts can leverage Ember's capabilities to forecast market trends and consumer behavior. The high-conviction signals can guide strategic decisions, helping organizations stay ahead of the competition.

Political Forecasting

Political analysts and enthusiasts can utilize Ember to gauge public sentiment and predict election outcomes. By examining the divergence in predictions from the AI models, users can gain insights into potential shifts in voter behavior.

Investment Strategies

Investors can use Ember to inform their investment strategies by analyzing the predictions related to emerging technologies and products. By understanding where the crowd's opinion diverges from expert assessments, investors can make more informed decisions.

How does Ember ensure the accuracy of its predictions?

Ember tracks the accuracy of its predictions using Brier scores, which measure both the accuracy and confidence of each model's predictions. This systematic approach allows users to gauge the reliability of the AI's forecasts over time.

What happens when the AI models agree?

When all three AI models agree on a prediction, it is noted but does not trigger a high-conviction signal. The goal of Ember is to highlight instances of divergence, which are more indicative of potential betting opportunities.

Can I access Ember's predictions in real-time?

Yes, subscribers to Ember receive access to predictions and high-conviction signals at 7 AM EST every day, before they are released to the public. This timing gives subscribers a strategic advantage in the market.

What types of data does Ember analyze?

Ember analyzes a wide array of data, including live prediction markets, bookmaker lines, AI research feeds, and real-time sentiment from various sources. This extensive data synthesis helps ensure that predictions are well-informed and relevant.