The Science App allows users to analyze any claim with both supporting and opposing evidence derived from peer-reviewed scientific research. By using AI to search scientific papers, it links users directly to the sources, providing a balanced analysis of evidence strength and scientific consensus. The platform is designed to aid researchers in streamlining their literature review process while also offering the general public access to evidence-based information in an approachable format.

MADDPG-Keras delivers a complete framework for multi-agent reinforcement learning research by implementing the MADDPG algorithm in Keras. It supports continuous action spaces, multiple agents, and standard OpenAI Gym environments. Researchers and developers can configure neural network architectures, training hyperparameters, and reward functions, then launch experiments with built-in logging and model checkpointing to accelerate multi-agent policy learning and benchmarking.