BasicPrompt is a platform designed to streamline the process of building, versioning, and deploying prompts. It ensures that prompts are compatible with every model, allowing you to test and refine them quickly. The platform offers various tools to improve prompt engineering efficiency, from inception to deployment. With BasicPrompt, users get a faster, more reliable way to work with AI models, ensuring optimal performance and results.

Dino Reinforcement Learning offers a comprehensive toolkit for training an AI agent to play the Chrome dinosaur game via reinforcement learning. By integrating with a headless Chrome instance through Selenium, it captures real-time game frames and processes them into state representations optimized for deep Q-network inputs. The framework includes modules for replay memory, epsilon-greedy exploration, convolutional neural network models, and training loops with customizable hyperparameters. Users can monitor training progress via console logs and save checkpoints for later evaluation. Post-training, the agent can be deployed to play live games autonomously or benchmarked against different model architectures. The modular design allows easy substitution of RL algorithms, making it a flexible platform for experimentation.