Block Blast Solver is a sophisticated AI-powered tool that offers immediate, optimal solutions for the Block Blast game. With advanced neural network-based recognition, dynamic chain reaction analysis, and predictive scoring systems, it identifies the best moves for any game state. Players simply upload a screenshot of their game board, and the solver analyzes it within seconds to provide precise steps to enhance their performance, making gaming more enjoyable and less frustrating.

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.