Storm Sniffer vs ExploreRL

Storm Sniffer lets you capture and inspect HTTP / HTTPS requests &responses on your iOS device. It's easy to debug your app’s networking issues without a computer. * Features Packet capture: capture http / HTTPS traffic from iOS devices using local VPN. View http / HTTPS requests and responses in text form, and support HTML, JS, CSS formatting preview; Rewrite: modify http / HTTPS requests and responses in real time. Including URL redirection, custom header, local / remote file mock, etc; Replay: Perform arbitrary HTTP requests with cron support; Scripts: Use local/remote Javascript for more scalability. * How it works Storm Sniffer creates a local VPN on your device, then forwarding all traffic to a Local Man-in-The-Middle Server. As long as the VPN is active, Storm Sniffer can capture all HTTP / HTTPS traffic over Wi-Fi or Cellular Data. * About the sensitive data Storm Sniffer's Root Certificate is a self-signed certificate that is generated in your device. All HTTP / HTTPS requests & responses are captured and stored on your devices WITHOUT ANY REMOTE SERVERS involved. * About Subscription After subscription, you can unlock all advanced functions. Currently, there are three kinds of auto-renewable subscription: Annual $17.99、Quarterly $3.99、 Monthly $1.99 * Terms of Use https://sniffer.x-storm.com/terms/ * Privacy Policy https://sniffer.x-storm.com/privacy/

Train, visualize, and experiment with Reinforcement Learning algorithms entirely on-device with ExploreRL. Built with MLX, ExploreRL provides an interactive sandbox to test various environments and algorithms with different hyperparameters to observe how RL agents learn through trial and error. LEARN BY DOING Train agents on classic adapted environments and observe learning in real time. Watch values grow, track reward curves, and develop intuition for how different algorithms and hyperparameters affect performance. Read and learn about reinforcement learning concepts in the documentation. ALGORITHMS Four common implementations: - Q-Learning - SARSA - Deep Q-Network (DQN) for discrete action spaces - Soft Actor-Critic (SAC) for continuous control INCLUDED ENVIRONMENTS Adapted from classic standards: Toy Text: Frozen Lake, Blackjack, Taxi, Cliff Walking Classic Control: CartPole, Mountain Car, Mountain Car Continuous, Acrobot, Pendulum Box2D: Lunar Lander, Lunar Lander Continuous, Car Racing, Car Racing Discrete FEATURES - Environment-specific settings - Real-time training metrics and reward visualization - Configurable hyperparameters: learning rate, discount factor, epsilon decay, batch size - Agent library with save, load, duplicate, and export functionality - Evaluation mode to test trained agents without further training - Educational documentation covering RL concepts, neural networks, and algorithms - Custom seed for reproducibility Note: Training performance will vary by device. Devices with newer hardware and more memory will have a better experience.