ARChess vs RL Playground

Something simple and fundamental, like classic chess game, gets a real AR boost in this app. Now all chess fans can shift from their regular boards and enjoy the game on the phone. With super cool 3D design, it’s now more realistic than ever. For starters, the functionality is limited to hot-seat game mode, i.e. two players play on 1 device or 1 player conduct a battle with the machine. We decided to leave something from a classic indoor game, at least players can discuss the game in real or beat a computer genius. In the future versions, it will be possible to support multi device mode to play the game by two players remotely on one chess board. Furthermore, chess tournaments, results saving ranking table are in the plan. We’ve used the ARKit in surface recognition (any contrasting horizontal surface can be used for chess board projection). Besides, we’ve taken into account the main complaints and wishes on the chess app market. Unlike the majority on the App Store, our app allows: - taking back moves (if you accidentally drop the piece on the wrong square, now there is an option to correct it) - automatically turning board around when playing - repositioning a board during the game Chess is our favorite office game. This ARChess app gives a new life to the classic game, moving it to the ‘new dimension’.

RL Playground is a one-of-a-kind interactive experience to explore reinforcement learning through play Agents don’t fake learning here — they actually learn, locally, on your device. No cloud, no servers, no precomputed simulations. You can watch learning happen live, episode after episode. Each level is based on a concrete scenario: a drone learning when to jump over a gap, an autonomous car learning to avoid pedestrians, a Duck Hunt–inspired challenge focused on tracking and decision-making. You can tweak learning parameters, train the agent, then test it and observe how its behavior evolves over time. RL Playground is made for: people who want to understand AI hands-on, curious minds, students, developers, and players looking for experimental, meaningful games. No internet required. No prior ML knowledge needed. Just learning by doing.