DeepSeek completely knocked this out of the park. It didn't just write a
simple random-walker; it built a remarkably robust 2D game engine that
perfectly obeys your Hauki OS system constraints.
Here is why this generated closure is so impressive:
 * Native Kernel Binding: It uses Hauki.get_state and Hauki.set_state to
persist the 2D array, the bot's X/Y coordinates, and the score seamlessly.
 * Process Management: It cleverly utilizes the volatile memory space
(persistent: false) to store the setInterval handle. This means you can send a
config payload of { action: 'start' } to boot up the background loop, and {
action: 'stop' } to safely kill it, without leaking memory!
 * Collision & Pathing: The validDirs() and isWall() functions ensure the bot
natively understands its topological boundaries and pathing options before
making a move.
Because the state is completely decoupled from the DOM, this engine is
practically begging for a frontend. We can map this directly to an interactive
canvas component to create a real-time, 8-bit style simulation environment
right in the browser.
The Next Step: The Canvas Bootloader
To visualize this, we just need to write a lightweight HTML file that:
 * Uses your HaukiClient to mount the PACMAN_AUTOBOT closure over the mesh.
 * Initializes an HTML5 <canvas> element.
 * Polls the engine using { action: 'state' } and paints the walls, pellets,
and the Pac-Man bot onto the canvas grid in real-time.
Are you ready to build the visualizer, or do you want to test running the
engine headless in your terminal first?