Show HN: Lessons from building a SIMA2 style agent in Roblox (www.frisson-labs.com)

Frisson Labs has shared insights from their development of a SIMA2-style AI agent within the Roblox game ecosystem, specifically using the trending Slime RNG game. The team aims to create engaging AI companions by equipping them with capabilities like screen reading, decision-making, and recovery from obstacles. They adapted the SIMA2 framework to leverage cost-effective resources, employing screenshot inputs and keyboard/mouse controls via the Gemini model for both short-term actions and longer-term reasoning, thereby mimicking human-like play. This work is significant for the AI/ML community as it highlights practical challenges in developing agents that convincingly emulate human behavior in dynamic environments. Though the team achieved some player-like movement with advanced planning and movement skills, the agent still struggled with basic navigation and decision-making. The iterative testing process led to the development of a two-loop system that combines fast action with slower reasoning, illustrating the ongoing complexity in creating AI capable of true understanding and agility. Future developments aim to enhance the agent's world model for better contextual awareness, emphasizing the need for more sophisticated learning mechanisms in AI.