Zhang-986/Stardew-Sage

Stardew-Sage is a niche application of common LLM patterns (RAG and Multimodal Vision) to a specific video game. With only 23 stars and stagnant velocity (0.0/hr), it represents a personal experiment or early prototype rather than a sustainable project. Its defensibility is minimal; the 'moat' consists entirely of the curated game-specific data in its vector store, which could be replicated in hours by scraping the Stardew Valley Wiki. The project faces extreme displacement risk from frontier models (like GPT-4o or Claude 3.5 Sonnet) that already possess high-quality internal knowledge of popular games and have native vision capabilities that can interpret game screenshots without specialized local code. Furthermore, the gaming AI space is rapidly moving toward general-purpose agents (e.g., Google's SIMA) that learn to play games from pixels, making hand-crafted RAG-based assistants for single titles look like a transitional technology. There is no evidence of a community or a unique dataset that would prevent a user from simply asking a general-purpose AI 'What should I plant in Summer?' or 'What does this NPC like?'.