Block Survivor

Block Survivor is a top-down survival game that uses AI to dynamically adjust difficulty in real-time. Unlike traditional games with static difficulty curves, Block Survivor creates a personalized gaming experience that adapts to each player's skill level every 30 seconds using decentralized AI.

The Problem- Most games struggle with difficulty balance - beginners get frustrated while experts get bored. Static difficulty curves can't serve all players effectively.

Our Solution- Block Survivor continuously monitors player metrics (reaction time, dodge rate, movement patterns) and sends them to an AI system running on decentralized infrastructure. The AI analyzes performance and generates new game configurations including:

1. Dynamic Terrain: Smooth (fast but slippery), Sticky (slow, hard to escape), or Rugged (balanced)
2. Boss Scaling: Speed, health, damage, and shield adjustments based on player skill

Why AI? Unlike simple algorithmic approaches, our LLM understands context and patterns. It can identify if a player is "aggressive but reckless" vs "overly defensive" and respond with appropriate terrain and enemy configurations to maintain optimal challenge.

Developer Appeal- The game features a sketch/hand-drawn aesthetic with developer humor - enemies are "code bugs" like Null Pointer Exceptions and Stack Overflow errors. Achievement names include "99 Bugs in the Code" and "Works on My Machine."

Impact- Block Survivor demonstrates practical applications of decentralized AI in gaming, making games more accessible while showcasing how blockchain and AI can enhance traditional gaming experiences.

Block Survivor combines three key technologies to create real-time AI-powered difficulty adjustment:

Core Architecture-

1. Unity Game Client: Collects player metrics and applies AI-generated configurations
2. Express.js API on Fluence VM: Manages game sessions and processes player data
3. 0G Network: Provides decentralized LLM inference for intelligent difficulty analysis

The AI System- We use 0G Network's decentralized LLM infrastructure to analyze player behavior. The system sends structured prompts containing player performance data, current game state, and terrain effect descriptions. The AI responds with new game configurations that maintain optimal challenge.

Key Technical Challenges-

• Real-time Performance: Achieved sub-20 second response times for 30-second game rounds
• Fallback Systems: Graceful degradation when AI is unavailable - game continues with previous configurations
• Cross-network Integration: Managing state across Unity client, Fluence VM, and 0G Network
• LLM Response Parsing: Robust extraction of JSON configurations from AI responses

Decentralized Deployment- The API runs on Fluence VM for truly decentralized hosting, while 0G Network provides the AI computation. This eliminates single points of failure and demonstrates practical use of decentralized infrastructure for real-time applications.

Notable Solutions- We solved several unique challenges including handling BigInt serialization from blockchain interactions, managing single-use authentication headers for 0G Network, and creating comprehensive testing frameworks to validate AI behavior across different player skill levels.

Why This Stack Works- Fluence VM provides decentralized hosting without compromising performance, while 0G Network offers actually usable real-time AI. Combined with Unity's cross-platform capabilities, this creates a new class of AI-powered games that weren't possible before these decentralized technologies matured.