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Welcome to Prism

Prism is a programming language where uncertainty is a first-class citizen. In a world where AI responses, sensor readings, and complex computations carry inherent uncertainty, Prism makes this uncertainty explicit and manageable.

Why Prism?​

Traditional programming languages treat all values as certain. But in reality:

  • AI/LLM responses have varying confidence levels
  • Sensor readings contain noise and measurement errors
  • Complex calculations propagate uncertainty
  • Business decisions often depend on confidence thresholds

Prism solves this by making uncertainty a core language feature.

Key Features​

🎯 Confidence Values​

Attach confidence levels to any value:

temperature = 72.5 ~> 0.95  // 72.5°F with 95% confidence
analysis = llm("Is this secure?") ~> 0.8

🔄 Uncertainty Propagation​

Confidence automatically flows through calculations:

avg_temp = (temp1 ~> 0.9 + temp2 ~> 0.85) / 2
// Result carries combined uncertainty

🌊 Confidence-Based Control Flow​

Make decisions based on confidence levels:

uncertain if (analysis ~> 0.7) {
  high { deploy_to_production() }
  medium { request_human_review() }
  low { reject_with_explanation() }
}

🤖 Built-in AI Integration​

Native LLM support with automatic confidence extraction:

response = llm("Analyze this code for vulnerabilities")
confidence = <~ response  // Extract confidence
decision = response ~@> "approve"  // Threshold gate

Getting Started​

Ready to start programming with uncertainty?

  1. Install Prism - Get the CLI and core packages
  2. Set up your editor - Install VS Code extension for syntax highlighting
  3. Write your first program - Learn the basics
  4. Explore the REPL - Experiment interactively

Quick Install​

# Install CLI globally
npm install -g @prism-lang/cli

# Install VS Code extension
curl -L https://github.com/HaruHunab1320/Prism-TS/releases/download/v0.1.0/prism-lang-0.1.0.vsix -o prism-lang.vsix
code --install-extension prism-lang.vsix
rm prism-lang.vsix

# Start coding!
prism

Use Cases​

Prism excels in domains where uncertainty matters:

  • AI/ML Applications: Handle LLM responses with explicit confidence
  • IoT & Sensors: Manage noisy sensor data with uncertainty bounds
  • Financial Systems: Make risk-aware decisions with confidence thresholds
  • Security Analysis: Evaluate threats with uncertainty quantification
  • Scientific Computing: Propagate measurement errors through calculations

Community & Support​

Join our community and help shape the future of uncertainty-aware programming!