Hey all 👋 I've just open-sourced AISpec - the first AI-native specification language that shifts focus from implementation to intent through systematic solution space reduction. Instead of hoping AI generates correct outputs and verifying afterward, AISpec lets you define spaces within which all outputs must be correct. Early tests show 30% less debugging time on generated code. Core Format:

Feature: Name {
What:
- "Clear action items"
- "Each one executable"
Boundaries:
- "Performance limits"
- "Resource constraints"
- "Business rules"
Success:
- "Measurable outcomes"
- "Clear metrics"
- "Expected behavior"
}

GitHub: https://github.com/cbora/aispec Framework Doc: https://chrisbora.substack.com/p/wbs-framework As a bonus, I have included how the Framework makes chatbots more reliable by systematically building understanding before responding, eliminating hallucinations through progressive solution space reduction. Would love feedback from those working with LLMs in production. The goal is to make AI outputs reliable by design rather than hope.