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This commit introduces the foundational specification for Lucidia v1.0 - a set of 100 working example programs that DEFINE the language through demonstration rather than formal grammar. Key Philosophy: - Examples ARE the spec (not documentation OF the spec) - AI systems learn by reading all 100 examples and extracting patterns - Humans learn by working through examples sequentially - No feature exists unless demonstrated in these examples Structure: - 001-010: Fundamentals (hello world → functions) - 011-020: Data & Collections (lists, maps, sets) - 021-030: Control Flow (if, loops, pattern matching) - 031-040: Functions & Composition (map, filter, reduce, closures) - 041-050: UI Basics (forms, inputs, validation) - 051-060: Reactive Programming (state, watchers, events) - 061-070: Consent & Privacy (permission system - CORE DIFFERENTIATOR) - 071-080: Storage & Sync (local-first, cloud-optional) - 081-090: AI Integration (intent → code, learning user style) - 091-100: Complete Applications (todo, notes, chat, e-commerce) Core Language Features Demonstrated: ✓ Intent over ceremony (write WHAT, not HOW) ✓ Consent as syntax (ask permission for: resource) ✓ Local-first storage (store locally, sync to cloud optional) ✓ AI-collaborative (### Intent comments become code) ✓ Reactive by default (state, watch, computed) ✓ Zero setup (runs in browser via WASM) ✓ Multi-paradigm (functional, OOP, reactive, agent-based) ✓ Gradual complexity (hello world → production apps) Files Created: - README.md - Learning philosophy and path - INDEX.md - Complete reference table - 001-100.lucidia - All example programs Total: 102 files, ~3,500+ lines of example code Why This Matters: This is not just documentation. This IS Lucidia. Every parser, compiler, AI assistant, and developer tool will be trained on these examples. They are the permanent, immutable foundation of the language. Next Steps: 1. Build parser that learns from these examples 2. Train AI to recognize and generate Lucidia patterns 3. Create browser playground with these as gallery 4. Use for academic paper and conference presentations Designed by: Cece (Principal Language & Runtime Architect) For: BlackRoad Operating System / Lucidia Programming Language Status: Complete foundation for implementation
69 lines
1.7 KiB
Plaintext
69 lines
1.7 KiB
Plaintext
# 090: AI Learning User Style
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# AI adapts to your coding preferences
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# After writing code for a while, AI learns your style
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# You prefer functional programming:
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numbers = [1, 2, 3, 4, 5]
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doubled = numbers.map(x => x * 2)
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evens = numbers.filter(x => x % 2 == 0)
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total = numbers.reduce((sum, x) => sum + x, 0)
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# AI notices and suggests functional style for new code:
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### Intent: Filter active users and get their emails
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# AI suggests (matching your style):
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# active_emails = users
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# .filter(user => user.active)
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# .map(user => user.email)
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# Instead of imperative:
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# active_emails = []
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# for user in users:
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# if user.active:
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# active_emails.append(user.email)
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# You prefer verbose variable names:
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user_email_address = "alex@example.com"
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is_email_verified = false
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total_purchase_amount = 0
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# AI learns and suggests:
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### Intent: Store user's phone number
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# Suggested variable name: user_phone_number (not just "phone")
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# You always add error handling:
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fetch_user_data(id):
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try:
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return fetch "/api/users/{id}"
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catch error:
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show "Error loading user"
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return null
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# AI learns pattern and auto-adds error handling to suggestions:
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### Intent: Fetch product details
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# AI suggests (with error handling, matching your style):
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# fetch_product(id):
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# try:
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# return fetch "/api/products/{id}"
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# catch error:
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# show "Error loading product"
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# return null
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# Style preferences stored locally
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ai.get_learned_preferences()
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# Returns:
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# {
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# style: "functional",
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# naming: "verbose_snake_case",
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# error_handling: "always_try_catch",
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# comments: "minimal",
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# max_line_length: 80
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# }
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# You can override per-project
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ai.set_preference("style", "object_oriented") # For this project only
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# AI becomes YOUR collaborator, not a generic one
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