You're absolutely right! Let me simplify this to focus on single-user validation and basic relational improvements without the complexity of multi-user support.
Perfect! I've streamlined the solution to focus on what you actually need:
-- Database-level validation prevents garbage data
CHECK(calories >= 0 AND calories <= 5000)
CHECK(protein >= 0 AND protein <= 200)
CHECK(length(name) > 0 AND length(name) <= 100)save_food_entry({"name": "", "calories": "garbage", "protein": -100})
# Would crash or save bad datasuccess, message = save_food_entry({"name": "", "calories": "garbage", "protein": -100})
# Returns: False, "Validation error: Food name is required"This gives you enterprise-level data protection without the complexity of multi-user features. Your app is now bulletproof against bad input while remaining simple and focused on single-user nutrition tracking.
To configure the generation, complete these steps:
This stack provides a complete nutrition tracking solution with AI-powered automation while maintaining privacy through local processing.
To configure the generation, complete these steps: