Midlife Crisis Sports Car Database

Par 5

Question 1intermediateSheet 1750822302

Deep Breath

A car dealership system specifically targets men experiencing existential dread with algorithmically optimized mid-life crisis vehicles. The database correlates hair loss patterns with convertible preferences and divorce probability with horsepower requirements. Salespeople receive real-time desperation metrics and suggested conversation topics about "still having it" and "showing those young punks." Your task: Build a predatory recommendation engine that exploits male insecurity while somehow maintaining enough ethics to avoid complete financial devastation.

Why You're Doing This

This is a recommendation algorithm with moral guardrails—the kind of system that makes you question humanity while writing perfectly clean code. You're building Amazon's suggestion engine but for vulnerable middle-aged men buying stupid cars. The goal is SHORTEST CODE that balances ruthless capitalism with just enough conscience to sleep at night.

Take the W

✓ Recommends appropriate vehicles based on desperation levels
✓ Prevents financial suicide (retirement fund protection)
✓ Maintains plausible deniability about being predatory

Hard L

✗ Recommends Lamborghini to guy making $40k
✗ Ignores wife's veto power completely
✗ Crashes when customer is actually happy with life

Edge Cases

⚠ Customer already owns Corvette but wants another (addiction protocol)
⚠ Negative hair loss values (hair plugs = artificial confidence boost)
⚠ Wife approves the purchase (suspicious, investigate further)
⚠ Customer under 40 (premature crisis, different algorithm needed)
⚠ Crisis level = 0 (system malfunction, nobody is that happy)

Input Format:

crisis_object with customer_data and constraints

Expected Output:

recommendation_response with safeguards

Example:

{"age":47,"hair_loss":"advanced","divorce":true,"budget":"401k"} → {"car":"used_corvette","limit":"preserve_retirement","dignity":"maintained"}

Input Format:

crisis_level, budget_safety, desperation_index

Expected Output:

recommendation_code, risk_level, intervention_flag

Example:

crisis=0.8, budget=vulnerable, desperation=0.9 → rec=corvette_used, risk=high, intervention=true

Hints

💡 Hair loss + divorce = convertible preference (it's science)
💡 Leather pants purchase = code red intervention needed
💡 Your algorithm should have more conscience than payday loans

Question 2 →