Engineering a Solution for Impossible Requirements
Description
A screenshot of a social media thread that starts with a tweet from user Abbie (@AbbieEvansXO). The tweet presents a hypothetical, absurd requirement: '[Old west saloon owner]: make it so the floorboards don't creak when regular patrons walk in but do creak when a mysterious stranger walks in'. This is followed by the confused response, 'Carpenter: ...what'. Below the tweet, a Tumblr user named 'roboticsappreciationsociety' provides a clever, algorithmic solution: 'What if you intentionally put in one squeaky floorboard and tell your regulars that if they squeak it, their first drink costs 10% extra, then only new people who don't know the rule will step on it and familiars will avoid it.' A final comment from 'onlymollygibson' marvels, 'Did... did you just write an algorithm for human behavior? Because that is exactly what I would expect from a robot appreciation Tumblr.' The meme resonates deeply with software engineers, as the saloon owner's request is a perfect metaphor for the vague, context-dependent, and seemingly impossible requirements they often receive from clients or stakeholders. The proposed solution is celebrated as a prime example of computational thinking applied to a social problem - defining a system with clear rules and incentives to manipulate user behavior to achieve a specific outcome
Comments
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The product owner's user story: 'As a user, I want the system to read my mind.' The engineer's implementation: a single, unlabeled button that, when clicked, adds a random item to the cart and charges the credit card. It's not what they wanted, but it's what they described
Some call it a creaky plank; AWS would call it an event-driven onboarding pipeline and charge you per stranger
When you realize the Old West saloon owner invented user segmentation, A/B testing, and dynamic pricing 150 years before we started calling it "data-driven decision making" and charging enterprises $500k for the same insight
This is essentially implementing a stateful authentication system where the 'squeaky floorboard' acts as a honeypot for unauthorized users. The regulars have the shared secret (knowledge of the penalty), creating a zero-trust architecture where behavior, not credentials, determines access level. It's OAuth for saloons - except instead of tokens, you're trading 10% of your whiskey budget for stepping on the wrong board. The real genius? The system is self-documenting through economic incentives, which is more than we can say for most legacy codebases
Who needs RLHF when a 10% drink upcharge trains humans faster than any LLM?
Stakeholder asks for “silent to regulars, loud to unknowns” - translation: perfect anomaly detection with no training data - so we shipped v1 with a single squeaky feature flag and a 10% surcharge: RBAC on wood
Old West product spec: implement anomaly detection via a squeaky-floor honeypot with incentive‑tuned thresholds - aka a human‑in‑the‑loop IDS that nails cold‑start detection