Hey HN! I'm Alexander. I love making stuff that solves my own problems.
If you ever ordered shoes online, you know how tricky sizing is. I personally own shoes ranging from size 11 and up to size 13.
Before Feetlot, the solution was to google "SHOE_NAME sizing reddit" and browse through countless threads on Reddit.
There, people would post something like "If I'm size 12 in shoe A, what size shoe B should I buy?"
Being a programmer, I decided to automate this process. That's how Feetlot was born.
The idea is simple: allow people to enter which shoes in which sizes they wear, then use this data to calculate size differences between shoes.
Example:
User 1 wears shoe A in size 10, shoe B in size 9.5.
User 2 wears shoe A in size 12, is looking to buy shoe B.
Based on User 1 data, we can calculate that there is a -0.5 difference between shoe A and shoe B.
So when User 2 signs up and enters "Shoe A, size 12", we recommend shoe B in size 11.5 to them.
Obviously the real world scenarios involve more shoes.
As of today, 12,152 people signed up and submitted 22,848 sizes resulting in staggering 303,139 unique shoe-to-shoe size differences!
What I love the most about Feetlot is that when people sign up and enter their sizes to get a recommendation, they are actually providing value to other users! I love the "I help you, you help me" model working here.
If you ever ordered shoes online, you know how tricky sizing is. I personally own shoes ranging from size 11 and up to size 13.
Before Feetlot, the solution was to google "SHOE_NAME sizing reddit" and browse through countless threads on Reddit. There, people would post something like "If I'm size 12 in shoe A, what size shoe B should I buy?" Being a programmer, I decided to automate this process. That's how Feetlot was born.
The idea is simple: allow people to enter which shoes in which sizes they wear, then use this data to calculate size differences between shoes.
Example:
User 1 wears shoe A in size 10, shoe B in size 9.5.
User 2 wears shoe A in size 12, is looking to buy shoe B.
Based on User 1 data, we can calculate that there is a -0.5 difference between shoe A and shoe B.
So when User 2 signs up and enters "Shoe A, size 12", we recommend shoe B in size 11.5 to them.
Obviously the real world scenarios involve more shoes.
As of today, 12,152 people signed up and submitted 22,848 sizes resulting in staggering 303,139 unique shoe-to-shoe size differences!
What I love the most about Feetlot is that when people sign up and enter their sizes to get a recommendation, they are actually providing value to other users! I love the "I help you, you help me" model working here.