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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
🖖 Train an artificial intelligence to play tinder for you
| Date | Stars |
|---|---|
| 2026-07-24 | 566 |
| 2026-07-25 | 566 |
| 2026-07-28 | 566 |
| 2026-07-30 | 566 |
| 2026-08-06 | 566 |
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# Auto-Tinder - Train an AI to swipe tinder for you
Auto-tinder was created to train an API using Tensorflow and Python3 that learns your
interests and automatically plays the tinder swiping-game for you.
In this document, I am going to explain the following steps that were needed to
create auto-tinder:
- analyze the tinder webpage to find out what internal API calls tinder makes, reconstruct the API calls in [Postman](https://www.getpostman.com/) and analyze its content
- Build a api wrapper class in python that uses the tinder api to like/dislike/match etc.
- Download a bunch of images of people nearby
- Write a simple mouse-click classifier to label our images
- Develop a preprocessor that uses the tensorflow object detection API to only cut out the person
in our image
- Retrain inceptionv3, a deep convolutional neural network, to learn on our classified data
- Use the classifier in combination with the tinder API wrapper to play tinder for us
## Step 0: Motivation and disclaimer
Auto tinder is a concept project purely created for fun and educational purposes.
It shall never be abused to harm anybody or spam the platform. The auto-tinder scripts
should not be used with your tinder profile since they surely violate tinders terms of service.
I've written this piece of software mainly out of two reasons:
1. Because I can and it was fun to create :)
2. I wanted to find out whether an AI would actually be able to learn my
preferences in the other sex and be a reliable left-right-swipe partner for me.
3. (Purely fictional reason: I am a lazy person, so why not invest
15 hours to code auto-tinder + 5 hours to label all images to save me a few hours of
actually swiping tinder myself? Sounds like a good deal to me!)
## Step 1: Analyze the tinder API
The first step is to find out how the tinder app communicates to tinders backend server.
Since tinder offers a web version of its portal, this is as easy as going to
tinder.com, opening up chrome devtools and have a quick look at the network protocol.
The content shown in the picture above was from a request to [https://api.gotinder.com/v2/recs/core](https://api.gotinder.com/v2/recs/core) that
is made when the tinder.com landing page is loading. Clearly, tinder has some sort
of internal API that they are using to communicate between the front- and backend.
With analyzing the content of */recs/core*, it becomes clear that this API endpoint returns a list of
user profiles of people nearby.
The data includes (among many other fields), the following data:
```json
{
"meta": {
"status": 200
},
"data": {
"results": [
{
"type": "user",
"user": {
"_id": "4adfwe547s8df64df",
"bio": "19y.",
"birth_date": "1997-17-06T18:21:44.654Z",
"name": "Anna",
"photos": [
{
"id": "879sdfert-lskdföj-8asdf879-987sdflkj",
"crop_info": {
"user": {
"width_pct": 1,
"x_offset_pct": 0,
"height_pct": 0.8,
"y_offset_pct": 0.08975463
},
"algo": {
"width_pct": 0.45674357,
"x_offset_pct": 0.984341657,
"height_pct": 0.234165403,
"y_offset_pct": 0.78902343
},
"processed_by_bullseye": true,
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:372597897f4dbad9, topic:tensorflow