I've trained my neural network to play Tinder
The video is here: https://www.youtube.com/watch?v=Ohs6sgmVNYI This video is a prof of concept. I always wanted to see if I could make my Neural Network play tinder for me. So I gather some pictures, created a fake tinder app (so I don't expose real people) and began the training. For this experiment I'm using scrcpy to control my cellphone and pyautogui alongside my neural network. The script is very simple: Get the image, run it trough a neural network, move the mouse to the output (yes or no) and click. There's a lot of hard coded things in my code, but as I said earlier,…
What it does
In the maker’s words, at launch
The video is here: https://www.youtube.com/watch?v=Ohs6sgmVNYI This video is a prof of concept. I always wanted to see if I could make my Neural Network play tinder for me. So I gather some pictures, created a fake tinder app (so I don't expose real people) and began the training. For this experiment I'm using scrcpy to control my cellphone and pyautogui alongside my neural network. The script is very simple: Get the image, run it trough a neural network, move the mouse to the output (yes or no) and click. There's a lot of hard coded things in my code, but as I said earlier, it is just a prof of concept. The code is pretty much garbage, but here's it if anyone care to look: import json from PIL import Image, ImageChops import numpy as np from Dejavu import Dejavu #this is my neural network (soon on GitHub) import pyautogui import time data = json.loads( open('nn.json','r').read() ) nn = Dejavu() nn.load(data) choices = { 0: (900,565), 1: (1055,565) } pyautogui.moveTo(850, 210) pyautogui.click() # I know there's only 6 pictures on my fake tinder app for i in range(7): img = pyautogui.screenshot().convert('L') img = img.crop( (810,210, 1150, 500) ) img.thumbnail( (36,36) ) arr = np.array(img).reshape(-1) arr = np.pad(arr, (0,36*36-arr.shape[0]), mode='constant') result = nn.predict( arr.tolist() )[0].tolist() result = result.index( max(result) ) pyautogui.moveTo( choices[result] ) pyautogui.click() if i < 6: time.sleep(3)
Does the same job
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In browser PPO training demo, made possible by tinygrad: TinyJit -> WebGPU kernels. Requires WebGPU.
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Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a…
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I’m 15 and self-taught. I'm learning ML from scratch because I want to really understand how things work. I’m not into frameworks. I prefer math, logic, and C++. I implemented a basic MLP that supports different activation and loss functions. It was trained via mini-batch gradient descent. I wrote it from scratch, using no external libraries except Eigen (for linear algebra). I learned how a Neural Network learns (all the math) -- how the forward pass works, and how learning via backpropagation works. How to convert all that math into code. I’ll write a blog soon explaining how MLPs work in…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Launched alongside, August 2019
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Startup Decks▲711View 100s of startup pitch decks or submit your own.
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