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Products that do what I've trained my neural network to play Tinder does
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,…
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Before the pandemic, my tiny startup was doing quite well selling Edge AI systems, based on our own lightweight AI inference engine, with object detection and face recognition for smart city and smart retail & food service applications. When the real world shut down, there was suddenly nothing to monitor on streets and in restaurants, so I set out to try and evolve our real time face recognition system into a video codec for high quality face-to-face online interactions, as I was not satisfied with the quality of Zoom and friends. I got it to work, and the first release for IOS was just…
2022 · vertigo.ai
- 2IB
2024 · graphgame.sabrina.dev
- 3WA
In browser PPO training demo, made possible by tinygrad: TinyJit -> WebGPU kernels. Requires WebGPU.
May 2026 · ppo.gradexp.xyz
- 4IU
Hi Hacker News, This is definitely out of my comfort zone. I just wanted to show you guys because I'm super proud of it. It's a 100% faithful recreation based off of the schematics, patents, and ROMs that were found online. So please watch the video and tell me what you think https://youtu.be/auOlZXI1VxA The reason why I think this is relevant is because I've been a programmer for 25 years and AI scares the shit out of me. I'm not a programmer anymore. I'm something else now. I don't know what it is but it's multi-disciplinary, and it doesn't involve writing code myself--for…
Jan 2026
- 5IC
I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.
2024 · bell-boy.github.io
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Couldn't resist when I saw Tinder for the other day! Thanks https://twitter.com/DasSurma for making the source code for Tinder for Bananas available!
2022 · tinder4cats.com
- 8IB
We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…
2025 · tinytpu.com
- 9ST
2022 · github.com
- 10NA
I built over the last two years a human-like neural network chess engine that tries to predict your rating from a single game. It automatically adapts to your play and tries to play like a human at your level would play, giving you a balanced game. At the core I’m using an AlphaZero / Leela Chess Zero style neural network that I have trained on 1 billion human games from the lichess.org open database. Around this network I have built a chess engine in Rust with algorithms that use the outputs from the NN to produce human-like moves at a given rating from beginner to world champion, as…
2022 · noctie.ai
- 11W1
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…
Jul 2026 · con-dog.github.io
- 12IW
I have been interested in neural nets since the 90's. I've done quite a bit of reading, but never gotten around to writing code. I used Gemini in place of Wikipedia to fill in the gaps of my knowledge. The coolest part of this was learning about dual numbers. You can see in early commits that I did not yet know about auto-diff; I was thinking I'd have to integrate a CAS library or something. Now, I'm off to play with TensorFlow.
Mar 2026 · github.com
- 13IM
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…
2025 · github.com
- 14IB
I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com
- 15TB
2017 · github.com
- 16IW
2017 · github.com
- 17MA
I made this tool to get some better intuition on how neural networks/backpropagation worked, but I'm really unsure what to do with it now, so I've put it up on github, and I wrote a little primer on backprop and neural networks to showcase it. Really curious to hear any thoughts you might have, or anything I got wrong in the write up!
2024 · github.com
- 18TA
Hello HN, I am Brian Cardinale, a penetration tester and security researcher at SecureCoders. We have been performing more and more AI based security assessments. We were presented a unique challenge of testing a system where the only interface was voice based, and as much as I like talking on the phone , we decided to create a test harness to facilitate the actual testing in a more systematic way. The technical test harness was the easy part, though. Creating test goals and attack strategies to help facilitate repeated and comprehensive testing became the real challenge. As such, we have…
Feb 2026 · redcaller.com
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2020 · github.com
- 20CF
Hey HN! We built the Cursor for everything: just press Cmd-K anywhere, and it helps you write or edit text. Use it to push back on your coworkers' code commit comments, fill out forms, or even get more dates on Tinder! Features: - Works on all apps and browsers - Add your own system prompts, or per-app system prompts - Customizable prompt shortcuts (like QuillBot) and per-app - MCP support, tool calls coming soon - Windows coming soon
2025 · hovergpt.ai
- 21TS
Hello Hacker News community! I'm currently working in financial risk management within the banking sector, and I began my career as a Data Science specialist. For quite some time, my friend and I have been developing a small pet project just for fun. This tool has repeatedly helped us save time when testing various hypotheses and machine learning models. The core idea is to combine different scripts—created in various programming languages and virtual environments—within a minimalist graphical interface. Whether you're building models, running a local neural network, or sending requests to…
2024
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- 23IB
I've been experimenting with ways to increase AI adoption for non-technical people. Basically, all companies are pushing for AI because it's all over the news and they feel left behind but most people have no clue where to start. I think 90% of people (ie non coders) are sufficiently well served by using cowork instead of claude code or something similar. If we can get people from sales, customer support, marketing, etc to collaborate with skills and cowork to form a company brain, I think it's gold. So I think there's opportunity for the community to share skills that work well for 1000s of…
Jun 2026 · claudinho.xyz
- 24AA
Hey HN, I'm an AI enthusiast and I am launching apps that use ML to solve problems that we all have. I realized that there are a lot of deepfake faces on youtube and social media, so I thought it would be useful (and fun!) to have a tool that can bust those AI faces. So I built DeeFace for 2 reasons: 1. Check if the face you're looking at is real or not 2. For fun! It's something that I from half a year ago would look at and aspire to build someday. I think it's soemthing anyone who's just starting out with ML would appreciate. I hope this tool is fun for you to use as it was for me to…
2024 · deefaces.com
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