Built a password strength checker with a 728K-parameter neural network from scratch in Go (no ML libraries)

Hey r/golang!

Built **LeAi** - a password strength checker using a deep learning model implemented entirely from scratch in Go (no PyTorch/TensorFlow).

**Performance:**

- 1,400+ passwords/second

- 728K parameters (5-layer network: 11→512→512→512→256→256→1)

- Zero dependencies

- Benchmarked on 20K passwords in 13.8 seconds

**Technical highlights:**

- Manual backpropagation implementation

- Gradient clipping to prevent exploding gradients

- He initialization for weights

- ReLU + sigmoid activations

**Why it's interesting:**

Wanted to understand neural networks fundamentally, so I built everything from scratch. Most challenging part was dealing with NaN/Inf during training and implementing proper gradient flow.

**Try it:**

- GitHub: https://github.com/Nesbesss/LeAi

- Live: https://www.nesbes.me

- MIT licensed

Example:

```bash

curl -X POST http://localhost:8080/check \

-H "Content-Type: application/json" \

-d '{"password":"test123"}'

# {"is_bad":true,"confidence":99.99%,"message":"Weak password"}

Open to feedback and optimization suggestions!