Projects
This project trains a generative adversarial network on MNIST, then exports the trained generator weights so the portfolio can reconstruct the model in the browser.
The browser demo runs a fully connected generator that maps a 128-dimensional noise vector through linear layers with batch normalization and LeakyReLU activations before producing a 784-value (28x28) MNIST image. The model uses a final Tanh output and contains 2,449,680 trainable parameters.
Awaiting generation.
This project mutates binary inputs for compiled test programs until a segmentation fault is found. The original command-line fuzzer uses a deterministic PRNG seed, extends the payload every 500 iterations, mutates each byte with a 13% probability, and writes both the crashing input and the seed/iteration pair needed to reproduce it.
The browser version below ports the core mutation loop to JavaScript and runs against lightweight target predicates, while the comparison table shows the native crash artifacts produced by the original fuzzer.
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Native Crash Finds
Placeholder summary.