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Handwired converts regular JavaScript control flow (if-else, loops, state) into matrix multiplication and ReLU operations without any training required. The resulting code stays fully differentiable and runs on GPU, enabling use cases like training frozen programs end-to-end or parallelizing sequential JS code.
- Transforms conditional logic into arithmetic using ReLU tricks (e.g., if-else becomes a weighted sum of both branches selected by a binary mask), keeping everything differentiable without training weights
- Collapses all operations into matmul and ReLU primitives, which GPUs can heavily optimize through fusion, sparsity, and reordering techniques
- Enables hybrid workflows: freeze a handwired program and train it alongside a generic neural net, or run sequential JavaScript at GPU-level parallelism (demo processes 100k JSON documents in milliseconds)
handwired
+ differentiable programming
+ neural nets
+ javascript
+ gpu
machine learning
+ optimization