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Introducing Handwired: JavaScript as Neural Networks
Developer Cheng Lou released Handwired, a tool that compiles ordinary JavaScript programs into neural network weights to replicate program output without training or GPUs.
What: Handwired is a proof-of-concept project that treats standard JavaScript functions as a set of operations that can be mapped to neural network layers, effectively bypassing the need for traditional backpropagation or massive training compute.
Why it matters: This challenges the assumption that neural networks must be trained via gradient descent; if code can be directly compiled into functional weights, it suggests new ways to bridge symbolic logic and neural architectures.
Decoder
- Differentiable: A mathematical property where a function can be optimized using gradient-based methods; in this context, it allows the program output to be adjusted by small changes to its 'weights'.
Original article
My dear friends, I’d like to show you my latest toy project Introducing Handwired: turn your regular JavaScript code into a neural net! - no training - exact outputs vs the JS program - fully differentiable All runnable in a browser, without GPU
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