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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
Ben Affleck discusses his technical knowledge of machine learning and computer vision, explaining convolutional neural networks and tensors in detail. He reveals he's visited Google and OpenAI's labs to see their video transformer work, leveraging his celebrity status to get private access.
- Affleck has hands-on experience with Python, GPUs, and visual effects workflows that incorporate machine learning, stemming from his ownership of a small VFX company before Artists Equity.
- He understands the technical mechanics of CNNs—converting images to tensors (numerical representations of RGB pixel values) and using edge detection and feature extraction to identify objects in video for compositing.
- He's gained private access to Google and OpenAI's video transformer research by directly calling and asking, using his name to get meetings most people couldn't arrange.