Are you experimenting with small networks or building large, multi-block architectures?

This helps optimize UI performance and features.

When you re wiring up a new architecture in PyTorch, what slows you down the most?

Layer selection? Shape debugging? Boilerplate? Something else?

NetSmith currently exports clean PyTorch modules.

I can expand the export options what would you prefer?

• Standard nn.Module

• Lightning-style modules

• Scriptable modules

• Custom template support

NetSmith is PyTorch-first.

If you could add one feature tomorrow for PyTorch models, what would it be?

Do you sketch models, copy older projects, or code directly in VS Code?

Understanding your workflow helps me design better drag-and-drop components.

Even experienced users hit snags with shape mismatches and repetitive boilerplate.

Where does it break down for you?

Planned:

Drag-and-drop layer editing

Reusable blocks (ResNet-style)

Automatic shape tracking

If your day-to-day work is deep in PyTorch and you re willing to break early builds, I want to hear from you.

Leave a comment and I ll reach out.