Camera-based workpiece alignment and AI-assisted toolpaths: Screph demos

Hi, I'm Sergei, the developer of Screph (screph.com/), a visual environment for building image-processing workflows. I'm exploring how it can help with irregular stock and repeated CNC setup.

I have two complementary English demonstrations:
1. Camera-based workpiece positioning and transformation of existing toolpaths, including clamps and excluded regions:
2. An AI-assisted design based on a wood photograph, accounting for knots, followed by alignment of the prepared paths to the board on a simulated table:

The first example uses configurable image-processing masks to distinguish stock, table, clamps and excluded regions. The second explores how the design itself could respond to features of an individual workpiece. These are separate stages: generating the design and estimating the pose for coordinate transformation. The operator should inspect the intermediate results and transformed paths before accepting a job.

These are software demonstrations and simulations. I don't own a CNC machine, and this is not a claim of a validated LinuxCNC integration or measured machining accuracy. Physical calibration, stock height, fixture clearance and controller handoff still need to be verified on the actual setup.

I'm looking for a small practical case with a machine owner, not necessarily wood, and can help with a limited test implementation at no charge. What would be useful in your workflow, and what checks would you require before accepting camera-derived positioning?