> These are deep neural network architectures that are task-specific for things like OCR, translation, or GUI detection. The way they consume and see data is trained to be task specific, which makes them up to 100x more accurate at their specific task. They also produce useful metadata like bounding boxes and confidence scores, letting developers build predictable workflows they can rely on.
Does code extraction and manipulation fit in that? Would interfaze be the agent that a coding agent uses?
Smaller models really arent great at structured output. If this works it would be great for a local model that might not be as good but as long as it respects structured output will be vastly more useful.
> These are deep neural network architectures that are task-specific for things like OCR, translation, or GUI detection. The way they consume and see data is trained to be task specific, which makes them up to 100x more accurate at their specific task. They also produce useful metadata like bounding boxes and confidence scores, letting developers build predictable workflows they can rely on.
Does code extraction and manipulation fit in that? Would interfaze be the agent that a coding agent uses?
Potentially stupid question: Does that mean we can chain them together line UNIX command line programs ? That would be so, so intuitive.
Smaller models really arent great at structured output. If this works it would be great for a local model that might not be as good but as long as it respects structured output will be vastly more useful.
> Smaller models really arent great at structured output.
That is a straight-up lie. Consider gpt-5.4-nano which supports structured output just fine.
https://developers.openai.com/api/docs/models/gpt-5.4-nano
It seems like a concern that's orthogonal to the model size.
I genuinely doubt that they are just lying though lol
This is cool, Id love to be able to fine tune on this architecture. Is this something on the roadmap ever?