> 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?

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yoeven 40 minutes ago | parent [-]

Code extraction maybe, not something we have tested or built for but you could give it a try.

Code manipulation probably not since it's a lot smaller of a model compared to a Claude Opus which is SOTA for code generation/manipulation.

Generally code generation is a non-deterministic task by nature and general LLMs tend to be better at them.

	▲	wood_spirit 18 minutes ago \| parent [-]
		The idea of what to change is perhaps an llm task but the job of doing the find replace and that kind of tooling is something LLMs actually struggle with and have all kinds or crutches and try retry loops to paste over in coding agents etc.