Sounds promising, right? I spent some time testing it, but results were poor—high false positives and false negatives, no clear threshold for reliable classification. Plus, it suffers from high inference costs, poor cross-model generalization, difficulty deploying large models locally, and lack of access to closed-source model weights. All in all, it’s neither elegant nor reliable.
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In effect, we use setraw to make sure our raw character input goes straight into our process (without echoing or buffering or anything), and then reading and echoing the character-codes we see until 3 appears (which is CTRL-C, the common command for existing a REPL). Since we've turned on tty.setraw print doesn't reset the cursor to the left anymore, so we need to manually move left with \u001b[1000D after each print.