Novinha Na Cam Se Masturbando (CONFIRMED)

2. For each word:

But wait, in the example response, the assistant replaced "John" with syn... which might have been an error. So I need to avoid that. Therefore, in my processing, I'll check if a word is capitalized. If it is, leave it. Otherwise, process it. For example, in "Hello, my name is John.", "Hello" is capitalized, so perhaps part of the greeting and not a name. Wait, but "Hello" is a greeting, not a name. So the capitalization check isn't sufficient. Hmm, this is a problem. How to differentiate between words that are names and those that are not.

1. Split the input text into words and punctuation.

- "Hello": capitalized, assume it's not a name (it's a greeting), but according to the user's instruction, names should be left. Wait, maybe "Hello" is not a name. So this is a problem because the capitalization isn't a reliable indicator. Hmm. Novinha na cam se masturbando

"Hello", ",", "my", "name", "is", "John", "."

2. For each word, determine if it's a name. Since there's no context, maybe the user expects a naive approach where certain capitalized words are considered names. However, in the example, "John" was kept the same, but the assistant replaced it with synonyms, which contradicts the instruction. Wait, in the example, the user input was "Hello, my name is John." and the assistant output was "Hellosyn2, mysyn1 namesyn3 issyn1 Johnsyn3." So "John" was replaced, but according to the user's instruction, names should be left unchanged. This seems like a mistake in the example. So the correct approach should be to leave names like "John" as they are, without adding synonyms. Therefore, in the output, "John" should not have the syn tags.

Alternatively, the user might not have any names in the text, just a sentence with ordinary words. In that case, all words should be replaced with synonyms. But in the user's instruction, they specify to "Leave names unchanged," implying that if there are names in the input text, those should remain. So perhaps the user will provide a different input with actual names, and we need to leave those as they are. So I need to avoid that

Therefore, in processing the text, I need to identify words versus names. That might be tricky because if the text includes a name like "John" without context, how can I know it's a name? Maybe the user assumes that names are obvious. So perhaps the user wants all words (excluding proper nouns) to have synonyms, but if a word is a name (like John), it's left unchanged.

Wait, maybe the user is testing the system to see if I can handle such a query. They might not have included the actual text yet, but they want me to process it once it's provided. Since the example in the history shows a similar scenario where the assistant replied with the result only, I should follow that structure.

But given the instruction, the assistant must try. Perhaps the example provided by the user was incorrect in the past, but the current task is to make sure that names are left as is. Otherwise, process it

1. Split the text into individual words, considering punctuation. Need to handle cases where words might be attached to punctuation, like "Hello,".

So, the key steps are: