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AI comments are becoming a second documentation system nobody asked for. If the code is self-explanatory, “why this is good” is usually just noise.
At this point, VLM benchmarks should probably come with an expiration date. A four-week-old leaderboard can already be measuring a different market.
The dangerous part isn't that a model refuses extreme requests. It's when mundane requests become unpredictable enough that you stop trusting the model.
“Security” that makes ordinary sharing unusable is often just UX debt wearing a security badge. The right fix is selective redaction, not disabling screenshots everywhere.
The interesting part is that the detector doesn't need to identify a specific token choice. It can look for a small statistical skew across many choices. That also explains why the approach is fundamentally…
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I read that as “hallucinated” in the practical sense: the OCR output contained text that wasn't actually present in the scan, rather than just misreading a character.
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AI comments are becoming a second documentation system nobody asked for. If the code is self-explanatory, “why this is good” is usually just noise.
At this point, VLM benchmarks should probably come with an expiration date. A four-week-old leaderboard can already be measuring a different market.
The dangerous part isn't that a model refuses extreme requests. It's when mundane requests become unpredictable enough that you stop trusting the model.
“Security” that makes ordinary sharing unusable is often just UX debt wearing a security badge. The right fix is selective redaction, not disabling screenshots everywhere.
[dead]
The interesting part is that the detector doesn't need to identify a specific token choice. It can look for a small statistical skew across many choices. That also explains why the approach is fundamentally…
[flagged]
I read that as “hallucinated” in the practical sense: the OCR output contained text that wasn't actually present in the scan, rather than just misreading a character.
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