I don't work for Pangram, but I do a lot of writing at work. API docs, blogs, code, tweets, linkedin, all that.
If you've tried AI detectors a couple years ago, they're basically in the same position that coding agents were a few years ago, where everyone was skeptical at first, but the tech has gotten a lot better. Give it a shot, it's quite good. They are slightly tuned a bit towards classifying things as AI, but I imagine that's deliberate.
The only thing is that their models are pricey, but, very useful.
People expect a binary response, is it AI generated yes or no. But it's more complicated than that. For example, if you see an emdash, it's probably AI generated. But it can also mean the author used it for fixing grammar or tenses. LLMs can't help but try to help. The same for it's not X, but Y. Sure it's a known pattern, but it's not like people don't use this trope all the time.
In my experience, Pangram is great for detecting an author who is trying to pass someone else's work as theirs, or if they are tackling a subject they have little to no knowledge in.
I’m surprised at the current sentiment in the comments. Pangram is amazing and has really interesting engineering too. I would have guessed that reliably identifying LLM generated text was not possible without watermarks.
they also publish great tech reports. their founder is so confident in their model that he's regularly on social media offering bounties for false positives
I don't work for Pangram, but I do a lot of writing at work. API docs, blogs, code, tweets, linkedin, all that.
If you've tried AI detectors a couple years ago, they're basically in the same position that coding agents were a few years ago, where everyone was skeptical at first, but the tech has gotten a lot better. Give it a shot, it's quite good. They are slightly tuned a bit towards classifying things as AI, but I imagine that's deliberate.
The only thing is that their models are pricey, but, very useful.
People expect a binary response, is it AI generated yes or no. But it's more complicated than that. For example, if you see an emdash, it's probably AI generated. But it can also mean the author used it for fixing grammar or tenses. LLMs can't help but try to help. The same for it's not X, but Y. Sure it's a known pattern, but it's not like people don't use this trope all the time.
In my experience, Pangram is great for detecting an author who is trying to pass someone else's work as theirs, or if they are tackling a subject they have little to no knowledge in.
I’m surprised at the current sentiment in the comments. Pangram is amazing and has really interesting engineering too. I would have guessed that reliably identifying LLM generated text was not possible without watermarks.
they also publish great tech reports. their founder is so confident in their model that he's regularly on social media offering bounties for false positives
https://www.pangram.com/blog/pangram-4-technical
https://pangram-public.s3.us-east-1.amazonaws.com/pdf/pangra...
https://www.pangram.com/blog/introducing-pangram-image-detec...
How accurate is this? I want to see a negative before i provide you with a positive.
Negative? What?
I detect SPAM.
> An AI detector that actually works.
I detect Claude.
Interestingly checking good chunks of their site, it detects as 100% human
I mean, that's just the most direct way to say what they're saying? Doesn't smell Claudy to me.
Like, if I had an AI detector that actually worked—and all the other ones didn't, which they don't—I'd put it exactly the same way.