encypherai

4 ms·

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by encypherai 1y ago
Really appreciate that, this was exactly the goal. Detection always felt like a guessing game, so we wanted to flip the model and build something verifiable from the start. We’re already starting conversations with folks close to the major
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Show HN: AI detectors suck, here's an open-source way to embed proof in AI text(github.com)
2 points by encypherai 1y ago|3 comments
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by encypherai 1y ago
AI detectors suck. They flag real students as cheaters, mislabel original writing as “likely AI,” and rely on statistical guesswork that just isn’t reliable. Even OpenAI shut down their own detection tool, citing low accuracy. So I built En
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by encypherai 2y ago
Relevant Article: https://encypherai.com/blog/ai-plagiarism-detection-is-broke... A video overview & demo of the python package can be found on our homepage: https://encypherai.com/
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Show HN: EncypherAI, Open-source tool for cryptographically verifying AI text(github.com)
2 points by encypherai 2y ago|1 comments
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by encypherai 2y ago
Interesting study, but I think this framing is a bit too pessimistic. AI isn’t replacing critical thinking, it's shifting where we need to apply it. We'll be spending less time on monotonous tasks and more time architecting projec
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by encypherai 2y ago
We've had the opposite experience, especially with o3-mini using Deep Research for market research & topic deep-dive tasks. The sources that are pulled have never been 404 for us, and typically have been highly relevant to the sear
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by encypherai 2y ago
Yes, several of the most popular (and even lesser-popular but newly open-sourced models such as Gemma 3 27b) overuse Em dashes. Even when prompting them to not use dashes, they almost can't help themselves and include them occasionally
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by encypherai 2y ago
Thanks for the detailed explanation of autoregression and its complexities. The distinction between architecture and loss function is crucial, and you're correct that fine-tuning effectively alters the behavior even within a sequential
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by encypherai 2y ago
That's a really interesting point about committing to words one by one. It highlights how fundamentally different current LLM inference is from human thought, as you pointed out with the scene description analogy. You're right tha