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An AI text detector can be built from scratch as a small educational project by fine-tuning a classifier model, and it can also be used as a verifier to train a small language model that produces text avoiding detection.

The author introduces the project: building an AI text detector to explain how AI detection works, motivated by Substack's new feature, and using the detector as a verifier for training an SLM that bypasses detection. ✦ AI generated

the author · Ahead of AI · 2026-08-15 · original ↗

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Substack recently launched its AI detector feature in the UI, which is super interesting. Separately, lots of people asked me about interesting local do-it-yourself LLM projects as demos to show what small language models (SLMs) are capable of. Putting one and one together, I thought it would be interesting to show how an AI detector can be implemented. I will also use it as a verifier to train a small language model to produce text that avoids detection. This is a small educational project for studying the limitations of AI detectors and exploring a verifier-based LLM application beyond regular reasoning models trained on math and code.

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Mechanism · 1

The detector will be built by fine-tuning a DistilBERT classifier, a method similar to Pangram models, which are believed to power Substack's AI detection feature.the author · Ahead of AI · conf 60%

Context · 1

There are different approaches to detecting AI-written text—from supervised classifiers and perturbation-based probability tests to perplexity measures and watermarking—and this tutorial builds a model returning a 0-100 score.the author · Ahead of AI · conf 70%

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gives example → An AI checker can detect whether your own writing has been overpolished into text that sounds AI-generated, so it can help improve writing while avoiding being flagged as spam.the author · Ahead of AI