Labs · Text X-Ray
X-ray a passage, see how
guessable every word is.
Results appear here — every word tinted by how guessable the model found it.
Texts over 6000 characters are trimmed. Works on phones too (same local-only pipeline), but download + init are slower — desktop is the comfortable path.
How the machine works
As a language model reads your text, at every position it keeps a ranked list of what the next word should be. We hold each of your actual words against that list: rank 1–10 counts as a dead giveaway (red), beyond rank 1000 as a genuine surprise (teal). Scan a passage and you get a statistical X-ray — clichés and repetition fuse into red bands, while genuinely personal phrasing tends to run teal. The per-word rank coloring comes from GLTR (Harvard NLP + MIT-IBM, 2019); seven years on, the original still sits server-side, English-only, on GPT-2. This one runs in your browser and adds the Chinese track that never existed.
Not a detector — and we have the data
Before shipping we calibrated the instrument on five groups, 100 provenance-labeled passages. The result belongs on the front page: human-written formulaic webfiction lands at a 64–71% top-10 hit rate, our own officially published AI novels (written under an anti-slop discipline) at 54–65%, and raw LLM continuations from five frontier models even lower, 52–61%. The bands overlap and flatly invert the folk intuition that AI text is the predictable one. The reason is mundane: a probe model measures distance from its own 2019-era training distribution, and formulaic webfiction is the densest part of that distribution. GLTR's authors admitted the method would fade against newer models; we quantified that fade with our own corpus. So when someone sells you a perplexity-based AI verdict, these three inverted bands are the question you should ask them.
Three lenses, one honest kit
This is the third lens in our AI-flavor toolkit: the AI Flavor Checkup covers the rule layer (stock-phrase blacklists and sentence-mold quotas, hits named one by one), Fiction Prose Stats covers the structure layer (sentence rhythm, dialogue ratio, verbatim repeats), and Text X-Ray covers the statistical layer — a real neural network's per-word surprise. Each has blind spots: rules miss new clichés, structure can't see word-level texture, statistics can't tell a formulaic human from a restrained machine. Used together, they're more honest than any one alone.
FAQ
Is my text uploaded anywhere?
No. On first scan your browser downloads a 73–81MB quantized mini-model from a CDN; after that, all inference happens on your device in a Web Worker (WASM). Nothing is sent to any server — with the model cached, the scan even works offline.
Can it tell whether a text was written by AI?
No, and we won't pretend otherwise. We calibrated the instrument on 100 provenance-labeled passages: on this probe model's scale, formulaic human webfiction (median 69% top-10 hit rate in Chinese) reads as MORE predictable than raw LLM continuations (57%). Perplexity and authorship stopped lining up years ago. What you get is per-word texture, not a verdict.
Why does it download 70+ MB?
Because a real neural network runs in your browser: the Chinese track is a 6-layer distilled Chinese GPT-2 (q8 ONNX, 73MB), the English track is DistilGPT-2 (81MB). Each downloads once and stays in browser cache; engine init takes a one-time 10–30s per page visit, and the scan itself is sub-second.
What's the relationship with GLTR?
Homage and a hand-off. GLTR (Harvard NLP + MIT-IBM Watson AI Lab, 2019) colored each word by its rank in GPT-2's predictions — the founding artifact of visual AI-text forensics. It stayed in 2019: server-side, English-only, with its own disclaimer about newer models. This is the same idea rebuilt for 2026: it runs in your browser, covers Chinese and English, and puts the 'fails on newer models' caveat on the table with our own calibration data.