Should you build your own Pangram?
A classifier that scores whether a piece of text was written by an AI model.
The interface is a text box and a percentage, which is exactly the kind of thing that tricks you into thinking it is a weekend project. The product is not the text box: it is a classifier trained on a very large, continuously refreshed corpus of human writing paired with output from every model release, tuned hard against false positives because accusing a real person of cheating is the failure mode that ends the company. You can absolutely build a local detector from perplexity and burstiness features in an afternoon, and it will be confidently wrong often enough to be useless for any decision that matters. Nobody outside your own head will accept your homemade score, and the calibration drifts every time a new frontier model ships. Build it to understand the problem, not to rely on it.
ENOriginal research content in English. Original prompt preserved in English.
Build a local AI-text-detection playground so I can see how weak naive detection actually is. No cloud calls, no accounts, no telemetry.
Stack, no substitutions: Python 3.11, FastAPI + uvicorn, transformers + torch, a single server-rendered HTML page with vanilla JS. No build step, no database, no Docker.
Core loop:
1. One page with a large textarea and an Analyze button.
2. POST /analyze takes the text and returns a JSON report.
3. Scoring runs entirely locally with gpt2 (small) loaded once at startup via transformers. Download on first run and cache it.
Features to compute per submission:
- Mean token log-probability and perplexity under gpt2.
- Burstiness: standard deviation of per-sentence mean log-probability.
- Rank-based signal: fraction of tokens that were in the model's top-10 predictions.
- Style stats: sentence length mean and variance, type-token ratio, punctuation counts, count of common LLM filler phrases from a hardcoded list.
Output: a 0-100 score from a simple weighted formula over those features with the weights in a single WEIGHTS dict at the top of the file, per-sentence heat colouring in the page, and a full feature table so I can see what drove the score.
Calibration, and be honest about it:
- Add a CLI command: python calibrate.py --human dir --ai dir that runs the features over two folders of .txt files, prints mean and spread per class, and prints the ROC AUC of the current weights.
- The web page must show a fixed banner: this is an uncalibrated heuristic, not evidence, false positives are common.
Explicitly out of scope: user accounts, saved history, PDF or DOCX parsing, any hosted API, model fine-tuning, mixed-authorship segmentation, batch uploads.
Deliver: main.py, calibrate.py, features.py, templates/index.html, requirements.txt, and a README with run instructions and one paragraph stating plainly why this cannot match a trained commercial detector. No .env needed since there are no secrets; say so in the README.The prompt opens pre-filled in your agent. Review it before running. · this prompt is generated from the build plan · improve it via PR
What would building your own look like?
Paste text, score it locally with a small language model's token log-probabilities plus a few style statistics, and get a hand-wavy human-or-machine guess with a loud accuracy disclaimer.
What you will need
- Local Python with torch and transformers
- A couple of GB of disk for small model weights, CPU works but is slow
- Your own labelled samples of human and AI text if you want any idea of accuracy
Because the number has to be defensible to someone else. Institutions and publishers are not buying a classifier, they are buying a third party willing to stand behind a false positive rate, plus continuous retraining against whatever model came out last month. A local heuristic detector gives you a plausible-sounding percentage with no error bars, which is worse than nothing when the outcome is an accusation.
xCalibration: a real false positive rate you can quote, instead of a vibe
xCoverage of new models, which changes every few weeks whether you update or not
xSentence-level and mixed-authorship detection rather than one blunt document score
xAny external credibility, since a self-built score persuades exactly zero teachers, editors or clients
xThroughput, batch uploads, API access and document parsing
Nothing worth pointing at. That’s why the prompt exists.
How much does Pangram cost?
- Monthly billing
- $20/mo$20/mo
- Per year
- $240/yr$240/yr
- On a Brazilian card, with 3.5% IOF
- R$105/moR$1,254/yr
Saved to My list. You will see a notice on this site when the price changes.
You complete the purchase on Pangram’s site. Vibe Radar does not handle payments.
Is it cheaper to build or buy Pangram?
Adjust the numbers: hours to build, your hourly rate, running costs and the subscription price.
Over 24 months, subscribing is cheaper.
Starting hours estimated from the analysis (one sitting). Does not price what you give up (support, integrations, security).
Can I rebuild Pangram?
Not really. Pangram’s value is not the code: The moat is a constantly retrained corpus of human and model text plus a reputation strong enough that other people accept the verdict. See the honest breakdown above.
How much does Pangram cost?
Pangram costs about $20/mo (Individual, checked 10/20/2026), which is $240 per year.
What do I lose by replacing Pangram?
Honestly: Calibration: a real false positive rate you can quote, instead of a vibe; Coverage of new models, which changes every few weeks whether you update or not; Sentence-level and mixed-authorship detection rather than one blunt document score; Any external credibility, since a self-built score persuades exactly zero teachers, editors or clients; Throughput, batch uploads, API access and document parsing. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Pangram?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.