Can AI replace WasItAIGenerated?
The interface is a text box; the product is a fine-tuned classifier. Detection quality does not come from a clever prompt. It comes from training a model on a labelled corpus and tuning it until the error rate is low enough to act on, which is months of work against a target that moves every time a new generator ships. Asking a general model "is this AI-written?" produces confident, unreliable answers, and in an academic-integrity setting that means falsely accusing real students. A one-shot build gets you the UI and none of the responsibility.
01What it costs
Checked Aug 12, 2026 · source: wasitaigenerated.com.
| Plan | Monthly | Billed yearly | What you get |
|---|---|---|---|
| Starter | Free | Free | 1,000 credits after email verification; text costs 1 credit/word, image 300 credits, audio/voice 1,000, video 2,000. |
| Unlimited Website | $9.99 | — | Unlimited website detections across text, image, audio, and video; API access not included. |
| Starter Pack | — | — | 40,000 API credits; credits never expire. |
| Credit Package | — | — | 200,000 API credits; credits never expire; bulk/API access. |
Hidden costs: The $9.99 website subscription does not include API use; API calls consume separately purchased credits.
02Could AI build it for you?
The core job: Build a private workspace that pastes in text, asks one general model for an AI-likelihood judgement with reasoning, stores results, and exports them.
What a working version needs:
- OpenAI or Anthropic API key in .env
- Node.js 22
- SQLite database
- A README stating plainly that the output is a guess, not a detector, and must never be used to accuse anyone
The text box is trivial. The trained classifier and its measured false-positive rate are the product.
03What you'd give up
- a trained classifier and the labelled corpus behind it
- a measured false-positive rate you can quote to an institution
- detection for images, audio and video, not just text
- per-sentence highlighting instead of one document-level guess
- retraining as generators change
Institutions do not buy a verdict, they buy a defensible one. A university that flags a student needs a documented error rate, an audit trail and a vendor who will stand behind the number. That is a measurement problem, not an interface problem, and it is why every serious buyer in this category asks about false positives before features.
05The build prompt
Paste this into an AI coding tool (such as Claude, ChatGPT, Lovable or Replit) to build your own version. Read the verdict first: this one is hard to get right.
Build the closest honest consolation tool inspired by WasItAIGenerated. Do not claim it detects AI writing. Use exactly this stack: Next.js 15 + TypeScript + SQLite. Primary job: paste text, send it to one general language model, and record that model's opinion on how machine-like the writing reads, with its reasoning. Start from an empty folder and create the complete working project. Single-user and private by default; store everything locally. Put every secret in .env and provide .env.example. Show the result as an opinion with reasoning, never as a score, a percentage or a verdict - the whole point of this build is that it cannot measure what the paid product measures. Put a permanent, non-dismissable notice in the UI and the README: this is one model's guess, it has no measured error rate, and it must never be used to accuse anyone of anything. Include clear empty, loading, validation, success and failure states. Add export so the user is not trapped in the app. Accessible keyboard navigation, labels, focus states and sensible contrast. Deliberately exclude these paid-product advantages: a trained classifier, a labelled corpus, a published false-positive rate, image/audio/video detection, per-sentence attribution. Do not fake accuracy claims, benchmarks or compliance statements. Write unit tests for the data model and one end-to-end smoke test of the core loop. Create a README with setup, architecture, data location and an explicit limitations section. Run the tests and build before finishing, then fix what fails.
06Open-source starting points
- Binoculars: Zero-shot LLM text detection using perplexity ratios between two models.
- DetectGPT: Curvature-based zero-shot detection of machine-generated text.
App prices, verdicts, alternatives and build prompts are adapted from Can I Vibecode It? (MIT License, © 2026 Rob Hallam). Each price shows the date it was checked and its source. Prices change; confirm on the vendor's site before you decide.
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