Detect AI-Generated Content with Our Advanced Text Analysis API

Hacker News

I'm launching an API to detect whether text was written by a human or AI. It works with short(ish) text and also longer text. This API could come in handy to detect spam or AI generated content in general from models like ChatGPT or GPT-3. Simply send text to the API and it will return the number of tokens used, probability of the prediction, and prediction of real or fake, as well as individual predictions for smaller chunks of the text. It works best with at least 8 sentences. The API is more or less ready but I'm looking for some Beta testers. Feel free to check out the demo on the website.

Visit product View on Hacker NewsJan 7, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, chatgpt · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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