Vyasa – A client-side AI writing detector (WASM, no API calls)

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Now that Wikipedia has banned AI generated articles. - https://en.wikipedia.org/wiki/Wikipedia:Writing_articles_wit... I wanted to try and see if it was possible to get somewhat decent engine based on signs of AI writing from wikipedia themselves. It runs entirely in the browser via WASM. Added instructions to further add more ways to figure out as we find out more about LLMs. Would love feedback!!, especially: - cases where it completely fails - patterns you think are stronger indicators - whether this approach is even worth pursuing vs proper ML models, considering this is a classical classification task, I just don't like the idea of detection of a black box with another black box.

Visit product View on Hacker NewsMar 28, 2026

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

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

best fitHighest predicted score across all platforms for this description.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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