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VeriIA – AI detector for Spanish and English text

Hacker News

VeriIA – AI detector for Spanish and English text

Hi HN, I’m an indie dev and built VeriIA ( https://detectordeia.pro ), an AI detector for Spanish and English text. The motivation: there are plenty of AI detectors for English, but I couldn’t find much for Spanish or other languages. Most people I know just paste Spanish text into an English-focused tool and hope the result still means something. I wanted to try what it looks like if you start from Spanish instead. Right now the app is simple: you paste or upload a text, it gives a probability that it was written with AI, plus sentence-level highlights so you can see which parts look more “AI-like”. It’s mainly being used on essays and reports. The intention is to provide a rough signal for reviewers, not a definitive answer. On the tech side it’s a fairly standard web stack: Next.js, React and Vercel on the frontend, with Supabase for auth and storing user scan history. This is an early version: only Spanish and English are supported, longer texts work better than short ones, and the scores are obviously probabilistic and sometimes wrong. I’m interested in feedback on two things: how (or whether) you think AI detection should be used in real workflows, and how you would approach evaluating a tool like this for non-English languages.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
60%60% 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
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% 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
21%21% 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.

Correct prediction on native model

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