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I built an AI in 3 days at 16 y/o that lets you chat with your PDFs

Hacker News

I built an AI in 3 days at 16 y/o that lets you chat with your PDFs

Hey, it's Safwan I'm 16 and obsessed with AI and coding. Last year, I was struggling with my homework - especially when teachers gave us long PDF documents to analyze for history and literature classes. The problem? I'd spend hours scrolling through 30-page PDFs trying to find specific quotes or information for my essays. My friends had the same issue - we were all wasting time instead of actually learning. I tried Ctrl+F, highlighting, even printing everything out... but I realized what we really needed was to ask questions to our documents like we would to a teacher. So during summer break, I taught myself more about AI and built the first version of PDF Chat. Now I can finish my research in minutes instead of hours, and I'm helping students and professionals worldwide do the same. Pretty cool for a high schooler, right?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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: coding · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% 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
38%38% 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
19%19% 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
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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