Document Q&A with GPT: web, .pdf, .docx, etc.

Hacker News

Hello fellow hackers, we made a site that gets GPT to answer your question using the info on a webpage you specify or document you upload (e.g., a large textbook .pdf file). Background: When ChatGPT came out, I had the idea of having it pull answers from my stereo receiver's annoyingly dense 32 page manual. My weekend project prototype proceeded to surprise with great answers—just like what we've all experienced by now. My co-founder thought we should productize it, and make it easy to use online. So here we are with a very early beta! (Try it on a HN thread...)

Visit product View on Hacker NewsMar 28, 2023

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

72%72% 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: chatgpt, using · Missing: mac, agents, macos

71%71% 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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: answers · 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

11%11% 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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