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I built Goodreads for academic papers to help my wife survive her PhD

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I built Goodreads for academic papers to help my wife survive her PhD

My wife recently started her PhD and while she uses Zotero as a citation manager, she was really missing a tool for keeping track of what she's reading right now/has already read/needs to read next. This overwhelming experience might be familiar to other researchers working on a niche field where every paper is called pretty much the same thing So I built her Paperstack. It's basically Goodreads, but for research papers: - Save articles to familiar To Read/Reading/Read stacks - Tag articles with logical projects/chapters/whatever your system is - Get AI-generated summaries so you can quickly decide if a paper is worth your time I would love feedback from any students and researchers managing paper overwhelm!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, wife · Missing: supports, reddit linkedin, podcasting
80%80% 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 · Missing: mac, agents, macos
61%61% 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
58%58% 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
31%31% 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
30%30% 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
15%15% 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.

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