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Explore & understand research papers with Theoria, an AI-powered app

Hacker News

Explore & understand research papers with Theoria, an AI-powered app

Hi HN, Theoria is my answer to doomscrolling. Instead of mindlessly scrolling, I wanted an engaging way to keep up with new preprints without the hassle of copy-pasting papers into an AI. It's for anyone who wants to stay current in their field or just explore new scientific ideas. I like how Theoria turned to out to be a consumer product and use it constantly myself and I'm curious if other people like it aswell. Here’s what it does: Personalized Feed: Create your own daily preprint feed from topics you love (any STEM topic atm), like Artificial Intelligence, Cosmology, or Genomics, or simply browse all fields. Personalized Podcasts: Turn your favorite articles into a short, natural-sounding podcast episode where two hosts discuss the papers. Instant Analysis: Grasp key points in seconds with AI tools that show a paper's "Deep Insights," "Technology Readiness Level," potential "Real-World Impact," and an "AI Peer Review." Chat with Albert: Ask our AI assistant, "Professor Albert," to explain complex topics from any paper in simple terms. It's built on a TypeScript/React/Node.js stack. You can try it out here for free: https://theoria-ai.com/ I'd love to get your feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, plain · Missing: mac, agents, macos
85%85% 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
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
38%38% 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
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% 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.

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