I made a free study tool powered by Qwen2.5

Hacker News

Hey HN, In my spare time, I built a web app that turns text or PDFs into question-and-answer flashcards using AI. It’s completely free - no paywalls, no sign-ups, just a tool anyone can use to study. To keep costs manageable, I set up my own AI server. As someone new to the AI space, it’s been a challenging but rewarding learning process. After testing various models, I found that Qwen2.5:3b strikes a good balance between speed and quality for my current server setup. This is a soft launch since I’m still unsure how well my server can handle parallel requests, but I’d love for you to give it a try: https://freeflashcardgenerator.com As someone who often struggled with schoolwork and got frustrated trying to concentrate, I hope this tool can make learning easier for others in the same boat. Please let me know what you think!

Visit product View on Hacker NewsJan 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

66%66% 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.

TrustMRRFits verified-revenue profile · Strong signals: para · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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