QuickNews, a News Aggregator Based on Doc2Vec

Hacker News

Hi everyone, Not happy with the current news aggregators and thrilled about ML models for text processing, a colleague and I built an aggregator fully based on Doc2Vec: https://www.quicknews.ai/. We currently poll ~120 news sources every 15 minutes, and construct a personalized news feed for every user. There are plenty of ways in which Doc2Vec is used here: 1) Clustering the articles into topics 2) Deduping overly similar articles when generating the feed 3) Routing notifications about breaking news to interested users 4) Calculating similarity between domains/publishers, so as to recommend sources similar to the ones the user already clicked on 5) Dislike (only on Android, and somewhat fickle) I'd be delighted if you tried it out and let me know what you think. Available on Android and iOS, with a web version planned. Android app: https://play.google.com/store/apps/details?id=com.quicknews.app iOS app: https://apps.apple.com/ca/app/quicknews-the-real-news-app/id1489638005

Visit product View on Hacker NewsFeb 3, 2020

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Did not reach leaderboard

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

77%77% 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: model, apple, google · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

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

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

42%42% 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

23%23% 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.

Incorrect prediction on native model

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