Smartly Organizing & distributing articles from HN,TC etc at ScoopSpot

Hacker News

We (pillvin and I) have created a micro blogging website which helps people to follow their interests. Interests could either be in other people or topics. http://www.scoopspot.com We would like to highlight a key usecase to show auto tagging and tag based personalized feeds in ScoopSpot: 1. ScoopSpot has a tagging model that automatically crawls links in a scoop (300 character long text message) and detects tags automatically. 2. A user can follow other users and topics. 3. It provides a personalized feed of scoops from people & topics followed (we call them favorites) by a user. 4. As part of this use case, a news aggregator was built to consume rss feeds from some of the well known technology websites such as Hacker News, Tech Crunch, ReadWriteWeb, TheNextWeb, ZDNet, TheServerSide and BBC Technology. The aggregator create scoops for every article discovered by it. The scoop would essentially contain title of the article and the link to actual page. Scoops from news aggregator can be found at http://news.scoopspot.com 5. In addition, it would also contain tags to highlight important topics that are addressed in a particular article. This helps organize news automatically by topics. For example:- If you are interested to know about what's happening in Java of late, you can visit http://www.scoopspot.com/Java or http://www.scoopspot.com/Google to get latest buzz on Google. 6. Now with feature in point #2, it is possible for users to follow Java and Google in ScoopSpot. This eliminates the need to visit each topic page separately. The feature in pt#3 provides a consolidated feed of favorite topics and people. We are in public beta now. We would like to get your feedback about this service in particular and ScoopSpot in general. Please read the "Why join ScoopSpot?" section to get an idea of prime features of ScoopSpot. Please feel free to sign in with your google/yahoo/facebook id.

Visit product View on Hacker NewsMar 22, 2012

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4points

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Did not reach leaderboard

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

79%79% 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 · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% 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 · Strong signals: personal, google, users · Missing: mobile apps, ios, entrepreneurs

43%43% 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

38%38% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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