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Science related links

Hacker News

Science related links

Hi HN, I've built this small website: http://follow.gy The links are obtained by crawling posts made by Kyle Hill on his blog (I obtained his permission beforehand). I also have a Facebook bot that posts on the Facebook Page every few hours: http://facebook.com/follow.gy . The backend is written in NodeJS, it uses socket.io and express and the database is redis. The website is running on an ec2 Medium instance. Largely the motivation came from wishing to build a website from "backend to frontend" all by myself (first time for me). But I also saw a lot of potential in the idea. So, let me know please your opinions on the website, from a technical as well as "product" point of view! Cheers, and happy holidays :) P.S.: I just finished it, and I'm thinking to start promoting it (currently only a handful of people in the whole world know about it! imagine that!). Any advice on how to go about that would be appreciated too!

Visit product View on Hacker NewsDec 22, 2013

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

74%74% 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

54%54% predicted probability of success on Product Hunt, 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

44%44% 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

38%38% 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

34%34% 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 · 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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