Random Reading Generator

Hacker News

After having taught myself Python, I've created a first - extremely trivial - "web app": www.luckyread.app. It recommends a random article to read every time you reload the page. The articles are taken from a weekly newsletter I publish since 2015. Generally, I'm selecting the top 3 most clicked pieces from each issue (cleaned by those that are outdated) and store it in a text file which is read by the script. Many of the articles are originally sourced from HN, so some here might appreciate the type of content. I built this mostly as a personal project to practice, and a little bit to promote my newsletter. However, obviously I could expand this. Any ideas what the next step could be?

Visit product View on Hacker NewsMay 12, 2019

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2points

Did not reach leaderboard

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

36%36% 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

18%18% 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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