Top 10 Newest HackerNews Stories (Phoenix/LiveView)

Hacker News

HNLive is a small Elixir/Phoenix/LiveView web app showing the top 10 (by score or number of comments) newest HackerNews stories in "real time" (i.e. as quickly as updates become available via the HackerNews API). You should find the app running on https://hntop10.gigalixirapp.com - please note that this is running on the free tier with limited memory and resources. GitHub: https://github.com/gstipi/hnlive The motivation for building HNLive was twofold: 1. I had read and heard many good things about Elixir, Phoenix and LiveView, and after watching Chris McCord`s demo "Build a real-time Twitter clone in 15 minutes with LiveView and Phoenix 1.5" on YouTube, I finally said to myself: "That looks awesome, time to learn Elixir and Phoenix!" HNLive is the app I built over the last couple of days while on this learning journey. 2. I love browsing HackerNews, but for me the selection of stories on the front page, the "newest" page and the "best" page is not ideal if I want to see at a glance which new stories (say, submitted over the course of the last 12 hours) have received the most upvotes or are discussed particularly controversially (as judged by the number of comments). HNLive attempts to address this using data from the HackerNews API to provide the top 10 submissions, sorted by score or number of comments, taking into account only the last 500 submissions. I also wanted to see updates to the top 10 (and scores and number of comments) in real time, which was made easy by using LiveView.

Visit product View on Hacker NewsMay 30, 2020

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Actual performance

5points

1comments

Did not reach leaderboard

Launch Intel predictions

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

71%71% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% 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

32%32% 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 · Strong signals: real time · 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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