I made an iOS HN app to navigate large threads without getting lost

Hacker News

I was struggling with navigating HN discussions using existing solutions, so I decided to implement a completely different approach, think of it as depth-first reading vs breadth-first reading. Visually it looks like swipeable stacks of comments and it offers several advantages over traditional interfaces: - Comment width doesn't get narrower no matter how deep in the comment tree you are - You always see the parent of the comment you're currently reading - Swiping allows you to move in and out of subtrees with animated transitions that you fully control - You can easily skip subtrees that don't interest you by scrolling As a result it's easier to maintain the context and to keep track of where you are in the discussion tree. The app is fully featured, it does all the things that you would expect it to do, and there's extra: custom boards, search, in-thread search, anchors, reading list, recent items. Video preview: https://imgur.com/a/tzBdpXw or https://streamable.com/arq45m

Visit product View on Hacker NewsMay 22, 2023

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

73%73% 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, video, way · Missing: mobile apps, personal, entrepreneurs

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: context, visual, using · Missing: mac, agents, macos

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

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

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

14%14% 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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