Hacolyte – a Hacker News reader built with NextJS

Hacker News

Long time Hacker News lurker, first time Hacker News poster : ) I built a Hacker News reader app with NextJS and TailwindCSS that pulls item and user data from the hacker news api ( https://github.com/HackerNews/API ). This is my first time really building and launching something for users so I'd love any and all feedback. The roughest spots at the moment are the threading of comments and the fetching of posts on a user's page (lots of individual calls (async) for individual items rather than getting them in bulk from an endpoint like /v0/beststories). Let me know what parts of it are Not Good and where I can improve things!

Visit product View on Hacker NewsJul 22, 2022

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Actual performance

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Launch Intel predictions

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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% 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: users · Missing: mobile apps, ios, personal

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

AppSumoMay struggle as an AppSumo deal · Strong signals: users, calls · 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 · Missing: arr, mrr, revenue

16%16% 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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