Trends – A GitHub trending PWA

Hacker News

Over the past few weeks I've been working on a small application to view trending repos on GitHub. I built the PWA[1] with React, Next.js and GraphQL .. but what's interesting is the application is only using React server side, meaning the client side javascript is only a few lines code adding some event listeners and registering a service worker for offline capability. This helped me achieve a perfect google chrome performance audit I learned a ton working on it and would love to talk about it if anyone has any questions! Application Link: https://trends.now.sh Source code on Github: https://github.com/hanford/trends [1]: https://developers.google.com/web/progressive-web-apps/

Visit product View on Hacker NewsJul 24, 2018

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95points

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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.

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 HuntOn track for Day 1 leaderboard · Strong signals: google, apps, using · Missing: mac, agents, macos

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

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

49%49% 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

38%38% 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

15%15% 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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