Free HN User Favorites API Endpoint (MIT)

Hacker News

I needed a way to access my hn favorites since I use them like bookmarks. This was put together very quickly. Uses x-ray (nodejs) API Endpoint: https://reactual.lib.id/hnfavs@dev Example GET request for user: sbr464, 1 page (30 favs): https://reactual.lib.id/hnfavs@dev/?id=sbr464&limit=1 It currently accepts 2 query params: id (string = '') - hn user id limit (number = 1) - pagination 30/page Will error if you don't include an id. I'll put it up on a more permanent URL and may move it to a different cloud function provider. I just used stdlib.com for now to get feedback. If the url changes, I'll put the updated url in my hn profile/source and include it in the JSON response error for a while. Link to source code (MIT): https://github.com/reactual/hacker-news-favorites-api/tree/m...

Visit product View on Hacker NewsJul 1, 2018

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4points

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Did not reach leaderboard

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% 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: way, para · Missing: mobile apps, ios, personal

41%41% 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

13%13% 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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