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Learn GraphQL by Roleplaying Game of Thrones Characters

Hacker News

Learn GraphQL by Roleplaying Game of Thrones Characters

Hey HN, Here's my GraphQL game! https://graphql-of-thrones.herokuapp.com/ I was learning GraphQL myself (through http://howtographql.com/ , it's great) and found myself pretty lost in the tooling and serverside code when it still hadn't been established clearly to me what GraphQL -is-. I am a big admirer of educational games like vim-adventures.com, flexboxfroggy.com and flexboxdefense.com to progressively learn something so I decided to do something similar for my bootcamp capstone. The point I wanted to get across is GraphQL is such a universal idea that you can literally envision Game of Thrones characters scribbling queries on parchment and that would still be valid GraphQL. Shoutouts are due to UnderExpress.com for the landing page components which made Show HN a few days ago and Codecademy for a proven UI I could shamelessly steal from (with styled-components). More code details written up here: http://www.swyx.io/graphql-of-thrones/ A little nervous since this was put together in a week with a lot of spit and glue but hoping it might help someone out there get to grips with GraphQL. https://graphql-of-thrones.herokuapp.com/

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
84%84% 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
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: education · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos
32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% 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
12%12% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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