Wu

WunderGraph – Aggregate REST and GraphQL APIs, Add AuthN/Z and Caching

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WunderGraph – Aggregate REST and GraphQL APIs, Add AuthN/Z and Caching

Hey, I'm Jens, founder of WunderGraph. Over the years of working with REST & GraphQL APIs, I found that some aspects of using it are way too complicated. Here's a list of problems I believe could be abstracted away: - Aggregating multiple GraphQL, REST, etc. APIs into a simple to use API (Backend for Frontend) without writing code - adding Authentication & Authorization to APIs you don't have full control over - adding efficient and easy to use Caching to GraphQL APIs without writing code - adding persisted queries for security and performance reasons without making my application code and deployment process more complex Companies like Facebook, who are concerned about security and performance, use persisted Queries and don't expose their GraphQL API directly to the public. While developing they write their Queries using Relay and persist (whitelist) them at compile time. At least that's my understanding from their blog posts and conference talks. WunderGraph takes this approach to the next level by turning the flow around. Relay, Apollo, URQL, etc. are very complex pieces of software because of the dynamic nature of GraphQL. With WunderGraph we define all Operations in GraphiQL "on the server" and then generate a very simple client from it. In a nutshell, Queries become simple GET requests with variables as query parameters, Mutations still are POST requests but just with variables as the body. A more in depth explanation including an example can be found here: https://www.reddit.com/r/graphql/comments/ijui1x/show_wundergraph_public_beta_aggregate_rest/ More info & docs: https://wundergraph.com/ For those who like to watch videos, here's a general overview: https://youtu.be/RwkThD5pz1E Here's a full 26m tutorial with React & Typescript that helps to start from scratch: https://youtu.be/8BQNeeVoFGI

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting
86%86% 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: using, code, apis · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
77%77% 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: video, way, para · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · 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
18%18% 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.

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