GraphQL Zero – AIs Mocking APIs

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I made an API mocking tool that imports a schema, populates fake data, then runs a local server. This is helpful for testing APIs or advanced prototyping. A demo video is here: https://www.loom.com/share/abad2cdf325e4e0b9addea1e14406166?... There’s two fun things about this tool 1. the complete lack of configs and schema annotations. i.e. you don’t need to learn faker-js. You just need your existing GraphQL schema and to to swap out the server URL in your frontend code. 2. the depth. Your fake objects have relationships to other fake objects. And in proxy mode, real objects can have fake relationships to fake objects I built this on my journey to productize Backend GPT ( https://news.ycombinator.com/item?id=34503418 ). I forked the open source GraphQL Faker project. Then added in so many LLM calls that it costs me $1 in OpenAI credits every time I process a schema. I learned from BackendGPT that without gating, many will use free OpenAI calls for their own ends (and those ends are often very weird). So this time I open sourced as much code as possible but put a small amount behind my server. Next steps are supporting mutation mocking via code gen models and mocking other kinds of things (REST APIs, databases, etc) Open to any ideas here

Visit product View on Hacker NewsJun 27, 2023

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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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