Mocking APIs by just posting jsons to endpoints

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I made a self-hosted API for crud-ing json data (initially for simple storage use cases). However, I started using to mock APIs, where I have scripts that create/edit random jsons and I make them available as API endpoints through this application. The API is based on your JSON structure. So the example below is for CRUD-ing [key1][key2] in file.json. The value (which can be anything) is then added to the body of the request. Moreover, there are helper functions for appending and incrementing values. DELETE/PUT/GET: /api/file/key1/key2/...

Visit product View on Hacker NewsOct 21, 2025

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

67%67% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% 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: host · Missing: plus, platform, intuitive

30%30% 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

16%16% 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

12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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