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AI-Powered Mock API Generator

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AI-Powered Mock API Generator

@aashj99 and I have worked on countless side projects where we’ve needed to spin up mock APIs for demos, prototypes, etc. We’ve used existing mock API generators, which require too much effort and manually defining fields to generate generic data that doesn’t quite match our use case. GPT is remarkably good at generating highly relevant and representative data. For example, generating a dataset of ‘list of commercial properties in Los Angeles’ gives you a realistic address (456 Sunset Blvd, Los Angeles, CA 90026) and neighborhood (Hollywood) and relevant fields like price_per_sq_ft and amenities. We’ve personally found this useful in quickly spinning up prototypes backed by realistic data that matches our use case. We hope you find it useful for all the projects you work on as well :) Here’s how you can use the Mock API Generator for your own project: - Use a natural language prompt (i.e. ‘list of Harry Potter characters’) to immediately generate a schema and corresponding dataset - Fiddle with the data by adding, editing, and deleting columns and setting number of rows and fields till it’s just right for you - Export your dataset as a CSV or generate a REST API that persists it with standard read and write endpoints We’re also planning on building a Figma plugin where you can generate mock data and APIs to power your Figma designs and prototypes. Let us know if you would find that interesting. Would love to hear some feedback and questions, as well as what you’d love to see next. Thanks :)

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apis · Missing: mac, agents, macos
78%78% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
37%37% 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 · 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 · Strong signals: arr · Missing: mrr, revenue, profit
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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