Hanabi.rest – AI-based API building platform

Hacker News

Hey HN! Hanabi.rest is a web application that utilizes AI technology to generate REST APIs and SQL from prompts or screenshots, significantly speeding up backend development. This application allows for checking the functionality of APIs directly connected to the data layer through a browser, thereby greatly improving the development process. Hono.js is optimized for web standards and Edge runtimes, enabling deployment in various environments such as Cloudflare, Fastly, Deno, Bun, Lagon, AWS, and Node.js. The APIs created can be shared through links, allowing community members to contribute to the improvement of applications through stars and forks. Support is also provided for importing npm packages, automatically adding types to the editor. Furthermore, it is possible to automatically generate APIs with AI using links to interfaces generated from Vercel's v0.dev. Additionally, the generated APIs can be deployed directly to Cloudflare Workers, and D1 databases can be created, migrated, and worker scripts deployed with just a few clicks from the browser, making it easy to introduce them into production environments. A dedicated CLI is also available for configuring projects and advancing local development. We’re eager to hear your thoughts and feedback. Give Hanabi.rest a try and let us know what you think!

Visit product View on Hacker NewsMay 30, 2024

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

90%90% 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: created · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews

67%67% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

61%61% 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 · Missing: mobile apps, ios, personal

25%25% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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