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YAML Studio – Visual OpenAPI Editor (no code, no $refs)

Hacker News

YAML Studio – Visual OpenAPI Editor (no code, no $refs)

Hi HN I built YAML Studio, a visual editor that helps you design complex OpenAPI specs without hand-writing YAML or dealing with $ref headaches. It's a drag-and-drop, form-based tool that generates clean OpenAPI 3.0/3.1 YAML — useful for devs, PMs, and tech writers who want to collaborate on API design without needing deep spec knowledge. Why I built this: I’ve spent countless hours manually writing and debugging OpenAPI files — breaking things with a bad indent or misused $ref. Existing tools like Swagger Editor are great, but still very code-centric. I wanted something more intuitive, where people could build valid specs visually, and hand off ready-to-use YAML. What it does: Add paths, methods (GET/POST/...), params, schemas, responses Full support for OpenAPI 3.0 and 3.1 Define request/response bodies via forms Supports OAuth2, bearerAuth, and x-* extensions Exports a complete, valid YAML spec instantly No signup required — just open and build Try it here Under the hood: Built with React, a JSON Schema-based config layer, and a YAML generator that flattens nested structures. All data stays in-browser — no telemetry, no backend (yet). I'd love your feedback: What’s missing? What would make this part of your workflow? Would you use this with your team or for solo API builds? Thanks in advance for trying it out!

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, code, open · Missing: mac, agents, macos
96%96% 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: supports, para · Missing: reddit linkedin, podcasting, created
72%72% 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, existing, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: intuitive · Missing: plus, platform, reviews
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
30%30% 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
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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