Voyager – Code-first diagrams for microservices

Hacker News

Voyager is a small tool we built for quickly pulling together diagrams of your microservices (and dbs, MQs, etc), and how the data between them all relates. Voyager started life as feature we were working on for Vyne ( https://vyne.co ), but we got feedback that some people would like to play with it directly. So, we've extracted it into a standalone tool, and we're releasing the first version today. Voyager creates diagrams from Taxi ( https://taxilang.org ) code. We're working support for other schema specs (OpenAPI, Protobuf, SQL DDL), as well as an on-prem version that lets you publish directly from your code. We'll also be open sourcing the code shortly too, there's just a little unpicking to do. Here's our announcement blog post: https://blog.vyne.co/introducing-voyager/ Please have a play, and let us know your thoughts!

Visit product View on Hacker NewsOct 14, 2022

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

84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting

38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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 · 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

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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