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Simple Graphviz Library for TypeScript

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Simple Graphviz Library for TypeScript

This is my first post to HN. This library provides a TypeScript friendly model of the Graphviz DOT language and functions to execute DOT commands. It also provides a module to manipulate AST as a low-level API. This library is also used by big names such as Apollo GraphQL and AWS CDK plugins, and has now grown to a 100k+ weekly dl project. I started this project to use it in the backend of Rediagram framework to generate infrastructure diagrams in React. See also https://news.ycombinator.com/item?id=24685886 .

Visit productView on Hacker NewsJan 19, 2023
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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, 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
66%66% 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: model, new · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
36%36% 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
10%10% 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
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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