Box-n-Weave.js – 0-dependency text-to-diagramming lib (16kb gzipped)

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I was frustrated with the complexity and size of existing diagram libraries, so I wrote a new one from scratch. Box-N-Weave.js generates box-and-arrow diagrams from text definitions, with a focus on maintainable technical documentation. Key features: - Zero dependencies, tiny footprint (16.2kb gzipped vs 745kb for Mermaid.js) - Clean separation of diagram content, styling, and animations - Git-friendly text format that can live alongside your code - Streamlined syntax for common technical diagrams (architecture, flows, state machines, etc.) - Built-in animation support The layout algorithm was written from scratch (not using dagre/elkjs/cytoscape) to keep it lightweight while flexible enough to add novel features. Examples: https://box-n-weave-026ffb.gitlab.io/#examples Repo: https://gitlab.com/andrewfulrich/box-n-weave I'd love feedback on bugs, the API design, and use cases you'd want to see supported. The goal is to keep it focused on doing one thing well - making it dead simple to maintain box-and-arrow diagrams as code evolves.

Visit product View on Hacker NewsNov 25, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, tiny · Missing: agents, macos, agent

69%69% 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: para · Missing: supports, reddit linkedin, podcasting

62%62% 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, ide · 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.

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

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

17%17% 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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