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Native-feeling charts for Obsidian Bases, built with Deno

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Native-feeling charts for Obsidian Bases, built with Deno

Hi HN! I've built a plugin for Obsidian that adds lightweight, highly customized, and native-feeling chart layouts (Bar, Line, Pie) specifically for the new database-like "Bases" feature. Repository: https://github.com/zobweyt/obsidian-bases-chart-layouts Plugin page: https://community.obsidian.md/plugins/bases-chart-layouts ### The Problem I needed a way to visualize my structured note data in Obsidian without the bloat. Existing chart solutions either required writing complex JavaScript inside codeblocks or felt like heavy web-views slapped onto the app. I wanted charts that feel like a built-in, core feature of Obsidian—lightweight, snappy, and deeply integrated with the UI. ### What makes it "Native-Feeling" It goes beyond basic SVG rendering to mimic Obsidian’s native desktop UX patterns: - *Interactions:* Full support for `cmd/ctrl + click` and middle-click (`auxclick`) to open underlying data files in new tabs or split panes. - *Context Menus:* Right-clicking the chart triggers native Obsidian context menus, not generic browser ones. - *Theme Adaptation:* It doesn't just switch light/dark modes. It dynamically inherits your exact Obsidian theme CSS variables and fonts. ### Tech Stack & DX I wanted to keep the codebase as clean as the UI: * *No Node/NPM Boilerplate:* The entire project is built purely with Deno (fmt, lint, build). This kept the development environment exceptionally lightweight and fast. * *Rendering:* Powered by Apache ECharts under the hood for crisp vector rendering. The final plugin size is only ~1MB. * *I18n:* Built-in localization support (currently English and Russian). ### How to try it 1. Ensure you have Bases enabled in Obsidian. 2. Add a chart layout to your view. 3. Map your columns using the native Properties panel. I’d love to hear your thoughts on the UX integration, and especially on using Deno for building Obsidian plugins. Any feedback or feature requests are welcome!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, visual · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: visualize, way · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.

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