We

We Built Our Own Chart Type for Live Dashboards

Hacker News

We Built Our Own Chart Type for Live Dashboards

Most analytics tools give you 10–15 chart types and call it a day. But what happens when none of them actually represent your data well? We ran into this when building a CRM dashboard. We wanted to show how sales reps connect to open deals: not just totals, but actual relationships. A bar chart was too flat, a table too dense. We needed a network graph. The problem? Most chart libraries and BI tools don’t support that out of the box. You either say “no” to the feature request, or you hack together a standalone visualization that breaks the product’s flow (no filters, no interactivity, no theming). So we built it ourselves, but in a way that still plays nicely with the rest of the dashboard. Luzmo now lets you define your own chart type, write your own visualization code, and then drop it into the dashboard editor like any other chart. Luzmo still handles the boring stuff: querying, filtering, theming, and cross-chart linking. The end result: - Sales reps become nodes in a network graph, with open deals orbiting around them. - Deal size controls node size; win probability controls color. - Everything responds to filters and interacts with other charts out of the box. The tutorial and GitHub repo walk through the whole process: setting up the builder, defining data slots, writing render methods, and packaging it all up for deployment. I’d love to hear from others who’ve solved similar problems: - Have you ever needed a chart type your BI tool didn’t support? - Did you build it yourself, or find a workaround? - How important is native interactivity vs. just embedding a standalone visualization? Read the post + see the full code: https://www.luzmo.com/blog/build-custom-charts

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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: visual, activity, code · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
34%34% 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
21%21% 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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