Embedded our open-source charting lib into a no-code storytelling tool

Hacker News

Hi HN, We're a small team working on a new dataviz engine and its applications. We built our charting engine in C++ and run it on WebAssembly. Two years ago, we launched Vizzu as an open-source animated data visualization library aimed at JS developers, allowing users to create dynamic charts. We also released a Python wrapper under the name ipyvizzu for data scientists. Since then, we've been working hard to make this technology accessible to everyone, regardless of their coding skills. Today, we're proud to introduce our no-code storytelling tool. With this tool, anyone can turn their static data into dynamic, interactive presentations. What Vizzu Does: - Easy Chart Creation — Simply select your data and get chart suggestions. - Interactive Exploration — Identify relationships, trends, and outliers by visually exploring your data. - Data Story Presentation — Create a sequence of slides with animated transitions. Unlike other data visualization tools, Vizzu automatically animates chart transitions, making presentations more engaging and easier to understand. The focus on storytelling and ease of use sets it apart. Try It Out: You can try Vizzu right away by signing up at https://vizzu.io . We'd love to hear your feedback and suggestions! Links: Open-source JS lib: https://github.com/vizzuhq/vizzu-lib Open-source Python tool: https://github.com/vizzuhq/ipyvizzu Previous Show HNs: https://news.ycombinator.com/item?id=28895897 & https://news.ycombinator.com/item?id=30895975 P.S.: Our open-source tools will still be available for anyone to build great stuff with.

Visit product View on Hacker NewsJun 27, 2024

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

87%87% 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 · Missing: supports, reddit linkedin, podcasting

86%86% 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

72%72% 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: users · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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