Kurisu – Statistical dashboard in a single HTML file
I built Kurisu to solve a specific problem I kept encountering: analyzing sensitive data in offline settings without IT approval or cloud uploads and without needing to login and out of accounts on every device i own. The constraints led to interesting technical decisions: *Single-file architecture:* The entire app is one HTML file. No build process, no npm install, no deployment. You can WhatsApp it to yourself and open it on any device with a browser. This bypasses corporate IT restrictions and makes distribution trivial. *Statistical engine in Web Workers:* Implements Spearman Rank…
What it does
In the maker’s words, at launch
I built Kurisu to solve a specific problem I kept encountering: analyzing sensitive data in offline settings without IT approval or cloud uploads and without needing to login and out of accounts on every device i own. The constraints led to interesting technical decisions: *Single-file architecture:* The entire app is one HTML file. No build process, no npm install, no deployment. You can WhatsApp it to yourself and open it on any device with a browser. This bypasses corporate IT restrictions and makes distribution trivial. *Statistical engine in Web Workers:* Implements Spearman Rank Correlation, Kruskal-Wallis, and Mann-Kendall tests client-side. The automated dashboard scores potential visualizations by statistical significance rather than generating random charts. *Local-first processing:* All data stays in the browser. Useful for compliance-heavy scenarios (HIPAA, financial data, NDAs) where cloud analytics tools are prohibited. *Real use case:* I work 9am-9pm, then code nights. During potential investor/client meetings over coffee, being able to drop their Excel file into Kurisu and generate insights in 30 seconds without "let me set this up in Excel" has been valuable. *Technical tradeoffs:* - Export occasionally crops at bottom edge (html2canvas limitation, fixing in v1.1) - Single file = harder to maintain at scale, lots of things break all the time but distribution simplicity wins for now Future plans: Optional AI model integration (bring your own API/local LLM), embedded tiny model experiments, desktop wrapper if there's demand. This is my first shipped product after 3 months of learning to code via AI assistance. Would especially appreciate feedback on: 1. The statistical implementation correctness 2. Single-file distribution model viability 3. Technical debt concerns as it grows 4. ways to be able optimise the app to work with with larger datasets and more complex calculations within the web worker. Built from Pakistan where most SaaS pricing doesn't make sense. $29 one-time felt right but feedback on this would be appreciated as well.
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