svelte + vite + webComponent, dashboard card
Storybook React renderer
React package for snapshot testing.
@vue/runtime-core
React package for shallow rendering.
Bundle Renderer for Vue 3.0
Renders highlighted Prism output using React
@vue/server-renderer
Storybook Vue 3 renderer: Develop, document, and test UI components in isolation
TypeScript definitions for react-test-renderer
A library for analysing JS RegExp
Storybook for React: Develop React Component in isolation with Hot Reloading
React package for creating custom renderers.
Listr verbose renderer
Just a simple logging module for your Electron application
Render videos in the browser (not yet released)
A library to find JS RegExp with super-linear worst-case time complexity for attack strings that repeat a single character.
Listr update renderer
Render Remotion videos using Node.js or Bun
An abstract Lit directive for adding a renderer to a Vaadin web component
Storybook Svelte renderer: Develop, document, and test UI components in isolation.
Create PDF files on the browser and server
Storybook HTML renderer: Develop, document, and test UI components in isolation
Screenshots with JavaScript
A multi-dimensional semantic layer on top of ActiveRecord that allows running pivot table queries and rendering them as CSV, HTML, or KickChart-ready hashes. Supports time dimensions, cohort analysis, custom rollups, and drilling through to the underlying ActiveRecord objects.
qdfca (Quick-Deploy Formal Concept Analysis) is a command-line filter that implements Formal Concept Analysis (FCA). It is small, scriptable, and easy to install, with no external requirements other than the standard Ruby library. The input is a formal context in CSV table format. The output is a dot format digraph rendering of the concept lattice with reduced labelling.
Proper related posts plugin for Jekyll - uses document correlation matrix on TF-IDF (optionally with Latent Semantic Indexing). Each document is tokenized and stemmed, every word found is treated as keyword for analysis (except for some stop words). TF-IDF matrix for the whole site is calculated (including extra provided weights), then if given accuraccy is lower than 1.0, LSI algorithm is used to compute new simplified vector space. Document correlation matrix is created using dot product of the matrix and its transpose. For each of the post' related documents are inserted into priority queue (sorted by score from document correlation matrix), assuming the score is greater than minimal required score. Selected few bests related posts are retrieven from the queue. Liquid template for each post is rendered and <related-posts /> is replaced with the outcomes of algorithm.