Search

GPU-accelerated (Metal) semantic search over a BBC News corpus. Portfolio project demonstrating a custom Metal compute kernel for top-5 vector similarity search.

Declarative JSON-schema mock data generation and REST-like mocking for React/Next.js — no separate server.

Migrate Angular projects to signal-based components using official Angular schematics plus a conservative custom codemod.

Shared connector substrate for Happy Technologies products: manifest/descriptor schema, a generic registry, config validation, and an OAuth/credential substrate. Domain-agnostic; products supply their own connector contract, crypto, and storage.

Framework-agnostic, pluggable authentication for React applications — session management, token refresh, HTTP client, and reusable auth UI.

Node.js TypeScript WebSocket service for compiling Arduino projects (tinyStudio backend)

Browser websocket adapter for nengi client

Convert tabular data between markdown, CSV, TSV, Excel (xlsx), JSON, and plain text. Includes a CLI and a TypeScript library for reading, writing, and transforming tables.

Capgo native push notifications, badge, and live-update trigger plugin for Capacitor apps.

Vitest configuration presets for libraries, React apps, and Storybook within the Galaxy.

Official TypeScript SDK for DStats. Collect Discord bot analytics with a simple adapter-based API.

Low-latency live browser rendering for Puppeteer: a web component, a puppeteer-extra plugin, and a backend session manager.

A package that provides a kinematic analysis of mechanisms and cams and custom tooth profile for spur gears.

The probabilistic serial mechanism

Mechanism Configuration is a library for managing chemical mechanisms in atmospheric models.

Mechanism-learn is a simple method to deconfound observational data such that any appropriate machine learning model is forced to learn predictive relationships between effects and their causes, despite the potential presence of multiple unknown and unmeasured confounding. The library is compatible with most existing ML deployments. The library is compatible with most existing ML deployments such as models built with Scikit-learn and Keras.

A package for handling missing data intelligently

virtual mechanism for mock and test

Keras Attention Layer

The probabilistic serial mechanism