Structured JSON output from 12 LLM providers with a single function call. Type-safe, retry-resilient, zero boilerplate.
Typescript client for the Hugging Face Inference Providers and Inference Endpoints
Tagged template literal for Sanity.io GROQ-queries
A type inference plugin for ESDoc [PoC]
Hardware accelerated language model chats on browsers
The **Cerebras provider** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for [Cerebras](https://cerebras.ai), offering high-speed AI model inference powered by Cerebras Wafer-Scale Engines and CS-3 systems.
Avro for JavaScript
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Inference API for Azure-supported AI models
OCI NodeJS client for Generative Ai Inference Service
`@microsoft/app-manifest`
Scaleway SDK inference
LLMz - An LLM-native Typescript VM built on top of Zui
List of ML tasks for huggingface.co/tasks
The official TypeScript library for the Cerebras API
MediaPipe GenAI Tasks
Heroku CLI plugin for Heroku AI add-on
Lightweight client for Roboflow's hosted inference API with WebRTC streaming support
Infers JSON Schemas from example JSON
runtime type checking and validation of untyped JSON data
Generate TypeScript-first JSON schemas from OpenAPI definitions
Simple runtime TypeScript validator library
SkyKoi — Koi runtime. Multi-channel gateway CLI with RPC Koi.
Pipecat Daily Transport Package
Given a bunch of JSON documents that are assumed to be similar, collects info about common structure.
A Ruby library for working with multi-dimensional data using named dimensions. Initialise from an array of hashes making it trivial to use with databases, CSV, JSON, and YAML. Dimensions and coordinates are inferred automatically.
PMN (Portable Move Notation) provides a rule-agnostic, JSON-based format for describing the mechanical decomposition of moves in abstract strategy board games. This gem implements the PMN Specification v1.0.0 with a functional Ruby interface, breaking down complex movements into sequences of atomic actions while remaining completely independent of specific game rules. PMN reveals the underlying mechanics of any board game move through sequential action decomposition, supporting both explicit and inferred piece specifications. Built on CELL (coordinate encoding), HAND (reserve notation), and QPI (piece identification) specifications, it enables universal move representation across chess variants, shōgi, xiangqi, and any abstract strategy game. Perfect for game engines, move validators, and board game analysis tools.
Contentful API wrapper library exposing an ActiveRecord-like interface
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