Explain any ML models anywhere

Construct AI & ML models with JSON using Typescript & Tensorflow

Construct AI & ML models with JSON using Typescript & Tensorflow

Deploy ML models to production

Explain any ML models anywhere

Splitifi Intelligence MCP — outcome predictions, judge profiles, and legal workflows powered by 29,211 production ML models trained on real court records. Serves litigants, attorneys, judges, mediators, CDFAs, and litigation funders.

Zero-dependency multi-agent orchestrator for local, open-weight ML models. Route prompts across local hardware nodes with clean, heavily typed TypeScript.

Local Transformers.js embedder for Kura (bge-m3). Runs ML models in JS with no Python.

ObelyZK CLI — prove ML models and verify on Starknet from your terminal

Native face embedding using ML models

An open-source machine learning framework.

ObelyZK CLI — prove ML models and verify on Starknet from your terminal

Autonomous ML research harness for Claude Code. The autoresearch loop as a formal protocol — iteratively trains, evaluates, and improves ML models with structured experiment tracking, convergence detection, immutable evaluation infrastructure, and safety

Framework-agnostic utilities for loading and running ML models in the browser via WebGPU/WASM

Run inference on Hugging Face open ML models for NLP, vision, and audio tasks.

Static and dynamic vulnerability scanner for ML models and pipelines

Construct AI & ML models with JSON using Typescript & Tensorflow

APIs manage Azure ML models, images, profiles, and services.

Core types and classes for Google services.

ml-learn is a straightforward JS library to train ML models on your JSON data