Back to today

Ternlight

A 7 MB embedding model that runs entirely in the browser via WebAssembly

Surfacing on:hn

Hot score

70/100

Tracking since 2026-07-06. Saturation 18%.

The sections below are AI-summarized from the source platforms listed at the bottom. Always verify against the original sources before acting on the information.

What is Ternlight?

Ternlight is a compact embedding model designed to run directly in the browser using WebAssembly (WASM), with a total size of only 7 MB. This allows developers to perform semantic search, clustering, or similarity computations on the client side without sending data to a server. The model is optimized for low latency and privacy, making it suitable for applications where data must stay local. Based on community signals so far, Ternlight appears to be a fresh launch, with a demo available at ternlight-demo.vercel.app. The lightweight footprint enables integration into web apps, browser extensions, or edge functions. While specific performance benchmarks and API details are still emerging, the core value proposition is clear: a self-contained embedding model that eliminates server dependencies for vector generation.

How to use this signal

Three ways a creator, builder, or agent can put Ternlight to work today. Each comes with a copy-paste prompt for ChatGPT or Claude.

  1. Benchmark against your current model

  2. Write a hands-on review

  3. Test as drop-in replacement

Key features

  • 7 MB model size
  • Runs in browser via WebAssembly
  • No server required for inference
  • Privacy-preserving local embeddings
  • Suitable for semantic search and clustering
  • Low latency client-side processing

Who should use this

Web developers building privacy-focused apps that need on-device semantic search, recommendation, or clustering without sending data to a server. Also useful for edge computing and browser extensions.

Comparable tools

Other tools tracked by trendsmeter in the same space.

Where it's surfacing

Source trail

1 source attached to this trend.

Trend velocity

rising

Saturation

18%

Schema

Word v1

Use this trend

Share the report, or copy a prompt that turns this signal into a useful brief.

Post to X

Track tomorrow's trend signals before they settle.

The daily feed, API, and MCP endpoint all read the same schema.

View OpenAPI