Liquid <> .txt Collaboration | Liquid AI

Liquid AI — Device-native foundation models.

Liquid AI is an efficiency-first foundation model company. We build highly capable, compute-optimized models that bring intelligence to any device and medium of choice.

Introducing LFM2: The Fastest On-Device Foundation Models on the Market | Liquid AI

Today, we release LFM2, a new class of Liquid Foundation Models (LFMs) that sets a new standard in quality, speed, and memory efficiency for on-device deployment. Built on a hybrid architecture, LFM2 delivers 200% faster decode and prefill performance than Qwen3 and Gemma 3 on CPU. It also significantly outperforms models in each size class on instruction-following and function calling—the core capabilities that make LLMs reliable for building AI agents.

Liquid AI on Twitter / X

Today, we release LFM2.5-350M. Agentic loops at 350M parameters.A 350M model trained for reliable data extraction and tool use, where models at this scale typically struggle.<500MB when quantized, built for environments where compute, memory, and latency are constrained.🧵 pic.twitter.com/zZPKzcCwH9— Liquid AI (@liquidai) March 31, 2026

Automotive — Solutions — Liquid AI

On-device AI for automakers — real-time, personalized in-car assistants that run on the vehicle's existing CPUs and NPUs.

MacPaw Partners with Liquid AI to Bring On-Device AI to Millions of Mac Users — Blog

MacPaw partners with Liquid AI to bring private, fast, on-device AI to millions of Mac users, starting with the Eney assistant for macOS.

Liquid AI Launches LEAP and Liquid Apollo: The Easiest Way to Build with On-Device AI | Liquid AI

Today marks a pivotal milestone in the evolution of edge AI. Liquid AI is thrilled to announce LEAP v0, our first developer-ready platform for on-device AI deployment—and Liquid Apollo, a lightweight iOS-native application built to showcase and stress-test small foundation models directly on your phone.

Union.ai: Ship fast. Scale big. Orchestrate the future.

The AI development platform to go from experimentation to production faster. Orchestrate, train, and serve AI.

Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents

The Future of AI Should Serve People, Not Platforms - The Liquid Frontier

The control layer for AI

The industry spent two years teaching LLMs to speak JSON. That work mattered: free-form text was unusable in production. Today, every serious inference provider uses some implementation (often open-source) of structured output. Structured output has become essential infrastructure that the team at .txt is proud to have spearheaded. Even as the ecosystem evolves, and open-source alternatives emerged, our engine remains the state of the art.

The control layer for AI

The industry spent two years teaching LLMs to speak JSON. That work mattered: free-form text was unusable in production. Today, every serious inference provider uses some implementation (often open-source) of structured output. Structured output has become essential infrastructure that the team at .txt is proud to have spearheaded. Even as the ecosystem evolves, and open-source alternatives emerged, our engine remains the state of the art.

Liquid4All/liquid-audio

Liquid Audio - Speech-to-Speech audio models by Liquid AI

How Claude's text watermarking works

Future Claude models will generate text that contains a watermark. This is a way of determining the likelihood that Claude was involved in writing the text, and we, along with several other major AI providers, are implementing this change to comply with the EU AI Act. In this article, we share answers to some of the questions we’ve received about how our chosen watermarking method works, whether it affects Claude’s outputs, and why we’re making this change.

LukeW | Common AI Product Issues

At this point, almost every software domain has launched or explored AI features. Despite the wide range of use cases, most of these implementations have been t...