Your agent can now work without you asking (and more)

This issue: an LFM that puts proactive agentic work within reach, a partnership that means better AI for macOS, and the coverage putting Liquid AI in the spotlight

ENGINEERING

🌐 Deploy everywhere: Putting agents on any device with the new LFM2.5-2.6B model

What’s small enough to run on a phone, fast enough to stay responsive on a CPU, and capable enough to power agentic workflows? Edge AI agents have been improving rapidly in recent months, but most still involve painful trade-offs between speed, size, compute, and cloud LLM usage.

In many cases, escalating a task to the cloud solves whatever immediate problem an edge agent is facing, but it also brings drawbacks. There’s the cost—in time, tokens, and privacy—which defeats at least part of the purpose of putting agents on-device in the first place.

But more important is the missed opportunity for proactive agentic work. Agents that run entirely on-device can use local, periodic inference over device context to identify useful tasks before a user explicitly asks for them. Keeping that loop local can reduce network latency, preserve privacy, work through connectivity gaps, and make frequent inference more practical.

Our newly released LFM2.5-2.6B model is optimized for just that, opening up the possibility of an edge agent that does useful work before users realize it needs to be done. Its size, speed, and capabilities are ideally suited to high-volume agentic workloads, and its training was designed specifically to complete chained agentic tasks while staying on-device.

Learn more about our agentic, on-device model below and contact our team for a demo.

→ Read the blog
→ Download the model

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PARTNERSHIPS

💻 MacPaw partners with Liquid AI to bring on-device AI to millions of Mac users

Liquid AI and MacPaw have announced a strategic, long-term partnership to co-develop the technology stack for local AI on the Mac, with the goal of bringing efficient, private, on-device LFMs to millions of Mac users.

LFMs will be fine-tuned specifically for macOS AI tasks, running locally on Apple silicon through MacPaw’s Elix inference engine, while MacPaw’s Mnemos memory layer lets the assistant retain context and get more useful over time. Together, that’s on-device intelligence, persistent memory, and native task execution in one product — something that hasn’t existed on the Mac before.

→ Read the announcement

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IN THE NEWS

📢 Recent coverage featuring Liquid AI, from post-transformer architecture to agents on a Raspberry Pi

MIT Technology Review on what comes after the transformer. Ramin Hasani explains our hybrid architecture and why the brain’s 20 watts is the efficiency target.

VentureBeat covers the LFM2.5-2.6B launch. Maxime Labonne on building for agentic harnesses rather than chatbots and running real agents on a Raspberry Pi.

TechCrunch on our MacPaw partnership and how we select an architecture that is different and tailored to the hardware first.

McKinsey convenes us with AMD, Dell, and Mercedes-Benz to discuss where AI is headed, from pushing intelligence onto edge devices to what it takes to actually scale AI across an enterprise.

The Boston Business Journal on how Liquid AI builds models its own way and why our path differs from the frontier labs.