Image classification is now supported in encoderfile 📸

encoderfile makes it easy to deploy encoder models with zero dependencies. With this release, we're expanding beyond text tasks to support image classification, including flexible Lua-based preprocessing and support for CLI, HTTP/S, and gRPC interfaces.

Coming next:
- Object detection and image segmentation

👀 Read the full post and let us know what features you'd like to see next: https://blog.mozilla.ai/image-classification-comes-to-encoderfile/

Can healthcare organizations build better AI without sharing sensitive patient data?

On June 10 at SIIM26, John Dickerson, CEO of Mozilla.ai, will present "Share For Healthcare: Privacy-Preserving, Open Access to Data and Models Is Critical in the Age of AI."

The session will explore how institutions can collaborate on AI development, improve models, and keep patient and provider data local and secure.

Get the details: https://link.mozilla.ai/siim26

Less than one week until our live Octonous webinar.

A lot of team productivity gets lost between tools, handoffs, approvals, and manual follow-ups.

Join Caroline Bohu from @MozillaAI for a practical walkthrough of how AI agents can help teams automate repetitive workflows and reduce busywork.

Save your spot: https://link.mozilla.ai/octonous-june2026-webinar

Meet Carlos Álvarez Barba, who recently joined https://Mozilla.ai.

Carlos has built and operated infrastructure throughout his career, from high-performance computing to cloud, platform engineering, data platforms and AI/ML systems.

He enjoys solving the behind-the-scenes challenges that keep systems reliable, deployments smooth and developers productive.

Outside of work, he’s passionate about tropical plants, paludariums and side projects.

Say hello and welcome Carlos to the team 👋

Open-weights models should not feel like a downgrade.

Today we're launching Otari, an open-source LLM gateway built on top of any-llm, and Otari.ai, the hosted platform built around it.

Use frontier or open-weights models without giving up web search, code execution, observability, budget controls, routing policies, and more.

Try the beta: https://link.mozilla.ai/otari-own-your-ai-stack

What happens when you turn a local AI model into a coding agent?

We tested Gemma 4 26B running locally through llamafile on a MacBook GPU.

The model connected to VS Code through an OpenAI-compatible server.

Then we gave it a broken React app to fix.

It read the codebase, rewrote components, and fixed the cart issue completely offline.

Try it yourself: https://link.mozilla.ai/huggingface-llamafile-010

The shift back to local AI isn’t only about privacy anymore.

For a lot of teams, it’s becoming a cost decision.

In this new post, Anushri Gupta, DevRel at @MozillaAI, looks at rising cloud AI costs, local AI lock-in, and why portability is becoming a bigger conversation for developers.

Read it here: https://link.mozilla.ai/ai-got-expensive

AI is useful. Until you have to connect it to real work.

On June 8 at 2PM BST, Caroline Bohu from @MozillaAI will host a live Octonous webinar focused on practical AI workflows for everyday work.

We’ll walk through connected workflows, approvals, automations, and practical examples across ops, support, sales, product, and marketing teams.

Join the live walkthrough: https://link.mozilla.ai/octonous-june2026-webinar

If you missed John Dickerson’s piece on sovereign AI a couple weeks ago, one section feels increasingly relevant:

Many AI systems today depend heavily on one provider’s pricing, policies, and access remaining stable over time.

The piece looks at how teams can build with more resilience and flexibility across their stack.

Read more: https://link.mozilla.ai/sovereign-ai