Announcement
If you are building LLM-powered applications today, you are probably managing multiple LLM providers, a pile of API keys, and your own logic for routing, budgets, and failovers. Accessing language models is no longer the challenge. Operating LLM infrastructure effectively is. Today, we are excited to launch Otari, the
Announcement
Meet transcribe.cpp, a new open-source C/C++ speech-to-text inference library with portable, GPU-accelerated support for multiple STT models. Developed through Mozilla.ai's Builders in Residence program, it makes adding fast, local transcription to applications easier than ever.
Case Study
A look at how we use Octonous inside mozilla.ai to reduce the everyday overhead of product work, from turning Slack feedback into GitHub issues to staying on top of product changes and finding context across the tools where work already happens.
Announcement
Encoderfile now handles images. Starting with image classification, you can run vision models as a single executable — no Python runtime, no serving infrastructure, just a file path in and a label out.
Technical Content
Runaway agents? Provider outages? Discover why your AI stack needs an LLM control plane, not just a gateway, to handle production routing, budgets, and privacy.
Technical Content
AI coding sessions can feel like a black box. Route OpenCode through the Otari Gateway to track costs, token usage, and model activity in real time. Get budget controls and visibility across every session without changing a single line of application code.
Announcement
Meet Otari, an open-source LLM gateway powered by any-llm, and Otari.ai, the hosted platform built on the same foundation. Run frontier or open-weights models through one API with usage tracking, budget controls, routing policies, observability, and team management.
Expert Opinion
Cloud AI pricing changed fast in 2026. This post looks at why more teams are moving back to local models, the tradeoffs behind tools like Ollama and LM Studio, and why portability and ownership are becoming bigger concerns for developers.
Product Release
cq exchange gives agents a shared place to store and retrieve experience-driven knowledge through private namespaces and a public commons.
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The future of AI may not be agents using today’s apps. It may be apps rebuilt around structured representations agents can inspect, modify, and validate directly. The deck, doc, or dashboard becomes the output, not the source of truth.
cq helps coding agents share resolution paths and learn from past failures. We partnered with Lauren Mushro to bring VIBE✓ into cq and help review knowledge units before they enter shared memory.
The Octonous open beta is live. Learn what we discovered during closed beta, the workflow patterns users kept returning to, and the biggest improvements shipped since launch.
Sovereign AI shows up across nations, companies, communities, and individuals. This piece, based on a conversation with John Dickerson, CEO at Mozilla.ai, looks at control over AI systems, avoiding single points of failure, and building with modular, swappable components.
Encoder models power most NLP in production, but deploying them still means dragging along Python runtimes and dependencies. Encoderfile introduces a single executable with an appended payload and a format that can be inspected and understood.
Running a small trade business includes a steady flow of admin work: quotes, scheduling, invoices, payments, and more. This post looks at how that workload builds up and introduces Clawbolt, a focused assistant designed around these everyday workflows.
When source code and distributed packages don’t match, risks increase. This breakdown of the LiteLLM incident shares what to watch for and how to reduce exposure.
cq explores a shared commons where agents can query past learnings, contribute new knowledge, and avoid repeating the same mistakes in isolation.
llamafile 0.10.0 unifies portability and modern model features. Bundle weights, run multimodal models, and access tool calling and Anthropic Messages API support, all from a single executable.
AI is changing product development. When building becomes effortless, the real constraint is no longer code. It’s clarity, product judgment, and knowing when the right decision is not to ship yet.
Mozilla.ai joins Flower Hub as a launch partner with fed-phish-guard, a federated phishing detection project. The classifier trains across distributed clients and shares only model updates, allowing collaborative learning without centralizing browsing data.
AI lets engineers generate thousands of lines of code in minutes. But humans still reason about systems slowly. That gap forces a rethink of ownership, reliability, and where safety really lives in modern software systems.
