A joint post from Continue and Docker exploring how to leverage containerized MCP blocks to enhance your AI native development experience Introduction As developers integrate AI into their workflows, they face several practical challenges: limited model context windows, insufficient tooling access, and difficulty connecting models to external data sources. The

As your codebase grows, finding the right information at the right time becomes increasingly difficult. Everyone who's spent hours hunting for a function they wrote months ago or searching for documentation on how to use a library understands this pain acutely. While Continue's default search capabilities

We're excited to announce a new partnership with nCompass, bringing Google's cutting-edge Gemma 3 family of models to the Continue Hub! This integration exemplifies what we believe is the future of AI native development: giving developers access to the best models through an open ecosystem of

With guest author Eitan Borgnia Frontier models like Claude 3.7 are becoming unbelievably strong coders, but they're also slow and expensive. They generate text at ~100-200 tokens/sec and can cost upwards of $15 per million output tokens. When you use frontier models to implement edits to

As developers, we want tools that respect our privacy, let us customize our experience, and integrate seamlessly into our workflows. Continue paired with Ollama offers exactly that—a way to run custom AI coding assistants locally. The benefits of this include 1. Data privacy: your code stays on your machine,

When Mistral AI launched the original Codestral model in May 2024, it immediately became the best autocomplete model—a Large Language Model (LLM) trained for code infilling and capable of low latency responses. This is why it has been our top recommendation for developers since then. And now, six months

We are excited to partner with SambaNova Systems to launch QwenCoder2.5 32B on their cloud! In October, they published a blog post about how to use Continue with Llama 3.1 405B. Today, Continue developers can now experience the power of getting suggestions from QwenCoder2.5 32B on the

Last week, Anthropic introduced the Model Context Protocol (MCP), a new open protocol that "enables seamless integration between LLM applications and external data sources and tools". Also last week, Continue added support for MCP! Let’s explore why MCP is well-suited for Continue and how you can use

“UX risk” Over the last year and a half of developing features that users didn’t like, finding out they didn’t like them, and then trying again, we’ve built a better idea of what will and won’t fly. Among the biggest predictors is what I call “UX

This is a guest post on the Continue Blog by Sophia Parafina, a developer advocate who has previously worked at Pulumi, Anaconda, and Docker. Continue is an open-source AI code assistant. It's a compound AI system that uses a set of models (chat, autocomplete, embeddings, etc) along with

Continue is the leading open-source AI code assistant, already having tens of thousands of users, nearly 200 contributors, and a vibrant, growing community. We envision an ecosystem of products that build with and around Continue, and that is part of why we released our project under an Apache 2.0

If you interpreted “root path” as the root directory of your computer, don’t worry! Continue wants nothing to do with this. What we’re going to share is our strategy for gathering (and caching) autocomplete context, which relies heavily on the root path: the path from a node in

Anthropic’s rollout of prompt caching, also known as context caching, follows similar offerings from providers like DeepSeek and Google and has been getting a lot of attention recently. At Continue, we're particularly excited about the potential impact on engineering teams, as prompt caching can significantly reduce costs