When Cursor acquired the Continue.dev team on June 18, 2026, it marked the end of an era for developers who had built their entire local AI coding stack around the open-source tool. The GitHub repository went read-only the next day, and users had until July 15 to export their data before it was deleted. For those running privacy-focused, self-hosted setups where code never leaves their hardware, the loss of Continue meant finding a new solution fast.

The irony cuts deep. Continue was built specifically as an open, privacy-first alternative to GitHub Copilot, designed for developers who didn't want to send their code to cloud servers. Then it got acquired by Cursor, which is itself a paid cloud-based IDE. The mission didn't survive the acquisition, even though the code lives on under an Apache 2.0 license.

What Happened to Continue.dev and Why It Matters?

Continue shipped its final 2.0.0 release on June 19, 2026, and the project entered maintenance mode. No new features, no new pull requests, no roadmap. For casual users who just wanted cloud-based AI assistance, the transition to Cursor's paid IDE might feel natural. But for developers who deliberately built a Proxmox homelab (a type of virtualization platform) with Ollama (a tool for running large language models locally) specifically to keep their code private, the shutdown created an urgent problem.

These developers had invested time and infrastructure into a stack where code never leaves their hardware. They needed a replacement that respected that same architecture. Several alternatives exist in the market, including Tabby, Void, and Aider, but one tool has emerged as the clear successor for this specific use case: Cline.

Why Is Cline Becoming the Go-To Replacement?

Cline offers near-perfect overlap with Continue on paper. Both are Apache 2.0 open-source VS Code extensions, both support bring-your-own-key authentication, and both connect to Ollama at localhost:11434, meaning they work with locally hosted models. For developers who had Continue working, migration to Cline can happen in under ten minutes.

The key difference lies in how the tools approach AI assistance. Continue operated primarily as a developer-controlled AI layer, answering questions when prompted. Cline is more agentic, meaning it operates with greater autonomy. It reads files, runs terminal commands, inspects error output, and iterates until a task is complete. Every action still requires human approval before execution, which is the right design for a tool that's actually touching your codebase.

For self-hosted setups running on modest hardware, this distinction matters significantly. Local AI models have historically struggled with complex, multi-step reasoning tasks. A 7-billion-parameter model can handle autocomplete reasonably well, but planning a multi-file refactor is typically a disaster. With Cline's architecture and proper model selection, the experience on consumer hardware becomes meaningfully better than what Continue's approach allowed.

Cline has substantial momentum behind it. As of August 2026, the tool has 5 million VS Code installs and 58,000 GitHub stars, making it far from a fringe project. It maintains an active development cycle, which is exactly what developers want when choosing a long-term replacement for a tool that just got acquired into oblivion.

How to Migrate from Continue to Cline with Ollama?

• Install Cline in VS Code: Open the Extensions panel, search for "Cline" (extension ID saoudrizwan.claude-dev), and install it. Despite the name referencing Claude, the tool is not locked to that model and works with any API provider, including Ollama.
• Configure the Ollama connection: Click the Cline icon in the Activity Bar, open settings, select Ollama as the API provider, and enter your Ollama instance URL (for example, http://192.168.1.50:11434) and model name. Leave the API key field blank since Ollama doesn't require authentication on local networks.
• Extend the context window: Create a Modelfile that increases the context window from Ollama's default 2K-4K tokens to at least 32,768 tokens. This is the critical step most guides skip, and it's why people think "Cline with Ollama doesn't work." Without this fix, the model loses track of what it was doing mid-refactor and starts hallucinating.
• Select the right model for your hardware: For 8-16GB of memory, use qwen2.5-coder:7b. For 16-24GB, use qwen2.5-coder:14b. For 24GB or more, use qwen2.5-coder:32b at Q4_K_M quantization, which is currently the community standard for local coding agents.

The context window fix is essential. Ollama defaults a model's context window to roughly 2,000 to 4,000 tokens. An autonomous coding agent like Cline blows past that within two or three tool calls. It reads a file, reads another file, reads the error output, and suddenly the context is exhausted and the model starts hallucinating. By creating a custom Modelfile that extends the context to 32,768 tokens or higher, Cline stops losing track of what it was doing mid-refactor.

What Hardware Do You Actually Need?

The realistic minimum for useful output is a 7-billion-parameter model at Q4_K_M quantization, which requires approximately 6GB of VRAM (video RAM) or 8GB of unified memory. For the 32-billion-parameter models that actually excel at multi-file work, you need approximately 22GB of VRAM or a mini PC with 32GB or more of unified memory.

The performance difference between hardware tiers is substantial. A Beelink GTi14 mini PC with a Core Ultra 9 processor and 64GB of DDR5 RAM can run qwen2.5-coder:32b at around 18 tokens per second, meaning the model generates about 18 words of output per second. A more budget-friendly option like the Beelink SER7 with a Ryzen 7 7840HS processor costs around $370 and runs the 7-billion-parameter model comfortably, though at slower speeds.

Model choice matters more with Cline than it did with Continue because Cline's agentic tasks require actual multi-step reasoning. A 7-billion-parameter model compiles TypeScript or C# on the first try roughly 70 percent of the time. A 14-billion-parameter model represents a meaningful jump in quality for multi-file reasoning and is considered the sweet spot if your hardware can handle it. The 32-billion-parameter model at Q4_K_M is the community gold standard for local coding agents right now, capable of completing multi-file refactors that would have required cloud-based Claude 18 months ago.

What Does This Shift Mean for the Broader Self-Hosted AI Movement?

The Continue.dev shutdown illustrates a broader tension in the open-source AI ecosystem. Tools built as privacy-first alternatives to commercial services can still be acquired by those very commercial services. Developers who invested in Continue's ecosystem now face the choice of migrating to a new tool or accepting that their privacy-focused stack has been absorbed into a commercial product.

The emergence of Cline as a viable replacement suggests that the market for self-hosted, privacy-respecting AI coding tools is real and growing. Developers are willing to invest time in setup and configuration if it means keeping their code on their own hardware. The fact that Cline has 5 million VS Code installs and continues to gain adoption indicates that this isn't a niche concern but a meaningful segment of the developer population.

For developers considering a migration from Continue to Cline, the technical barrier is low. The real work is understanding that the context window fix is non-negotiable and selecting the right model for your hardware tier. Once those pieces are in place, the experience of having a local, privacy-respecting AI coding assistant becomes genuinely useful, not just a theoretical alternative to cloud-based tools.