Yesterday, Thursday, November 12, I got an unexpected lesson in AI continuity planning. Around lunchtime here in Central Texas, Microsoft 365 Copilot became unavailable to me. Outlook, Teams and other Microsoft 365 services were also caught up in a widespread service disruption. At the time I could only say to myself: “I wonder how Copilot goes bye-bye in this day and age?”
Why (and for How Long)
Copilot Goes Bye-Bye
Microsoft tracked the broader event as MO1550821. Reporting based on Microsoft’s service updates says that incident began at 1:26 PM Eastern, or 12:26 PM Central. The trouble was not a sudden flood of users. Microsoft said a number of servers unexpectedly became unavailable, putting extra strain on the infrastructure still standing. Automated health-check failures and an overwhelmed core process complicated recovery. The outage continued for many hours and into November 13 for some users.
In my case, that meant Copilot was essentially gone from noon through the end of my workday. The timing was especially inconvenient because I had four roughly 1,000-word pieces to produce for a hurry-up project (submissions starting NOW). Their themes were modernization, hybrid IT strategy, securing infrastructure for AI, and managing growing IT estates without growing budgets apace.
Necessity being the mother of invention, I decided this was a fine time to find out whether the Lenovo ThinkStation P3 Ultra could become its own AI writing workbench. Google AI helped me get there. By the end of the exercise, I had a local LLM workflow and all four articles completed. Sometimes an outage provides motivation that a normal workday does not.
Ollama Provides the Local AI Engine
The foundation is Ollama, which runs open models locally on Windows. I installed the Windows build and confirmed that its local API was available at http://127.0.0.1:11434. Ollama’s Windows documentation says the application runs in the background after installation and makes the ollama command available in PowerShell and other terminals.
My first check was simple: ollama list. There were no models aboard yet. I then pulled qwen3.6 and wound up using qwen3.6:latest as the model behind this writing experiment. Once downloaded, that model could run on the P3 Ultra instead of depending on somebody else’s cloud staying healthy and accessible.
Ollama for Windows: https://ollama.com/download/windows
AnythingLLM Turns Model Into Workspace
Ollama supplied the engine, but I needed a practical document-oriented front end. AnythingLLM Desktop filled that role. Its workspace approach let me collect source material for the project, embed it, retrieve relevant files, and converse with the model around a defined body of documents.
Installation presented one Windows-like speed bump. Smart App Control blocked AnythingLLM from launching on this particular PC. I chose to disable SAC in Windows Security so I could proceed. That is a consequential security setting, not a step I recommend taking casually. Here, it was a calculated tradeoff on a machine I manage.
There was another wrinkle. AnythingLLM now includes its own local model runner, but I specifically wanted Ollama. In Settings, under AI Providers and LLM, I selected Ollama and pointed the base URL at http://127.0.0.1:11434. I also expanded the model context window to 32,768 tokens so larger source collections had useful breathing room. I was a bit non-plussed running out of tokens in AnythingLLM’s default workspace, let me tell you!
AnythingLLM Desktop: https://anythingllm.com/download
Blank Model Setting Throws a Curveball
My most interesting configuration glitch produced a wonderfully terse error: “model is required.” AnythingLLM’s newer interface auto-saves selections, so there was no missing Save button to blame. Instead, I discovered that my workspace had been created before I pulled qwen3.6. It had retained a blank model override.
Opening the workspace’s Chat Settings and explicitly choosing Ollama plus qwen3.6:latest cleared the problem. A fresh chat then caused Ollama to load the model into memory. That was the moment the collection of installers, settings and URLs turned into a functioning system.
From Experiment to 4 Finished Articles
For the actual writing job, I loaded the project’s text sources into the AnythingLLM workspace and used Save and Embed. Rather than pinning a huge pile of material into every prompt, I let retrieval do the heavy lifting. Then I worked through the articles section by section, keeping each piece focused on its assigned IT theme.
The result surprised me. This was not merely a laboratory demo showing that a local model could answer a prompt. The P3 Ultra became a useful production tool. In one afternoon, the local Ollama/AnythingLLM combination helped me finish four substantial articles while my normal cloud assistant was unavailable.
I am not giving up Copilot. Far from it. But yesterday changed my view of local AI. The lesson is less about replacing one assistant with another than eliminating a single point of failure. Cloud AI is convenient and powerful right up until the cloud goes away. Now, when that happens, the P3 Ultra has another answer: keep the sources local, load the model, and keep writing.
Here in Windows-World, it’s best to be ready for anything and everything. Sooner or later, that readiness will pay off. Long live local LLMs!
