More and more AI power users are running a hybrid setup: a local model on their laptop for quick, private, or cost-free tasks, and a cloud model like Claude or ChatGPT for heavier lifting. It's a smart approach. But it creates a context problem most people don't anticipate.
Your cloud AI doesn't know what you told your local AI. Your local AI doesn't know what your cloud AI produced last week. Every tool is its own silo, and you're manually re-explaining your context every time you switch.
The solution is a single knowledge base that works with every model in your stack — local and cloud alike. Here's how to build one.
Why People Use Local Models (And Why Context Still Matters)
The reasons for running a local model are compelling: no API costs, complete privacy (your data never leaves your machine), offline access, and speed for simple tasks. Tools like Ollama, LM Studio, and Jan make it genuinely easy to run capable models locally.
But "local" doesn't mean "simple." The same frustration you feel with cloud AI — re-explaining context, starting from scratch every conversation — exists with local models too. And it's actually worse, because local models tend to have smaller context windows. Where Claude might give you 200,000 tokens of context, a local model on your laptop might give you 8,000 to 32,000. That means context management isn't just convenient — it's critical.
The Token Problem Is Bigger for Local Models
Here's where document format becomes especially important. A Word document or PDF that takes 68,000 tokens simply won't fit in an 8,000-token context window. Not even close.
The same content in markdown takes roughly 22,000 tokens — about 70% less. Even that might be tight for smaller local models, but it's the difference between impossible and workable.
This is why building your knowledge base in markdown isn't just a best practice — it's the only approach that works consistently across both local and cloud models. Cloud models have room to accommodate bloated formats. Local models don't.
One Knowledge Base, Every Model
The core idea is simple: instead of maintaining separate context for each AI tool you use, you maintain one organized knowledge base and share the relevant parts with whichever model you're working with at the moment.
AI Context Keeper is built for exactly this. Every file and folder gets a shareable link — a URL that returns the document's markdown content when accessed. You paste that link into any AI tool that accepts text input, and the AI loads your context instantly.
This works with cloud models (Claude, ChatGPT, Gemini, Perplexity) and with local models that support web browsing or URL fetching in their interface. For local models that don't support links directly, you can copy the markdown content from ACK and paste it — but now you're pasting clean, token-efficient markdown instead of a bloated Word doc or PDF.
A Practical Tiered Workflow
Here's what a hybrid local/cloud setup looks like with a shared knowledge base:
Quick tasks → local model. Drafting a short email, summarizing a meeting note, answering a simple question. You paste your "About Me" document and a relevant context file, and the local model handles it quickly with no API cost.
Heavy lifting → cloud model. Complex analysis, long-form writing, research synthesis. You share your full context folder via link — brand guidelines, project brief, relevant research — and Claude or ChatGPT does the deep work with a larger context window.
Valuable outputs → saved back to ACK. Whether the output came from a local model or a cloud model, if it's worth keeping, it goes back into your knowledge base. Use the Chrome extension for cloud tools, or copy-paste for local tools. Either way, it joins your growing library and becomes available to every future conversation.
Setting Up Your Knowledge Base for a Hybrid Stack
The setup is the same whether you're using local models, cloud models, or both. Start with these five core documents — they cover the context you'll reach for most often regardless of which AI tool you're using.
Then organize by how you actually work. A few folder structures that work well for hybrid setups:
- By use case: Quick Reference (for local model tasks) / Deep Work (for cloud model tasks) / Research / AI Outputs
- By project: Active Projects / Reference / Templates — with each project folder containing everything either model would need
- By topic: Brand / Product / Clients / Strategy — share the whole relevant folder regardless of which tool you're using
The key insight: your folder structure should mirror the shape of your work, not the shape of your AI tools. The knowledge base sits above the tools. Each tool just gets whatever slice of it is relevant to the task at hand.
Local Models and Privacy: The Right Mental Model
One of the main reasons people run local models is privacy — keeping sensitive data off cloud servers. A shared knowledge base doesn't compromise this if you're thoughtful about it.
Sensitive documents (client data, confidential strategy, personal information) can stay in a separate folder that you only ever share with your local model — never paste into a cloud tool. Your general context documents (brand voice, product overview, communication preferences) are fine to share anywhere.
ACK's folder structure makes this natural. Keep a "Local Only" folder for sensitive context and a "Shared" folder for everything else. You always control what goes where.
When Claude Projects or ChatGPT Memory Isn't Enough
If you're using local models, platform-specific features like Claude Projects or ChatGPT's memory don't help you at all. Those features are locked to their respective platforms. Your local Llama model has no idea what's in your Claude Projects.
A cross-platform knowledge base is the only approach that gives you consistent context across every model in your stack — from the smallest quantized model running on your laptop to the most capable frontier API. Same documents, same links, same context. Every model, every time.
Ready to build a knowledge base that works everywhere? Start free — it takes about 30 minutes to set up your first five documents, and you'll immediately feel the difference across every AI tool you use.