Paste your /ctx URL into any local model running Ollama or LM Studio. Persistent context that never touches someone else's server.
Private. Portable. Not locked into any platform — built to work 15 years from now.
Every Ollama session starts completely cold. No context carry-over. No way to preserve what your model learned yesterday.
Claude remembers you across sessions, but your local Llama 3 model has no persistence. You chose local for privacy, not for amnesia.
You run Ollama to avoid cloud lock-in. But that means losing context, losing memory, losing efficiency. Privacy shouldn't cost productivity.
Your context isn't locked to one AI. Any model—Ollama, LM Studio, GPT4All—can read your /ctx URL and load your persistent context instantly.
Context stored in Firestore behind a read token. Not in OpenAI's training. Not in Anthropic's pipelines. Deterministic, not ML.
Run Ollama locally. Load CacheTank context via URL. Your model runs on your machine. Your data stays yours. No cloud dependencies.
Paste your knowledge, instructions, or context. CacheTank structures it instantly.
Add the URL to Ollama, LM Studio, or any local model. Context loads automatically in every session.
Your local model now has memory. No cloud lock-in. No restarting from zero.
Yes. CacheTank stores your context as a /ctx URL that any system can read—including Ollama, LM Studio, and any local LLM that accepts a context source. Simply paste the /ctx URL into your Ollama system prompt or context field, and your persistent context loads automatically in every session.
Store your context in CacheTank using the Cache it feature, get your /ctx URL, and add it to your local model's system prompt or context window. Every time you start a new session in Ollama or LM Studio, that context loads automatically—giving your local model persistent memory without cloud dependencies.
Absolutely. CacheTank context is stored in Firestore, not in any AI company's infrastructure or training pipelines. Your context never touches Claude, ChatGPT, or any other AI model unless you explicitly send it. Perfect for privacy-first local LLM setups.
Create your context in CacheTank, copy your /ctx URL, and paste it into LM Studio's system prompt or context field. CacheTank will load your saved context automatically in every new session, eliminating the cold-start problem of local LLMs.
A privacy-first context layer stores your AI context—knowledge, instructions, memories—separately from any AI model, cloud service, or training pipeline. CacheTank is deterministic (rules-based, not ML), doesn't train on your data, and lets you load context into local models like Ollama without exposing anything to cloud providers.
Pause anytime — 1, 2, 3, or 4 weeks. Your library waits for you.