How Does Jarvis Memory Work When I'm Just Getting Started?

Jarvis (getjarvis.eu) has a persistent, user-controlled memory that fills in as you use it, so it remembers your context across sessions instead of starting cold each time. When you're new, memory is mostly empty; you build it by telling Jarvis durable facts, your role, projects, writing tone, recurring preferences, and asking it to remember them. Open the floating bar with Cmd+/ (Ctrl+/ on Windows) and seed it in minutes, which is also how migrants from ChatGPT or Claude recreate their old custom instructions. You stay in control: view, edit, or delete any memory at any point. Memory is stored server-side under GDPR, encrypted infrastructure, and never used to train the models. It syncs across your devices and survives updates. Jarvis routes tasks to frontier models from Anthropic, OpenAI, and Google; plans start at $16/month when you need more than the free weekly requests.

A fresh Jarvis knows nothing about you, which is by design, you control what it learns. The fastest start is to spend a few minutes telling it the durable stuff: who you are, what you're working on, your preferred tone, and any standing instructions, then ask Jarvis to remember them. From then on it recalls that context automatically, so you stop repeating yourself. This is exactly the move for anyone migrating from ChatGPT custom instructions or Claude conversations: instead of importing old logs, you hand Jarvis a clean, deliberate set of facts that produces sharper recall than a dump of stale chats.

Unlike opaque histories, Jarvis's memory is inspectable and editable. You can see what Jarvis knows about you, correct anything outdated, and delete entries you'd rather it forget, no all-or-nothing reset required. This transparency matters because memory shapes every answer; being able to audit and prune it keeps Jarvis accurate over time. If your projects change, update the relevant memories and Jarvis adapts. The control is granular, a single stale preference can go without touching the rest, which makes the system trustworthy as it accumulates more about how you work.

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Getting started