Imagine a modern, AI-forward product team.

The designers use Figma and Lovable. The engineers use Claude Code and Cursor. The team lead uses ChatGPT. Thats a lot of tools, each representing a siloed set of user data.

As models converge in capability, the differentiator is no longer the LLM. It is the quality of the user’s prompt and the relevance of the context they provide. The frontier labs are recognising that their models are becoming commodities and that their moat will be long term memory and context that they build up with their users.

Small teams can often maintain a mental map of the current context of their work. In a brand new startup the design team, engineering team, leadership team, are two people sitting next to each other at a desk. The state of the entire company resides within the two brains of its founders. With the passing of time and as the enterprise scales this becomes much harder. This institutional knowledge is transferred into a more permanent form in the shape of spreadsheets, databases, word documents, pdfs, email threads.

But there’s a disconnect between this vital context and the team’s AI tools.

Manually uploaded context becomes stale in each individual’s AI tool, valuable prompts are trapped in individual chat histories. Overloading the LLMs fails to work as they begin to suffer from “context rot.” As you flood a prompt with more data, the model’s ability to accurately recall specific information actually decreases.

Minnas is a tool that aims to fix this problem: https://minnas.io

Minnas is a central hub for a team’s prompts and context. It allows you to store, manage, and share your knowledge in one place.

Instead of copy-pasting context into every new window, Minnas provides a single source of truth. Your team stays aligned, and your AI outputs stay accurate.

I made a demo video below: