We gave AI tools a memory for your architecture. Here's what we learned.

We love Cursor, Copilot, and Claude. But we kept running into the same problem. They don't know your architecture.

They generate code using generic patterns, not your team's actual decisions. And slowly, without anyone noticing, your codebase starts drifting. The repository pattern you agreed on. The state management library you picked after two weeks of debate. The validation approach your senior engineer insisted on.

None of that lives anywhere AI can actually read.

• It lives in Slack threads.

• Old wiki pages.

• Scattered Markdown files.

• Someone's memory.

So we built Hopsule.
Hopsule turns your engineering decisions into structured, enforceable context and injects them directly into your AI tools.

Simple example:

Your team accepts a rule: "Database access must go through the repository layer."

Hopsule records it. Every AI session that follows knows it.

What's included:

GitHub Sync · IDE Addon · CLI Tool · MCP Support · Decision Graph

The mental model is simple:

Humans make architectural decisions. Hopsule stores them. AI follows them.

We just launched our Public Beta and we're genuinely looking for feedback, not just upvotes.

What's confusing?
What's missing?
Where are we wrong?