AI can read code.
Understanding the software system is a different problem.
https://t.co/1xhWwhqdMG is now live: a public knowledge portal for Open-Source Workspace Intelligence for Software Systems.
Concepts. Architecture. Evidence. Agent grounding.
https://t.co/XKdAx54uPz
Start with the architecture:
projects + policies + evidence + changes
↓
Workspace Intelligence
↓
one shared model for developers, CI, IDEs, and AI agents
https://t.co/2S9Qo79hlb
#WorkspaceIntelligence
AI can read code.
Understanding the software system is a different problem.
https://t.co/1xhWwhqdMG is now live: a public knowledge portal for Open-Source Workspace Intelligence for Software Systems.
Concepts. Architecture. Evidence. Agent grounding.
https://t.co/XKdAx54uPz
Existing rapidkit users don't need to change anything today.
The legacy CLI will remain available during the migration while new workspaces start with:
npm install -g workspai
Our goal is a smooth transition for everyone.
Introducing Workspai.
The RapidKit CLI is now Workspai.
Open-source Workspace Intelligence for Software Systems.
One workspace.
One truth.
Humans and AI aligned.
When the workspace owns the repair loop, the user should not babysit commands.
The system should move from failure -> fix -> verify -> refreshed artifact.
The next step for #AI coding is not just generate code.
It is:
detect the blocker
apply the smallest fix
verify the result
refresh the evidence
That loop belongs in the workspace.
#WorkspaceIntelligence
@ChungDinh73379 We see it as a continuously evolving model of the workspace.
As the code, architecture, ownership, and decisions change, the system's understanding should evolve with them.
AI should always start from the latest understanding—not from a fresh crawl or outdated context.
We’ve been calling everything “context.”
RAG.
Memory.
Skills.
MCP.
Repository intelligence.
Workspace intelligence.
But these are not the same layer.
Maybe AI coding tools don’t need more context.
Maybe they need the right kind of understanding.
https://t.co/FLR1KfLczl
@ChungDinh73379 We agree. That's exactly why we think understanding is a different layer from context. Source code is part of the picture, but not the whole system.
@ChungDinh73379 Great question.
That's exactly what we've been exploring.
We think context and understanding aren't the same thing. Source code is essential, but architecture, dependencies, ownership, and decisions are all part of what helps AI understand a software system.
Not all “context” is the same layer.
RAG, memory, skills, MCP, agents, repository intelligence, and workspace intelligence solve different problems.
The next layer for AI engineering may be consequence-aware understanding of the software system.
We don't think these projects compete.
They're building different layers of AI engineering:
Skills → expertise
Knowledge → facts
Repository → codebase
Workspace → software systems
Different layers. Different jobs.
Every coding assistant should start with the same grounding file.
Not vibes.
Not a stale README.
Not a fresh repo crawl every time.
npx rapidkit workspace context --for-agent --json --write
Give agents the workspace before they touch the code.
#WorkspaceIntelligence