Most AI development workflows have a hidden inefficiency:
Project understanding disappears between sessions.
The result is familiar:
- Repeated explanations
- Lost decisions
- Fragmented reasoning
- Context switching overhead
What We Changed in Contorium v3
We unified our architecture around a single concept:
Project Intelligence Layer (PIL)
Instead of focusing on chats, prompts, or agent memory, PIL focuses on preserving project intelligence itself.
The system records:
STATE
INTENT
DECISION
WHY
and tracks:
TIMELINE
IMPACT
CONFIDENCE
EVOLUTION
PROVENANCE
Runtime Contract
All runtimes now expose the same capabilities:
Capture
capture_focus
capture_note
capture_decision
Inspect
inspect_state
inspect_intent
inspect_decision
inspect_why
...
Transfer
transfer_context
transfer_intelligence
transfer_handoff
This creates a consistent interface across:
- IDE
- MCP
- CLI
while sharing a single local intelligence repository.
Why This Matters
As AI tooling becomes more capable, continuity becomes more important than raw intelligence.
The challenge is no longer generating code.
The challenge is preserving understanding.
Contorium v3 is our attempt to make project intelligence a first-class development artifact.
⸻
GitHub: https://github.com/ContoriumLabs/contorium
Website: https://www.contorium.dev
Top comments (1)
The continuity problem is underrated.
A lot of AI systems can generate useful output, but preserving the reasoning behind decisions is much harder. Once context disappears, teams end up repeating work or rediscovering conclusions they already reached.
I particularly like the focus on capturing not just state and decisions, but the "why" behind them. That often ends up being the most valuable piece of information months later.