Yesterday we shipped QonQrete v0.6.0-beta, and this one is a fundamental architectural shift.
Most agentic AI systems still rely on context stuffing — shoving entire codebases into every prompt. It works… but it’s slow, expensive, and completely non-scalable.
QonQrete now does context differently — and locally.
🔥 Dual-Core Architecture
We split “context” into what actually matters:
🦴 Qompressor (Skeletonizer)
Creates an ultra-low-token structural skeleton of the codebase (signatures, imports, docstrings).
→ Near-zero token cost, full architectural awareness.
🧭 Qontextor (Symbol Mapper)
Builds a machine-readable YAML map of symbols, responsibilities, and dependencies.
→ Deep, queryable project context without raw code flooding.
💸 CalQulator (Cost Estimator)
Every task (briQ) gets a token + cost estimate before execution.
→ No more surprise API bills. Full budget transparency.
📊 The Results
| Metric | Improvement |
|---|---|
| Tokens Used | 96% fewer |
| Cost Reduction | ~25× |
| Execution Speed | ~3× faster |
| Context Handling | Local-first |
This isn’t prompt optimization.
This is architectural deconstruction of context itself.
🧠 Why This Matters
Agentic AI doesn’t scale by sending more tokens.
It scales by understanding structure, intent, and relevance — locally, deterministically, and auditable.
QonQrete is now:
- ✅ Local-first
- ✅ File-based
- ✅ Budget-aware
- ✅ And finally economically sane for real projects
🔗 GitHub: github.com/illdynamics/qonqrete
🧪 v0.6.0-beta is live — feedback & contributors welcome.