Remove model tree, add Coming Soon section for app

README.md

@@ -50,33 +50,9 @@ GGUF quantizations of **Sixpert K2** for Ollama, LM Studio, jan, KoboldCpp, and
 
 Sixpert K2 is a 9B parameter mixture-of-experts (MoE) model designed for deep reasoning, complex agentic workflows, and multimodal understanding. Built with a 1M-token context window and fine-tuned on 500M+ reasoning tokens, it represents a significant leap in the 9B parameter class.
 
-## Model Tree
+## Coming Soon
 
-```
-Sixpert Model Family
-├── Sixpert K1 (8B) — Multimodal reasoning & agentic AI
-│   ├── Base: Qwen 3.5 architecture
-│   ├── Fine-tuned on 500M+ reasoning tokens
-│   ├── Self-scaffolding RL training (Ornith 1.0 inspired)
-│   ├── Domains: coding, trading, cybersecurity, biomedical, reasoning
-│   └── Frozen LLM Judge for: K2 training
-│
-├── Sixpert K2 (9B MoE) — Deep reasoning & complex agentic workflows
-│   ├── Base: Sixpert K2 base (MoE)
-│   ├── Fine-tuned on 500M+ reasoning tokens
-│   ├── Self-scaffolding RL training (Ornith 1.0 inspired)
-│   ├── Domains: coding, trading, cybersecurity, biomedical, reasoning
-│   └── Frozen LLM Judge for: K1 training
-│
-└── Sixpert K3 (upcoming) — Self-improving AI agent
-    ├── Android app (Google Play Store)
-    ├── Integrated sandbox + Browserbase
-    └── Self-improving architecture (Ornith 1.0 self-scaffolding RL)
-
-Cross-Model Collaboration:
-  K1 ←→ K2: Scaffold sharing, cross-model verification, self-play curriculum
-  K1 judges K2 │ K2 judges K1 (frozen LLM judge — never updated during training)
-```
+🚀 *Sixpert AI app coming soon to Google Play Store — a self-improving AI agent with sandbox, Browserbase integration, and Ornith 1.0 self-scaffolding RL built in.*
 
 ## Self-Improving Training Architecture