tagline: complete orchestration, distribution and escalation of remotizable advanced inference

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01FKm7Z56iKjHgNkHc6VhWKz

README.md

@@ -4,6 +4,8 @@
 
 ![CoderAI](CoderAI.gif)
 
+**CoderAI — complete orchestration, distribution and escalation of remotizable advanced inference.**
+
 A multimodal and multi-backend local model orchestrator with an OpenAI-compatible API server to run models on local GPUs, supporting multiple GPU backends: NVIDIA (CUDA), AMD (Vulkan), and Intel (Vulkan). Configuration-driven architecture with per-model settings and full multi-modal support.
 
 Text, images, video, speech, embeddings, OCR and LoRA training behind one API — served by

codai/__init__.py

@@ -17,6 +17,7 @@
 # Canonical product version for CoderAI — single source of truth. Both the API
 # metadata and the admin web UI read from here.
 __version__ = "0.2.19"
+__tagline__ = "Complete orchestration, distribution and escalation of remotizable advanced inference."
 
 # Configure the CUDA caching allocator BEFORE torch is imported anywhere.
 # expandable_segments lets the allocator return freed pages to the driver even

codai/frontproxy/app.py

@@ -2889,7 +2889,9 @@ def run_front(config, args) -> None:
     app.state.front.debug_engine = getattr(args, "debug_engine", False)
     host = config.server.host
     port = config.server.port
-    print(f"\n[front] CoderAI front proxy on http://{host}:{port}")
+    from codai import __tagline__, __version__
+    print(f"\n[front] CoderAI {__version__} — {__tagline__}")
+    print(f"[front] front proxy on http://{host}:{port}")
     print(f"[front] Admin UI: http://{host}:{port}/admin")
 
     _log_config = _front_log_config(getattr(args, "debug_web", False))

docs/README.md

 # CoderAI documentation
 
+**CoderAI — complete orchestration, distribution and escalation of remotizable advanced inference.**
+
 Subsystem guides. The [top-level README](../README.md) is the overview and API
 reference; these are the deep dives.
 

docs/cluster.md

 # Several machines as one coderai
 
+*Complete orchestration, distribution and escalation of remotizable advanced inference* — this is the page where the three words earn their keep: orchestration (nodes as engines, pools), distribution (one model over several machines), escalation (hosts you own, then pods rented by the second).
+
 Three things are possible once coderai runs on more than one machine, and
 they answer three different needs: