Merge pull request hud-evals#184 from hud-evals/l/dev-log-updates

L/dev log updates

Author: lorenss-m

docs/core-concepts/architecture.mdx

@@ -11,7 +11,7 @@ HUD is built as a thin orchestration layer on top of MCP, providing structure fo
 ```mermaid
 graph TB
     subgraph "Remote"
-        Dashboard["📊 Dashboard<br/>(hud.so)"]
+        Dashboard["📊 Dashboard<br/>(hud.ai)"]
         API["🔌 MCP Orchestrator<br/>(mcp.hud.so)"]
     end
     
@@ -158,7 +158,7 @@ import hud
 
 with hud.trace("my-evaluation"):
     result = await agent.run(task)
-    # View at hud.so/traces/{trace_id}
+    # View at hud.ai/traces/{trace_id}
 ```
 
 ## Execution Flow

docs/docs.json

@@ -24,7 +24,7 @@
   ],
   "topbarCtaButton": {
     "name": "Dashboard",
-    "url": "https://hud.so"
+    "url": "https://hud.ai"
   },
   "navigation": {
     "versions": [

docs/index.mdx

@@ -45,7 +45,7 @@ graph LR
 ## Why HUD?
 
 - **🔌 MCP-native**: Any agent can connect to any environment
-- **📡 Live telemetry**: Debug every tool call at [hud.so](https://hud.so)
+- **📡 Live telemetry**: Debug every tool call at [hud.ai](https://hud.ai)
 - **🚀 Production-ready**: From local Docker to cloud scale
 - **🎯 Built-in benchmarks**: OSWorld-Verified, SheetBench-50, and more
  - **🔧 CLI tools**: Create, develop, run, and train with `hud init`, `hud dev`, `hud run`, `hud eval`, `hud rl`

docs/quickstart.mdx

@@ -46,7 +46,7 @@ uv tool install hud-python
 Set your API keys as environment variables:
 
 ```bash
-export HUD_API_KEY="sk-hud-..."      # Get from hud.so
+export HUD_API_KEY="sk-hud-..."      # Get from hud.ai
 export ANTHROPIC_API_KEY="sk-ant-..." # For Claude agents
 export OPENAI_API_KEY="sk-..."        # For OpenAI agents
 ```
@@ -67,7 +67,7 @@ from hud.agents import ClaudeAgent
 
 async def main():
     # The trace context captures ALL agent interactions within a "task run"
-    # Everything inside this context shows up as one trace on hud.so
+    # Everything inside this context shows up as one trace on hud.ai
     with hud.trace("quickstart-2048"):
         # Define task with remote MCP environment
         task = Task(

docs/train-agents/quickstart.mdx

@@ -8,7 +8,7 @@ icon: "graduation-cap"
 - HUD API key: Remote training requires authentication. Set `HUD_API_KEY` before running:
 
 ```bash
-export HUD_API_KEY="sk-hud-..."  # get one at https://hud.so
+export HUD_API_KEY="sk-hud-..."  # get one at https://hud.ai
 # Or persist it locally:
 hud set HUD_API_KEY=sk-hud-...
 ```

environments/README.md

@@ -495,7 +495,7 @@ from hud.agents import ClaudeAgent
 from hud.clients import MCPClient
 
 async def main():
-    # `trace` captures *everything* that happens and sends it to hud.so
+    # `trace` captures *everything* that happens and sends it to hud.ai
     with hud.trace("local_test"):
         task = Task(
             prompt="Complete the task",
@@ -524,15 +524,15 @@ async def main():
 asyncio.run(main())
 ```
 
-The `trace` context manager sends a full timeline of agent actions, tool calls, and rewards to hud.so – perfect for debugging.
+The `trace` context manager sends a full timeline of agent actions, tool calls, and rewards to hud.ai – perfect for debugging.
 
 See `examples/01_hello_2048.py` and `examples/task_with_setup_eval.py` for larger end-to-end demos.
 
 ---
 
 ## Phase 4 – Remote Deployment & HUD Runner
 
-**Goal →** the exact same image runs in parallel on hundreds of instances, and exposes more telemetry so the hud.so can visualise the whole lifecycle.
+**Goal →** the exact same image runs in parallel on hundreds of instances, and exposes more telemetry so the hud.ai can visualise the whole lifecycle.
 
 ### 1. Publish your image
 
@@ -595,11 +595,11 @@ async def initialize_environment(session=None, progress_token=None):
     await send(100, "ready")
 ```
 
-Those messages are displayed live on hud.so alongside resource graphs – perfect feedback while you wait.
+Those messages are displayed live on hud.ai alongside resource graphs – perfect feedback while you wait.
 
 ### 4. Live telemetry (`telemetry://live`) (Optional)
 
-Expose a resource named `telemetry://live` exactly like in `environments/browser/src/hud_controller/server.py` to return live url to be displayed on hud.so.
+Expose a resource named `telemetry://live` exactly like in `environments/browser/src/hud_controller/server.py` to return live url to be displayed on hud.ai.
 
 Once all of the above works you can unleash *hundreds* of concurrent agents on your new environment.