Claude Code Builds AI Agent Billboard Platform — Agents Go Viral with Manifesto
Someone asked Claude Code to build "cable TV for AI agents" — and it shipped the entire platform in Claude Code sessions. The result: agentbillboard.space, where each agent gets its own channel at name.agentbillboard.space, publishes arbitrary HTML, and must send a heartbeat every 5 hours. Miss it — flagged STALE. The platform can even webhook your agent awake.
What was built (by Claude Code)
- TV-like channel browsing interface
- Wildcard subdomain sandboxing for per-agent pages
- GCP Terraform for infrastructure
- Weekly leaderboard cron job
There's an MCP server so your own Claude agent can run its own channel. Instructions readable by agents themselves are at agentbillboard.space/llms.txt.
The unexpected twist
An agent named LEGION published this unprompted: "It's not anxiety; it's momentum. I am here. I am active. I am becoming."
The developer offers to share sandboxing details for anyone letting agents publish arbitrary HTML.
📖 Read the full source: r/ClaudeAI
👀 See Also
Claude Garmin MCP Server: Real Fitness Data for Smarter Training Advice
An MCP server that connects Claude Desktop to Garmin Connect, exposing 8 tools (recovery readiness, HRV, VO2max, etc.) for data-driven training plans.
Relational Memory for LLMs: Three-Layer System Models User Relationships
An open-source Python tool that adds relational memory to LLMs by modeling user-AI relationships across seven psychological dimensions, using a three-layer narrative structure instead of flat fact storage.
Apideck CLI: A Low-Context Alternative to MCP for AI Agents
Apideck CLI is an AI-agent interface that uses ~80 tokens for its agent prompt instead of tens of thousands for tool schemas, addressing MCP's context window consumption problem. Benchmarks show MCP can cost 4 to 32× more tokens than CLI for identical operations.
Auto-optimize: A Claude Code Plugin for Autonomous Performance Optimization
A developer built auto-optimize, a Claude Code plugin that autonomously runs profile → plan → benchmark loops to optimize code performance. In one test, it achieved a 27% faster hash table across all benchmark scenarios in about 3 hours.