Using ntfy for OpenClaw agent notifications

A Reddit user describes using ntfy (specifically the self-hosted version of ntfy.sh) as a push notification system for OpenClaw agents instead of Discord or Telegram bots.
Setup details
The user runs their OpenClaw gateway headless on a Lightnode VPS and needed reliable notifications when long scripts finish or if the agent crashes. They set up ntfy serve on the same VPS and pointed their domain to it using Caddy.
How it works
ntfy uses a basic HTTP pub-sub model. Instead of setting up bot channels, background agents make HTTP POST requests to ntfy, which then pushes notifications to devices. The user reports using it successfully with spare phones and smart devices.
Benefits mentioned
- Easier to set up on devices without Discord/Telegram
- Completely free and self-hosted
- Cleaner than cluttering a Discord server with automated logs
- Agents can potentially set it up themselves with one sentence (given permissions)
The user specifically mentions they're using "ntfy serve" (the self-hosted version) and found it performed well enough to share their positive experience.
📖 Read the full source: r/openclaw
👀 See Also

Pre-coding routine with Claude Code: 5 MCP servers before writing a line
A developer shares a 60-90 second routine using 5 MCP servers (memory, codebase graph, Tavily search, Context7 docs) and safety hooks to dramatically reduce hallucinations and wasted edits.
A sub-agent reply is not a completion receipt: orchestrator verification checklist
OpenClaw's sessions_spawn is non-blocking—a reply doesn't mean done. Use yield and Task Flow, and reconcile child state to avoid false success.

Routing Agent Subtasks to Cheaper Models Dropped Cost from $18 to $4 on Same Refactor
A developer cut agent run costs from $18 to $4 by routing routine subtasks (lint, rename, config edits) to cheap models like DeepSeek V4 Pro and Tencent Hunyuan Hy3, reserving Opus 4.7 for complex reasoning.

Reducing Claude Hallucinations with Pre-Output Prompt Injection
A Reddit post details a method to cut Claude AI hallucinations by half using a pre-output prompt that forces the model to record uncertainties and next steps before responding. The approach involves adding specific markdown instructions to Claude's system prompt and creating a Python script.