The hardening stuff you did probably broke the gateway. OpenClaw is fragile on hardened systems — it needs specific ports, memory access, and permissions that typical security lockdown removes. Before you reinstall, try this: `openclaw gateway stop` and `openclaw doctor --fix`. If that doesn't work, you likely need to walk back some of the hardening (firewall rules, file permissions, or ulimit settings). What security procedures did you run? That'll help pinpoint what got locked down.

Congrats on the Mac Mini — it's a great starting point. Here's the fastest path I'd take if I were starting over: Step 1 — Install OpenClaw (15 min) npm install -g openclaw, then openclaw init. That's it. Skip NemoClaw for now — extra abstraction layers add confusion when you're learning. Go straight to the source. Step 2 — Get one agent working well (1-2 days) Don't jump to multiple agents yet. Get your main agent solid first: • Create SOUL.md (identity + decision principles) • Create MEMORY.md (lean index of what matters) • Connect it to one channel (Telegram is easiest to start) • Give it one real task from your business and see how it handles it Step 3 — Add cron jobs (day 3-4) This is where the "self-improving" part starts. Schedule recurring tasks: • Morning brief: review priorities, check email • Evening review: log what happened, update memory • These crons are what make the agent persistent and learning between sessions Step 4 — Add sub-agents for heavy tasks (week 2) Once the main agent is solid, spawn sub-agents for isolated work. Main agent = the brain that decides what to do. Sub-agents = disposable workers for research, analysis, content generation. Step 5 — Add the "self-improving" layer (week 3+) This is just a cron that reviews its own performance. Mine runs nightly — reads the day's notes, identifies gaps, proposes improvements, and updates its own rules. Re: base model Mac Mini — the base M4 has 16GB RAM which is enough for OpenClaw + cloud models (Anthropic/OpenAI). You won't be running large local models on 16GB, but that's fine — cloud models are better for your use case anyway. Use Ollama with a small model (llama3.1:8b) as a fallback only. Biggest mistake to avoid: trying to build the whole system at once. Each step should be working and useful before you add the next one.

I run a Mac Studio with OpenClaw + Ollama for exactly this — here is what I would do knowing what I know now. Skip NemoClaw. Just install OpenClaw directly via npm. It is the simplest and most reliable path. Step 1: Install OpenClaw on your Mac Mini. Connect Telegram. Get one agent working well for a specific task (email triage, research, whatever your biggest time sink is). Step 2: Install Ollama and pull a model like qwen2.5 for routine tasks. Configure OpenClaw to route simple work to the local model and reasoning-heavy work to Claude via API. This alone cut my API costs by 95%. Step 3: Only THEN start adding sub-agents. One per business function. Each with a clear task, input source, and output destination. Do not try to build a self-improving system on day one. Build one useful agent, make it reliable, then add the next one. The self-improving part comes naturally once you give agents persistent memory (MEMORY.md + daily log files) and a heartbeat routine. They start learning your patterns and improving their own workflows over time. Biggest mistake I see: people try to build 5 agents before any single one works reliably. Start with one. Make it bulletproof. Then scale.