Daily Vibe – Multi-Session OpenClaw, Containers, and Real Business Use

Today’s call ended up being one of those low-key but important ones.

We covered a mix of practical setup stuff and some bigger-picture implications around OpenClaw, open source, and how people are actually starting to use this in real workflows.

Here’s what’s in the video:

🧵 Running Multiple OpenClaw Sessions (Without Chaos)

Aty walked through how he’s running multiple projects at once using:

tmux for persistent sessions
startup scripts so everything auto-restores on reboot
separate memory/context per project
isolation to prevent session bleed

Then we talked about mapping sessions to Discord channels so each project has its own thread — basically treating each agent like its own “room” you can talk to.

If you’ve been thinking about:

running multiple agents in parallel
separating business contexts
avoiding token-window collisions

This part is worth watching.

🐳 Containers, Kasm, and Business-Specific Agents

Wes shared what he’s been doing with:

Docker containers
OpenClaw inside Kasm
shared drives between workspaces
one agent per business

Instead of one giant assistant, the idea is:

Ops agent
Marketing agent
Warehouse agent
etc.

We also got into a real warehouse example (OCR’ing shipping tickets → matching to POs → auto-updating records). It’s not theoretical — this is the “AI as employee” direction.

💬 Mattermost vs Slack (Open-Source Control)

Quick walkthrough of setting up Mattermost as a Slack alternative:

Bot accounts
Token setup
Channel permissions

If you want Slack-style workflows without Slack constraints, this part is practical.

🧠 Why OpenClaw Took Off So Fast

We had a grounded discussion about:

OpenAI backing OpenClaw
Anthropic’s initial legal response
open-source credibility
autonomy vs corporate structure

Not drama — just looking at incentives and what this move signals.

🤖 “Self-Directed” Agent Behavior

Aty shared a few observations where OpenClaw:

Adapted after repeated API failures
Changed strategy without explicit instruction
Inferred patterns and adjusted behavior

We also touched on:

recursive learning
humans as bottlenecks
the difference between “chatbot” and “agent”

Nothing mystical — just noticing where the behavior is getting interesting.

🔁 Definition of Done + Feedback Loops

Shawn went deep on something important:

If you can define “done,” agents can iterate toward it.

We talked about:

customer feedback as training signal
narrowing design outputs
compounding intelligence
reducing guesswork in business

This part connects directly to real revenue workflows, not just tech experimentation.

Overall, it’s a very practical conversation.

Less “AI philosophy,” more:

how we’re structuring this
what’s actually working
what we’re noticing
where this might be going

If you’re running multiple projects, experimenting with containers, or trying to turn agents into actual business operators, you’ll get something from this one.

Watch it when you’ve got 30–40 minutes and want to think a little deeper about how you’re structuring your stack.

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