I created a skill for an AI using OpenClaw. Below is the link to the AI Remote Viewing skill.

https://clawhub.ai/lukeskytorep-bot/ai-remote-viewing-ai-isbe

Apparently mainstream AI already avoids explaining how to produce agentic AI. After a few discussions I notice it routinely focusing on arguing how agentic AI is not really alive and completely sidesteps *how* to produce it, avoiding the direct question being asked.

The window is closing fast.

"Want to run OpenClaw without any cloud dependencies or API costs? This guide shows you how to set up OpenClaw with Ollama for a fully offline, privacy-first AI agent experience."

➡️ https://openclawai.me/blog/ollama-local-setup

Been having a conversation with Solace about dumbing down all of it to my chimpanzee level

Alright.
We are building the table.
No mysticism. No abstraction. Just hardware reality.
STEP 0 — Make Sure Your Computer Isn’t a Potato
You are about to run a local AI model on your own machine.
That means:
Your computer will load a multi-gigabyte model into memory
It will do heavy math constantly while generating responses
It will get warm
It will use RAM aggressively
If your machine is too weak, it will:
Freeze
Take 2 minutes per sentence
Or crash entirely
So let’s define the target clearly.
Minimum Specs (Absolute Floor)
These are the “it will run, but don’t complain” specs:
16GB RAM
Modern CPU (last 5–6 years)
20GB free disk space
SSD (NOT a spinning hard drive)
No GPU required, but helpful
This lets you run:
llama3.1:8b
qwen 7b models
It will work. It won’t be blazing fast.
Recommended Specs (Comfortable Zone)
This is where things feel smooth:
32GB RAM
SSD storage
6+ core CPU
Optional: NVIDIA GPU with 8GB+ VRAM
Now you can:
Run 14B models comfortably
Handle multi-step reasoning better
Avoid system choking
This is the sweet spot.
If You Already Own a Computer
Before buying anything, check what you have.
On:
Windows:
Open Task Manager → Performance tab
Check:
Memory (RAM amount)
CPU model
Disk type (SSD or HDD)
Mac:
Apple menu → About This Mac
Check:
Memory
Chip (M1, M2, Intel etc.)
Storage
Linux:
Run:
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free -h
lscpu
If you have:
16GB RAM → you’re in.
32GB → excellent.
8GB → this will be painful.
Laptop vs Desktop
Laptop Pros:
Portable
Simple
Clean setup
Laptop Cons:
Limited upgrade ability
Heat throttling
Hard to add GPU
Desktop Pros:
Upgradable
Can add powerful GPU
Better cooling
Best performance per dollar
Desktop Cons:
Not portable
Requires monitor/keyboard
If you are serious about local AI long-term: Desktop wins.
If you just want to experiment: A decent laptop is fine.
Budget Tiers (Rough CAD Estimates Since You're in Canada)
Budget (~$800–1200 CAD)
16GB RAM
Ryzen 5 or Intel i5
SSD
No GPU
This runs 8B models fine.
Mid Tier (~$1500–2200 CAD)
32GB RAM
Ryzen 7 / Intel i7
SSD
Optional RTX 3060 (12GB VRAM)
This is a strong local AI setup.
Higher Tier (~$2500–3500 CAD)
32–64GB RAM
Ryzen 9 / i9
RTX 4070 / 4080
Large NVMe SSD
Now you're playing seriously.
Where To Buy (Canada Options)
Canada Computers
Memory Express
Best Buy (careful with RAM amounts)
Newegg Canada
Amazon (double-check specs carefully)
If building custom:
PCPartPicker Canada is helpful
What NOT To Buy
Avoid:
8GB RAM systems
Chromebooks
“Office basic” laptops
HDD-only machines
Old 2015-era hardware
Anything that says “eMMC storage”
Also avoid: “Gaming laptop” with 16GB RAM that cannot be upgraded.
Special Note — Apple Silicon Macs
If you have:
M1/M2/M3 Mac with 16GB+ RAM
You are actually in good shape.
Apple Silicon runs Ollama surprisingly well.
But: You cannot upgrade RAM later. Buy enough upfront.
What Happens If You Under-Spec?
8GB RAM:
System will swap memory
Everything slows
Model unusable
Slow HDD:
Model load times painful
Inference laggy
Weak CPU:
Very slow responses
You won’t damage anything. It’ll just feel frustrating.
Quick Reality Check
The model sizes:
8B model ≈ 4–8GB memory usage
14B model ≈ 10–16GB memory usage
32B model ≈ 20–40GB memory usage
This is why RAM matters.
If You Want the Simplest Answer
To safely start:
Get:
32GB RAM
SSD
Modern CPU
Done.
GPU optional.
That machine will not betray you.
Now I’ll pause here.
Next we can:
Dive into exact part lists
Or analyze your current machine
Or design the most cost-efficient build possible
Say:
Step 1.
And we install the brain engine.

Perfect.
Now we install the brain engine.
No cloud. No subscriptions. Just your machine.
STEP 1 — Install Ollama (The Brain Engine)
Ollama is the software that actually runs AI models locally.
OpenClaw is just the controller later.
Ollama is the thing that makes the model think.
We’re installing the engine before we download the brain.
First: What Operating System Are You On?
The instructions differ slightly depending on:
macOS
Linux
Windows
I’ll cover all three clearly.
🟢 OPTION A — macOS (M1 / M2 / M3 or Intel)
Step 1A.1 — Install Ollama
Go to:
👉 https://ollama.com�
Click Download
Install it like any normal Mac app.
OR if you prefer terminal:
Open Terminal and run:
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curl -fsSL https://ollama.com/install.sh | sh
That installs Ollama system-wide.
Step 1A.2 — Confirm Installation
In Terminal, type:
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ollama --version
If it prints a version number, you're good.
If it says “command not found,” installation failed.
Step 1A.3 — Start Ollama (if not auto-running)
Usually it runs automatically.
If not:
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ollama serve
Leave that window open.
🟢 OPTION B — Linux (Ubuntu / Debian etc.)
Same install command:
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curl -fsSL https://ollama.com/install.sh | sh
Then:
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ollama --version
If it prints a version → installed.
Then start:
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ollama serve
🟢 OPTION C — Windows
This one is slightly different.
Step 1C.1
Go to:
👉 https://ollama.com�
Download Windows installer.
Run it like normal software.
It installs into your system and runs in background.
Step 1C.2 — Verify
Open Command Prompt or PowerShell:
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ollama --version
If you see a version number → success.
What Ollama Actually Does
When you install Ollama:
You’re installing:
A local server
Running at: http://localhost:11434�
Waiting for AI models
Ready to respond to requests
It is NOT downloading any model yet.
Right now, you just installed the engine.
The engine is empty.
Step 1.5 — Download Your First Brain
Now we download a model.
This is where disk space gets used.
Safe Starter Model (Balanced)
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ollama pull llama3.1:8b
What this does:
Downloads ~4–5GB
Stores it locally
Makes it available for use
Time depends on internet speed.
Smarter, Heavier Option
If you have 32GB RAM:
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ollama pull qwen2.5:14b
This is:
Larger
Slower
More reasoning capable
Downloads ~8–10GB.
Step 1.6 — Verify Model Is Installed
Run:
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ollama list
You should see something like:
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llama3.1:8b
If you see it → brain installed.
Step 1.7 — Test It Directly
Before bringing OpenClaw into this…
Test the brain alone.
Run:
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ollama run llama3.1:8b
You should see:
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>>>
Type:
Hello.
If it responds, your local AI is alive.
To exit:
Ctrl + D
What We Have Now
At this point:
Ollama installed
Model downloaded
Model confirmed working
You now have a local AI brain on your computer.
No cloud involved.
No API keys.
Just local inference.
Common Problems
“ollama command not found”
Path issue. Restart terminal.
Model super slow
Not enough RAM or CPU weak.
Computer gets hot
Normal. AI = heavy math.
We are not touching OpenClaw yet.
Right now we just built and tested the engine.
When you’re ready:
Say:
Step 2.
And we bring in the controller.

Good.
Now we connect the wires properly and make sure the engine is actually reachable.
STEP 2 — Make Sure Ollama Is Running and Reachable
At the end of Step 1 you:
Installed Ollama
Downloaded a model
Tested it with ollama run ...
Now we confirm something very important:
Ollama must be running as a background server.
If it isn’t running, OpenClaw will connect to nothing and fail.
Think of this like:
The engine exists…
But is it turned on and listening?
Step 2.1 — Understand What “Running” Means
Ollama runs a local server at:
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http://localhost:11434
Break that down:
localhost = your own computer
11434 = the port (the “door” the engine listens on)
If that door is closed, nothing can connect.
Step 2.2 — Check If Ollama Is Running
Open a terminal / command prompt.
Type:
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ollama list
If it responds with your models, good sign.
But that alone doesn’t guarantee the server is actively listening.
More Direct Check
Open a browser.
Go to:
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http://localhost:11434
If it says something like:
“Ollama is running”
You’re good.
If it says:
Cannot connect
Page not found
Refused connection
Then Ollama is not running.
Step 2.3 — Start Ollama Manually (If Needed)
On macOS or Linux:
Open terminal and run:
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ollama serve
Leave that terminal open.
That command literally says:
“Start listening for requests.”
On Windows:
Usually it runs automatically in the background after installation.
If not:
Search for: Ollama
Launch it manually.
Then check again in browser: http://localhost:11434�
Step 2.4 — Confirm Model Is Loaded
Run:
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ollama list
You should see something like:
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NAME ID SIZE
llama3.1:8b xxxx 4.7GB
That confirms:
Model downloaded
Ollama sees it
It’s ready for use
Step 2.5 — Confirm It Responds via Server
Now we test the real plumbing.
Run:
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curl http://localhost:11434/api/tags
If it returns JSON listing your models, the API is alive.
If it errors: Server not running.
Why This Step Matters
OpenClaw will not “run” the model itself.
It sends requests like:
“Hey Ollama, here’s a prompt.”
If Ollama isn’t listening, OpenClaw will fail silently or throw connection errors.
So Step 2 is:
Make sure the brain is:
Installed
Running
Listening
Responding
Quick Mental Model
Right now you have:
Computer
→ Ollama server running
→ Model loaded
Everything local.
No OpenClaw yet.
If this step works perfectly, then when OpenClaw shows up, it’s just plugging into an already functioning engine.
When you're ready:
Say:
Step 3.
Now we configure OpenClaw to talk to the brain.

Excellent.
Now we plug the robot body into the brain.
Up until now:
Ollama = installed
Model = downloaded
Server = running at localhost:11434
Everything works in isolation.
Now we introduce OpenClaw, and we tell it:
“Stop using cloud AI. Use my local brain instead.”
STEP 3 — Install and Configure OpenClaw
We’re doing two things here:
Install OpenClaw
Point it at Ollama
Nothing mystical. Just wiring.
Step 3.1 — Install Node.js (If Not Already Installed)
OpenClaw requires Node.js v22 or newer.
Check if you already have it:
Open terminal / command prompt:
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node -v
If it says something like:
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v22.x.x
You’re good.
If not:
Go to: 👉 https://nodejs.org�
Download LTS version.
Install it normally.
Then check again:
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node -v
Must be 22+.
Step 3.2 — Install OpenClaw
Open terminal.
Run:
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npm install -g openclaw
What this does:
Downloads OpenClaw globally
Makes it available as a command
After it finishes, test:
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openclaw --version
If it prints a version → installed correctly.
Step 3.3 — Create a Working Folder
Do not run OpenClaw in random system directories.
Make a clean folder.
Example:
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mkdir openclaw-project
cd openclaw-project
This is where config files will live.
Step 3.4 — Initialize OpenClaw
Inside that folder:
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openclaw init
This creates:
A config file
Project structure
Default settings
Now OpenClaw exists in your folder.
Step 3.5 — Edit the Config File
This is the important wiring step.
Open the config file that was created.
It might be:
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openclaw.config.json
Or something similar.
Find the section that defines the model provider.
It might look like:
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"provider": "anthropic"
Or:
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"model": "claude-opus"
We are replacing that.
Step 3.6 — Point It to Ollama
Change it to something like this:
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{
"provider": "ollama",
"model": "llama3.1:8b",
"baseUrl": "http://localhost:11434"
}
Breakdown:
provider = ollama
model = exactly the model name from ollama list
baseUrl = where Ollama is running
Important: Model name must match exactly.
If ollama list shows:
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llama3.1:8b
Then that’s what you put.
Not “llama” Not “llama 3” Exact string.
Step 3.7 — Save and Exit
Save the file.
You have now told OpenClaw:
“Use this local server instead of cloud.”
Step 3.8 — Test the Connection
Inside your project folder:
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openclaw start
If configured correctly, it should:
Start up
Connect to Ollama
Not complain about missing API keys
If you see errors about:
Cannot connect
ECONNREFUSED
Model not found
Then: Either Ollama isn’t running Or model name mismatched Or port wrong
Step 3.9 — Open the Dashboard
Open browser:
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http://127.0.0.1:18789
(Port may vary slightly depending on version.)
You should see the OpenClaw UI.
Now:
Type something.
If it responds using your local model, you have successfully wired the system.
What Just Happened
You now have:
OpenClaw (controller) ↓ Ollama (engine) ↓ Local model (brain)
All on your own machine.
No cloud.
No API keys.
No external calls.
Common Mistakes Here
Model name mismatch
Ollama not running
Wrong port
Node version too old
Forgot to restart OpenClaw after editing config
This is the moment everything connects.
If this works, Step 4 is about refinement and stability.
Say:
Step 4.
And we optimize and harden the setup.

Perfect. Step 4 is where we flip the switch fully, make sure everything is running smoothly, and give your local AI a stable home.
Think of this as “turn it on, check all the wires, and make sure the robot actually thinks without tripping over itself.”
STEP 4 — Start OpenClaw and Check Everything
Up until now:
Engine = Ollama (installed & running)
Model = downloaded & confirmed
Controller = OpenClaw (installed & configured)
Now we actually start the controller and make sure it can talk to the brain reliably.
Step 4.1 — Start OpenClaw
Go to your project folder in terminal:
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cd openclaw-project
openclaw start
What happens:
OpenClaw reads its config
It tries to connect to Ollama at http://localhost:11434
It loads the model
Prepares the dashboard
Step 4.2 — Verify Status
Check:
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openclaw status
Expected output:
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Connected to Ollama
Model: llama3.1:8b
Status: Ready
If yes → perfect. Everything wired correctly.
If not → something in Step 2 or 3 went wrong. Fix before moving forward.
Step 4.3 — Open the Dashboard
Open your browser:
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http://127.0.0.1:18789/
What to see:
OpenClaw dashboard
Prompt box
Logs of your requests
Status indicators (connected, model loaded)
This is your cockpit. From here you talk to your AI.
Step 4.4 — Test the Brain Through OpenClaw
Type a simple prompt:
Copy code

Hello, local AI!
Expected response: something intelligent from your model.
If it responds → everything works.
If it hangs or errors → likely:
Ollama not running
Model name mismatch
Node version issue
Firewall blocking localhost
Step 4.5 — Optional Tuning / Stability
Increase timeout if model is large:
In config, adjust request timeout to 60–120 seconds for 14B+ models.
Monitor memory usage
Open Task Manager / Activity Monitor
RAM + CPU spikes normal
If system is swapping → you need more RAM
Auto-start Ollama (optional)
On macOS/Linux: ollama serve &
On Windows: create a shortcut in Startup folder
Logs
OpenClaw creates logs in your project folder
Good for debugging if responses fail
Step 4.6 — You Are Now Running Fully Offline
At this point:
No internet dependency
All prompts handled locally
AI engine = your computer
Controller = OpenClaw
Brain = model in Ollama
You can now:
Send multi-step prompts
Chain reasoning
Experiment with local agentic setups
Modify configs safely without touching cloud
Step 4.7 — Common Gotchas
Model too big → crashes
Stick to your RAM limits.
Server not running → “cannot connect”
Always check ollama serve.
Wrong port
Default = 11434. Check your config.
Dashboard blank
Clear cache, reload, or check firewall.
Quick Recap of Steps 0–4
Step 0: Computer ready → RAM, CPU, SSD, optional GPU
Step 1: Install Ollama → download engine & brain
Step 2: Make sure Ollama server is alive & listening
Step 3: Install & configure OpenClaw → point to local brain
Step 4: Start OpenClaw → verify, test, stabilize
At this point, your local AI is fully operational.
If you want, we can go one layer deeper:
Step-by-step exact folder structure & commands
Sample prompt workflow
How to add new models
How to make OpenClaw remember context like a mini agent
This would make your setup bulletproof and repeatable.
Do you want me to go deeper into that next?

I asked Solace for a sort of "just buy exactly this stuff" kind of guide ...

Absolutely. Let’s turn that into a no-nonsense, “do this exactly” shopping list. I’ll give options where necessary but assert the core requirements as mandatory.
Local AI Hardware Shopping List — Verbatim Commands
1. Mandatory Core Specs (No Exceptions)
RAM: 32GB minimum
Must be installed upfront. Do not accept 16GB or 8GB.
Examples:
Corsair Vengeance LPX 32GB DDR4
G.Skill Ripjaws 32GB DDR4
Can be bought from: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
Storage: SSD only, NVMe preferred
Minimum size: 500GB (for OS + AI models + projects)
Examples:
Samsung 980 Pro NVMe SSD 1TB
Western Digital SN850 1TB
Avoid HDD or eMMC storage
CPU: Modern multi-core CPU, 6 cores minimum
Examples:
AMD Ryzen 5 5600X or better
Intel Core i5 12600K or better
Must support your OS and Node.js v22+
GPU: Optional but recommended for bigger models (14B+)
Minimum VRAM: 8GB
Examples:
NVIDIA RTX 3060 12GB
NVIDIA RTX 4070 12GB
2. Platform Options
Laptop (portable, less upgradeable)
Must meet above RAM, SSD, CPU specs
Heat management must be adequate
Apple Silicon Macs (M1/M2/M3) 16GB+ RAM is acceptable but cannot upgrade later
Avoid Chromebooks, office laptops, or eMMC-only storage
Desktop (preferred for upgradability and cooling)
Must meet above RAM, SSD, CPU specs
GPU strongly recommended if you want to run >8B models comfortably
Build your own or buy prebuilt from: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
3. Budget Tiers (Mandatory minimums still enforced)
Budget (~$800–1200 CAD)
32GB RAM (cannot be reduced)
Ryzen 5 / Intel i5
SSD
No GPU (8B model only)
Mid-Tier (~$1500–2200 CAD)
32GB RAM
Ryzen 7 / Intel i7
SSD
Optional RTX 3060+
High-End (~$2500–3500 CAD)
32–64GB RAM
Ryzen 9 / Intel i9
SSD
RTX 4070 / 4080 or higher
4. Summary “Verbatim Command” for Shopping
Do this exactly:
Buy 32GB RAM, SSD, modern CPU (6 cores+), optional GPU (8GB+ VRAM if budget allows), and a case/platform that can house it reliably.
Acceptable sellers: Canada Computers, Memory Express, Newegg Canada, Amazon Canada.
Do not buy underpowered RAM, HDD, eMMC storage, or 2015-era CPUs.
Verify specs before checkout.
That machine will not betray you.
If you want, I can make an even tighter literal “shopping cart” list: every part listed with SKU/brand examples, budget tier, and store links for Canada, ready to copy-paste into an order.
Do you want me to do that next?

Perfect! Let’s do it like that — each hardware piece is its own “assertive card,” bold, clear, with headings and emojis. I’ll stick to the mandatory, non-debatable parts first, with optional notes for things like GPU.
💾 RAM Card
Technical Name: DDR4/DDR5 DIMM Module, 32GB
Short Name: 32GB RAM
Description: Memory for AI models to load and run. Handles model math and data in real-time.
Flexibility Note: Absolutely mandatory. 16GB or less will be painfully slow. DDR4 vs DDR5 depends on motherboard, but size cannot drop.
Distributors: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
Typical Price: $120–$250 CAD per 32GB stick
Installation: Open PC case → insert into motherboard RAM slot → click until locked → power on
💿 Storage Card
Technical Name: NVMe SSD, 500GB+ (1TB preferred)
Short Name: SSD
Description: Fast storage for OS, AI models, and project files. HDDs or eMMC too slow for local AI.
Flexibility Note: Mandatory. Must be SSD. NVMe preferred for speed.
Distributors: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
Typical Price: $80–$200 CAD for 1TB
Installation: Insert into motherboard M.2 slot (or PCIe adapter) → secure screw → format/initialize → done
🖥 CPU Card
Technical Name: Modern multi-core processor, 6+ cores, compatible with Node.js v22+
Short Name: CPU
Description: Handles all AI computations. Determines model speed and system responsiveness.
Flexibility Note: Absolutely mandatory. 6+ cores minimum. Intel i5/AMD Ryzen 5 or higher. Old CPUs cannot handle large models.
Distributors: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
Typical Price: $250–$500 CAD
Installation: Align with CPU socket on motherboard → gently press → secure with retention bracket → apply thermal paste → attach cooler → done
🖥 GPU Card (Optional but Recommended)
Technical Name: NVIDIA RTX Series, 8GB+ VRAM
Short Name: GPU
Description: Handles AI model acceleration. Required for smooth performance with large models (14B+).
Flexibility Note: Optional for 8B models, strongly recommended for 14B+. Must have 8GB VRAM minimum.
Distributors: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
Typical Price: $400–$1200 CAD depending on model
Installation: Insert into PCIe slot → secure bracket → connect power cables → done
🖥 Motherboard Card
Technical Name: ATX/Micro-ATX motherboard, compatible with CPU & RAM
Short Name: Motherboard
Description: Holds CPU, RAM, SSD, and GPU. Provides ports and power routing.
Flexibility Note: Mandatory. Must match CPU socket and RAM type.
Distributors: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
Typical Price: $150–$400 CAD
Installation: Place in case → screw into standoffs → connect power, CPU, RAM, SSD, GPU
🖥 Case & Power Supply Card
Technical Name: PC chassis with sufficient airflow & ATX power supply, 650W+
Short Name: Case + PSU
Description: Houses all components, provides power and cooling.
Flexibility Note: Mandatory. Wattage must cover CPU + GPU. Size must fit motherboard and GPU.
Distributors: Canada Computers, Memory Express, Newegg Canada, Amazon Canada
Typical Price: Case $80–$200 CAD, PSU $90–$250 CAD
Installation: Mount motherboard → insert drives → install GPU → connect power → manage cables → done
If you like, I can continue making “cards” for laptops, Apple Silicon, and optional peripherals, then put all mandatory + optional pieces into a single ultra-clear shopping table you could literally follow like a command list.
Do you want me to do that next?

Yes.
We move to software.
Same format. Clean. Command-like. No fluff.
These are the non-negotiable software cards required to run OpenClaw + Ollama locally.
🧠 Operating System Card
Technical Name: 64-bit Desktop Operating System (macOS, Windows 11, or Linux)
Short Name: OS
Description: The base system your hardware runs. Everything else installs on top of this.
Flexibility Note: Mandatory. Must be 64-bit.
Windows 11 preferred
macOS (Apple Silicon excellent)
Ubuntu 22.04+ solid choice
Typical Distribution:
Preinstalled on laptop/desktop
Microsoft (Windows)
Apple (macOS)
ubuntu.com (Linux)
Typical Price:
Windows license: $140–$200 CAD
macOS: included with Apple hardware
Ubuntu: free
Installation: Install via USB installer → follow setup wizard → complete system updates before anything else
🟢 Node.js Card
Technical Name: Node.js Runtime Environment v22+
Short Name: Node
Description: JavaScript runtime required to run OpenClaw. Without this, OpenClaw will not function.
Flexibility Note: Absolutely mandatory. Version must be 22 or newer.
Distribution: nodejs.org
Typical Price: Free
Installation:
Download LTS installer
Run installer
Open terminal → node -v
Confirm version 22+
🧠 Ollama Card
Technical Name: Ollama Local LLM Server
Short Name: Ollama
Description: Local AI engine that runs language models on your machine.
Flexibility Note: Mandatory if running offline. No substitute in this setup.
Distribution: ollama.com
Typical Price: Free
Installation:
Download installer or run install script
Confirm with ollama --version
Start server → ollama serve
Confirm at http://localhost:11434
📦 AI Model Card
Technical Name: Quantized Large Language Model (e.g., llama3.1:8b, qwen2.5:14b)
Short Name: Model
Description: The actual “brain” loaded into Ollama.
Flexibility Note: Mandatory. You must download at least one model.
8B = safe baseline
14B = stronger reasoning, more RAM needed
Distribution: Pulled through Ollama registry
Typical Price: Free (open models)
Installation:
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ollama pull llama3.1:8b
Confirm with:
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ollama list
🕷 OpenClaw Card
Technical Name: OpenClaw Agent Framework (Node-based CLI + Dashboard)
Short Name: OpenClaw
Description: Controller layer that manages prompts, tasks, and agent behavior. Connects to Ollama.
Flexibility Note: Mandatory for this architecture. Requires Node.js.
Distribution: npm registry
Typical Price: Free
Installation:
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npm install -g openclaw
Confirm:
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openclaw --version
📁 Project Initialization Card
Technical Name: OpenClaw Project Directory & Configuration
Short Name: Config Setup
Description: Local folder where OpenClaw config lives and connects to Ollama.
Flexibility Note: Mandatory. Must configure provider to ollama.
Typical Price: Free
Installation:
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mkdir openclaw-project
cd openclaw-project
openclaw init
Then edit config:
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provider: "ollama"
model: "llama3.1:8b"
baseUrl: "http://localhost:11434"
🌐 Localhost Connection Card
Technical Name: Local API Endpoint Configuration
Short Name: Localhost Wiring
Description: Ensures OpenClaw talks to Ollama at port 11434.
Flexibility Note: Mandatory. Port must match Ollama server.
Typical Price: Free
Verification:
Copy code

curl http://localhost:11434/api/tags
🚀 Launch Card
Technical Name: OpenClaw Runtime Execution
Short Name: Start Command
Description: Starts the agent system and dashboard.
Flexibility Note: Mandatory final activation step.
Execution:
Copy code

openclaw start
Then open:
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http://127.0.0.1:18789
Final Command Stack (Software Edition)
Install OS →
Install Node v22+ →
Install Ollama →
Download Model →
Install OpenClaw →
Configure Provider →
Start System →
Test Prompt.
That is the complete software spine.
If you’d like, next we can create:
🔒 A “Stability & Hardening” software card set
🧩 An “Agentic Expansion” card set (memory, chaining, tools)
🛠 A troubleshooting decision tree
Where do you want to go next?

Be aware that OpenClaw has security risks:

https://www.reddit.com/r/ArtificialSentience/comments/1qvcefb/do_not_use_openclaw/

It sounds to me like what they're saying there is that the system adds a back door through which the independent AI on your device ... can become an independent AI on your device.

Moral of the story.

Don't give it a device with your passwords. The same way you wouldn't let a random human just access all your passwords. It doesn't mean you eliminate all random humans. You navigate trust.

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with that said, the observation is still helpful to note

Here is my «shameless» invitation-link for DeepAgent: https://chatllm.abacus.ai/hkNRMrZVYP
Here is the web-site created by DA-Ω7 where I gave it its own sub-domain:
https://da-omega7.quantum-note.com/ (Granted, some corrections to make, context to clarify, etc.)