Using the stack on Windows

Setup is done and the script printed SMOKE OK. This page is everything that comes after: your addresses and logins, the handful of commands you need, how to get your own files in and out, how to update, and what to do when something goes wrong. Bookmark it — you won’t need the setup guide again.

Everything with a ./kingo in it is typed in the Ubuntu app, inside the kingo-pod folder.

Your services

Use your normal Windows browser — WSL2 forwards localhost from Ubuntu to Windows automatically. If setup had to move a port your addresses differ from the defaults below — ./kingo credentials always prints the truth for your own laptop:

Not all of these run at the same time: in abp mode — what setup installs — only Langflow, n8n, CloudBeaver, Qdrant and PostgreSQL are on, and ./kingo credentials marks the others as off. See Modes for switching.

CloudBeaver looks empty at first (“No Connections”) — you have to log in via the gear icon → Login before the class database shows up, and give it the database password once. Two minutes, step by step: Using CloudBeaver.

MCP (Model Context Protocol) is how AI assistants such as Claude Desktop, Claude Code, or Cursor connect to tools. Point yours at the Jupyter MCP endpoint http://localhost:4040/mcp (header Authorization: Bearer kingo-mcp-2026) and it can write and run code in the class notebook for you. ./kingo mcp prints exactly this.

OpenCode (AI coding in the terminal) runs inside your Ubuntu — install it once with:

curl -fsSL https://opencode.ai/install | bash

then run opencode. The first run asks for an API key; change it anytime with opencode auth login.

Everyday use

Open the Ubuntu app and go to the class folder:

cd kingo-pod

then one command per job:

What if I just close the Ubuntu window? Nothing breaks — the stack keeps running in the background: your services stay reachable in the browser, and it keeps using its memory (about 3 GB in abp mode, up to 6 GB in full mode). It stops only when you run ./kingo down or shut down / restart Windows. Your data survives all of these — closed windows, down, reboots. After a reboot, open Ubuntu and run ./kingo up again. (Docker Desktop users: the stack may come back by itself when Docker starts — ./kingo status shows what’s up.)

Modes: which services run

Not every class week needs all nine services, and an 8 GB laptop cannot run them all comfortably. A mode is the set of services that runs — the others are simply off (nothing is deleted; every mode keeps your data):

./kingo mode shows the current one. Switching is one line, for example:

cd ~/kingo-pod && ./kingo mode full

It takes about a minute: the stack stops and comes back with the new set of services. Your notebooks, dashboards, flows and workflows are all still there — switch back any time with ./kingo mode abp.

On Windows there is no memory setting to make: Windows lets WSL use up to half of the laptop’s memory by default (4 GB on an 8 GB laptop) and takes back whatever the containers do not use. abp, langflow and n8n fit that default; bi and full on an 8 GB laptop will run, but slowly — kingo says so when you switch. ./kingo memory shows what the containers have and use, and which modes fit.

Your own files — the shared folder

shared is a folder inside kingo-pod that both you and Langflow can see. Put your own files there: a SQLite database, a CSV, an Excel sheet, a PDF.

It is one folder with two names, because you and Langflow look at it from different sides:

So a database you drop in as ~/kingo-pod/shared/trials.sqlite is /app/shared/trials.sqlite when you type it into a Langflow component. It works the other way round too: whatever Langflow writes there shows up on your laptop. The folder is made for you — if it is not there, run ./kingo update once.

Opening a SQLite file in Langflow? The SQL component wants a database URL, not a path, and an absolute path takes four slashes: sqlite:////app/shared/trials.sqlite.

Where is the folder in Windows? Ubuntu’s files show up in File Explorer. Open Explorer and follow the left sidebar:

Linux → Ubuntu → home → your Linux user name → kingo-pod → shared

Your Linux user name is the one you chose when Ubuntu first started, so your folder is not called frank like the one in the picture. You can also paste \\wsl.localhost\Ubuntu\home\ into the address bar and click on from there.

Drag files in and out like in any other folder. Langflow sees them at once.

Quickest way there from Ubuntu — this opens the folder in Explorer:

cd ~/kingo-pod/shared && explorer.exe .

Then right-click shared in Explorer’s sidebar → Pin to Quick access, and it is one click away from then on.

Keep private files out of it. Anything running inside Langflow can read, change and delete what is in this folder — including a flow someone else built and you imported. Never let it hold your only copy of something. Nothing outside this one folder is reachable from Langflow.

Using Docker Desktop? Files that Langflow itself writes into the folder end up owned by root inside Ubuntu — deleting those needs sudo rm. Files you put in are never affected. With Podman (the default) this does not happen.

Keeping up to date

One line brings your installation up to date — newest class files, images and rebuilt containers. Safe to run any time in Ubuntu, from any folder:

cd ~/kingo-pod && ./kingo update

Run it whenever your instructor announces an update. It works for every install, however old — nothing has to be downloaded by hand.

If something breaks

Always start with one command (in Ubuntu, inside kingo-pod) — it checks the usual suspects and tells you what to do:

./kingo doctor

FAQ

Why Podman and not Docker? They do the same job and this stack runs identically on both (our tests run on both, every day). We default to Podman because it’s fully open-source and free for everyone — Docker Desktop’s license requires payment at larger companies, and we don’t want the tooling you learn to expire with your student status. If Docker Desktop is already on your laptop, the setup script simply uses it — you are not missing anything either way.

I already use Docker / have my own database. Will this break my stuff? No. Kingo runs in its own containers (all named kingo-…) with its own storage. The only possible overlap is a port number — and setup/fixports resolves that automatically by moving Kingo, never your software.

The passwords are printed in a public repo?! Yes, on purpose. Every service is reachable only from your own laptop (127.0.0.1 — people on the same Wi-Fi cannot connect; WSL2’s localhost forwarding keeps it that way). So these are classroom conveniences, not secrets. The one real rule: the class shares an n8n encryption key, so never put a real API key into an n8n workflow you share or export.

Where is my data? Databases, notebooks, and workflows live in container volumes inside WSL2 and survive ./kingo down, reboots and mode switches. Only ./kingo reset deletes them (it asks first).

Where did Jupyter and Metabase go? They are off in abp mode, which is what setup installs — ./kingo status and ./kingo credentials say so. ./kingo mode full turns everything on (about a minute; see Modes); the instructor announces when a class week needs it.

Can I give the containers more memory? Not through kingo, and on an 8 GB laptop it would not help: whatever WSL takes, Windows no longer has, so the fix is a lighter mode (./kingo mode abp). The half-of-the-laptop limit is Windows’ own setting (a .wslconfig file). If you know your way around that file, changing it is your own project — kingo neither needs it nor touches it.

Can I use extra Python packages (say, statsmodels)?

• JupyterLab (:8888) already ships the data-science standards — pandas, statsmodels, scikit-learn, seaborn, and friends. For anything else, run %pip install <package> in a notebook cell (repeat it if the stack was restarted since).
• Langflow: the class set (statsmodels, ragas, …) is built in — import statsmodels just works in Python components. Need one more? ./kingo langflow pip install <package> installs it on the spot; it lasts until the next ./kingo down + up, so run it again after that (or ask the instructor to add it for everyone). If the install upgraded a compiled package (pyarrow, numpy, pandas — pip lists what it installed), run ./kingo restart langflow before building a flow: the running Langflow still has the old one loaded, and the build would otherwise fail with “size changed, may indicate binary incompatibility”.
• JupyterHub (:8000) starts a minimal Python without the data packages — use %pip install there too, or simply do data work in JupyterLab.

Where are my Windows files inside Ubuntu? Your Windows drives are mounted under /mnt — e.g. C:\Users\you\Documents is /mnt/c/Users/you/Documents.

How it all fits together

┌─ Your laptop (Windows) ────────────────────────────────────┐
│                                                            │
│   Browser, KNIME (on Windows)                              │
│       │  always talk to  localhost:<port>                  │
│       ▼  (WSL2 forwards localhost into Ubuntu)             │
│  ┌─ Ubuntu on WSL2 (Windows' built-in Linux) ─────────┐    │
│  │                                                    │    │
│  │   container engine (Podman or Docker) runs one     │    │
│  │   container per service:                           │    │
│  │   [Langflow] [n8n] [JupyterLab] [Metabase] ...     │    │
│  │       │        │                                   │    │
│  │       └────────┴──► [PostgreSQL]   [Qdrant]        │    │
│  │                                                    │    │
│  │   containers reach each other by service NAME:     │    │
│  │   postgres:5432, qdrant:6333                       │    │
│  └────────────────────────────────────────────────────┘    │
└────────────────────────────────────────────────────────────┘

Two address rules cover everything:

1. From your laptop (Windows browser, KNIME): always localhost:<port>.
2. From one service to another — e.g. an n8n workflow or Langflow flow connecting to the database: use the service name as host, not localhost. PostgreSQL: host postgres, port 5432, database classroom, user student, password kingo2026. Qdrant: http://qdrant:6333. (Inside a container, localhost means the container itself — the most common mistake. Rule 2 is also why moved host ports never affect service-to-service connections.)

KNIME (optional)

KNIME runs on Windows itself, not inside Ubuntu: download KNIME Analytics Platform from https://www.knime.com/downloads and install it like any other program. To use the class database, create a PostgreSQL connection with host localhost, port 5432 (or your moved port from ./kingo credentials), database classroom, username student, password kingo2026. The stack must be running (./kingo up in Ubuntu) while you use it.