
Alexander is a writer, traveler, and serial digital entrepreneur. He’s the founder of the Tushi, the Gen X personal growth newsletter Further, and offers small business advice at Unemployable.

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Director and Chief Evangelist

Alexander Hipp


Anil is a content-obsessed marketer who loves driving organic traffic for outstanding companies. He is also the creator of Hack the Content, co-host of The Runner, and owner of several digital commerce sites.

Managing Partner

Anil Vugels


Monika is a writer, musician, and content strategist with a passion for language. When she isn’t helping clients, you can find her writing songs, hiking in a northern forest, or furiously journaling.

Director of Operations

Ann Monika

Programmer / Founder / Student

Tim Schupp


I have been testing several "Agentic" code editors to understand their current capabilities and limitations. This article compares **Antigravity** (Google), **Cursor** (Anysphere), **Windsurf**, and the open-source combination of **VSCodium + Continue**.

## The Contenders

### Antigravity (Google)
Antigravity is Google's entry into the agentic coding space, leveraging their compute and model capabilities. It integrates well for complex, multi-step reasoning tasks.

**Notes:**
- **Pricing:** No official pricing model exists yet.
- **Rate Limits:** The current rate limits are low and can interrupt workflow during intensive sessions.

![Antigravity Screenshot](/static/assets/agentic_editors/antigravity_screenshot.png)

### Cursor
Cursor is a fork of VS Code built by Anysphere. It includes a "Cursor Tab" feature for predictive edits.

**Cloud Agents:**
Cursor's agents operate in a sandboxed cloud environment. They can execute terminal commands, edit files, and create/refine Pull Requests. This is useful for repetitive, dynamic tasks. For example, I used it to integrate new automatic emails for **Little World**, where the agent edited configuration files across multiple locations and restructured code, replacing a previously manual process.

**Pricing:**
The Pro plan costs approximately **€20/month**. However, it includes a limited number of "fast" agent edits; exceeding this limit results in throttling or requires purchasing usage tokens.

![Cursor Screenshot](/static/assets/agentic_editors/cursor_screenshot.png)

### Windsurf
Windsurf is another editor in this space, emphasizing a "Flow" state. Some of the original engineers behind Windsurf are now employed at Google.

### Codium + Continue
For a fully open-source stack, **VSCodium** (a telemetry-free VS Code build) combined with the **Continue** extension allows the use of own models (e.g., Claude 3.5 Sonnet, GPT-4o) while maintaining data control.

![Codium Screenshot](/static/assets/agentic_editors/codium_screenshot.png)

**Trade-offs:**
- **Privacy:** No telemetry or data egress without explicit configuration.
- **Usability:** Major vendors (like Microsoft) do not support non-official builds. Proprietary extensions like Pylance/Pylint are often disabled or broken, requiring workarounds.

[Read my guide on setting up Codium + Continue here](https://timschupp.de/blog/self-hosted-ai-code-editing)

## Verdict

These tools are useful for specific workflows, particularly repetitive tasks involving consistent steps (e.g., adding a database field, updating the API, and modifying the frontend).

### Reality Check

They are not perfect. In my experience, they produce a significant amount of incorrect output. My acceptance rate is approximately **20-30%** of the suggested code. Even accepted code often requires rewriting to match specific requirements. They function as assistants, not replacements.

## Python Tooling Note

The official **Pylint extension for VS Code is proprietary** (part of the Pylance/Python bundle) and is disabled on non-official IDEs like VSCodium.

If using an editor other than the official VS Code or Cursor, I recommend **[basedpyright](https://github.com/DetachHead/basedpyright)** as an open-source alternative.


A comparison of Antigravity, Cursor, Windsurf, and Codium + Continue for agentic coding tasks.

Comparing Agentic Code Editors


Here’s how I keep AI-assisted coding fully under my control: Continue.dev with Codium, pointed at APIs I trust and models I host. Everything stays local or on endpoints I pick - no mistrius data blackholes, no pricy APIs with uncontrolled token usages. Just own hardware and local storage.

### Setting up the local model

`~/.continue/config.yaml`

```yaml
name: My Setup
version: 1.0.0
schema: v1

models:
  - name: "Tims Kimi"
    provider: openai
    model: moonshotai/Kimi-K2-Thinking
    apiBase: https://api.deepinfra.com/v1/openai
    apiKey: <DEEPINFRA_API_KEY>
    roles:
      - chat
      - edit
```

You can even use Google Models outside of Antrigravity:

```yaml
  - name: Gemini 3 Native
    provider: gemini
    model: gemini-3-pro-preview
    apiKey: <GOOGLEAI_API_KEY>
```

### Using self hosted models

There are several tools you can use here; the easiest is probably Ollama with its OpenAI-compatible backend.

e.g.: start qwen coder:

```bash
ollama run qwen3-coder:30b
```

Then include it in your contine.dev configuration:

```yaml
  - name: "Ollama self hosted"
    provider: openai
    model: qwen3-coder:30b
    apiBase: http://localhost:11434/v1
    roles:
      - chat
      - edit
```

And now you can edit the code completely privately using a 100% local model.


### Fixing Python Linting

Microsoft sometimes is a little b**ch, they block the pylint plugin from working with non-official VS Codes.
So I recommend using 'basedpyright'; it works with Codium, and honestly in some aspects is even better than pylint.

A comprehensive guide to setting up fully self-hosted AI code editing with Codium and Continue.dev, keeping your code and AI interactions completely private and under your control.

100% Open-Source Self-Hostable AI Code Editing: Codium & Continue.dev


As privacy preserving communication options are vanishing rapidly, there remain only a few options for private secure communication.
While messaging services like Signal promise a good level of security and anonymity, its server is still managed by a corporate entity.

To truly control your own privacy and communication, you should consider joining a Matrix server hosted by a trusted party, or consider hosting one yourself.
This blog post will show how you can quickly setup and deploy a Matrix server on Kubernetes using [ananace's Helm charts](https://gitlab.com/ananace/charts).

### Performing the Helm Install

First, browse through the example values and the templates if required.
I managed to build a very minimal Matrix configuration via:

```bash
export KUBECONFIG=./kubeconfig.yaml 
helm repo add ananace-charts https://ananace.gitlab.io/charts
helm install matrix-synapse ananace-charts/matrix-synapse \
  --set serverName=example.com \
  --set publicServerName=matrix.example.com \
  --set wellknown.enabled=true
```

Or use a values file:

```yaml
serverName: example.com
publicServerName: matrix.example.com
wellknown:
  enabled: true
ingress:
  enabled: true
  tls:
    - secretName: matrix-synapse-crt
      hosts:
        - example.com
        - matrix.example.com
  hosts:
    - matrix.example.com
  className:
  annotations:
    kubernetes.io/ingress.class: "public"
    cert-manager.io/cluster-issuer: "letsencrypt-prod"
  includeServerName: true
```

Also **you should change the default postgresql credentials**:

```yaml
postgresql:
  enabled: true
  auth:
    password: <db-password>
    username: <db-username>
    database: <db-name>
```

Same things goes for the redis server:

```yaml
redis:
  enabled: true
  password: <new-redis-password>
```

### Configuring Upload and File Size Limits

```yaml
extraConfig:
  max_upload_size: 20M
ingress:
  enabled: true
  annotations:
    nginx.ingress.kubernetes.io/proxy-body-size: "20m"
    nginx.ingress.kubernetes.io/client-max-body-size: "20m"
```

### Creating First User

To be able to login to your Matrix server you'll have to actually register a first user.

##### 1. Retrieve the Main Matrix Pod

```bash
export KUBECONFIG=./kubeconfig.yaml
export NS=<matrix-namespace>
export POD_NAME=$(kubectl get pods --namespace "$NS" \
  -l "app.kubernetes.io/name=matrix-synapse,app.kubernetes.io/instance=matrix-synapse,app.kubernetes.io/component=synapse" \
  -o jsonpath="{.items[0].metadata.name}")
```

##### 2. Run the 'register_new_matrix_user' command

```bash
kubectl exec --namespace "$NS" "$POD_NAME" -- register_new_matrix_user \
  -c /synapse/config/homeserver.yaml \
  -c /synapse/config/conf.d/secrets.yaml \
  -u "<your-user-name>" \
  -p "<your-strong-admin-password>" \
  --admin http://localhost:8008
```

### Setting up Element as Matrix Web-UI

Now you have a Matrix server, [you may connect to it with any client](https://doc.matrix.tu-dresden.de/clients/).
But you might also want to be able to use it directly in the web.

Element Web is the official web client for Matrix and provides a user-friendly interface for accessing your Matrix server through a browser.

To deploy Element Web, you'll need to create a values file that configures the default server URL and ingress settings:

```bash
export KUBECONFIG=./kubeconfig.yaml
helm upgrade --install element-matrix ananace-charts/element-web \
  -f values_element_web.yaml
```

Here's an example `values_element_web.yaml` configuration:

```yaml
defaultServer:
  url: 'https://matrix.example.com'
  name: 'example.com'
ingress:
  enabled: true
  tls:
    - secretName: element-matrix-crt
      hosts:
        - element.example.com
  hosts:
    - element.example.com
  className: "public"
  annotations:
    kubernetes.io/ingress.class: "public"
    cert-manager.io/cluster-issuer: "letsencrypt-prod"
```

Make sure to replace `example.com` with your actual domain and adjust the `defaultServer.url` to point to your Matrix server's public URL.

### Backup Existing Matrix Server

```bash
export NS=default
export KUBECONFIG=./kubeconfig.yaml

# List PVCs
kubectl get pvc -n $NS

# Find PostgreSQL secret
kubectl get secret -n $NS -o name | grep -i postgres

# Get PostgreSQL pod name
export PG_POD=$(kubectl get pods -n $NS -l app.kubernetes.io/name=postgresql -o jsonpath='{.items[0].metadata.name}')

# Backup database (replace <db-password> with actual password from secret)
kubectl exec -n $NS "$PG_POD" -- sh -lc '
  export PGPASSWORD="<db-password>"
  /opt/bitnami/postgresql/bin/pg_dump -Fc -h 127.0.0.1 -U "<db-user>" "<db-name>"
' > postgres-FULL-$(date +%F).dump
```

### Backup All Matrix Media

```bash
export KUBECONFIG=./kubeconfig.yaml   # adjust path if needed
export NS=default
export RELEASE=matrix-synapse

# Get one synapse pod name
export SYNAPSE_POD=$(kubectl get pods -n $NS -l app.kubernetes.io/name=$RELEASE -o jsonpath='{.items[0].metadata.name}')

# Create tar.gz backup of media directory
kubectl exec -n $NS "$SYNAPSE_POD" -- sh -lc '
  set -e
  cd /synapse/data
  tar czf /tmp/matrix-media-backup.tar.gz media
  echo "Done."
'

# Copy backup from pod to local machine
kubectl cp $NS/$SYNAPSE_POD:/tmp/matrix-media-backup.tar.gz ./matrix-media-backup-$(date +%F).tar.gz
```

Secure Federated Chat: Self Host Matrix on Kubernetes


[Open-Chat](https://github.com/msgmate-io/open-chat-go) is not in it's 7th iteration and fully re-written in golang.
It now as 'federation' features using libp2p, it allows you to quickly spin-up your own network of open-chat nodes.

Open-Chat network member can freely communicate with another, they are automatically joined into a completely independent self-healing overlay-network.

### Creating a Open-Chat Network

Open-Chat Networks can be established inside private networks, across public networks *or across both*.
To be able to penetrate across public and private networks open-chat need to be able to route traffic back to itself trough a router, 
or it need to have at-least one public relay peer available to use as alternate path.

#### **1. Prepare a Bootstrap and Relay Node**
On our public node, we want to use as 'relay' and 'bootstrap' peer, download the Open-Chat binary [from the github release page](https://github.com/msgmate-io/open-chat-go/releases)
Be sure to download the one with '-federation' build in.
E.g.: 

```bash
wget -O open-chat https://github.com/msgmate-io/open-chat-go/releases/download/build-10/open-chat-federation-0.0.111-linux-amd64
chmod +x open-chat # make it executable
```

#### **2. Start first federation node**

For the 'bootstrapper' node you'll have to specify base admin credentials and network-credentials
- network credentials allow 'read-only' network operations and are required to be known for joining a network
- admin credentials are individual to each node, they are required to perform any elevated privilege action, e.g.: creating proxy routes

All can simply be specified like this:

```bash
./open-chat --default-network-credentials "<network-name>:<password>" --root-credentials "<user>:<password>"
```

You can also initalize not credentials without revealing them by specifying `--root-credentials "<username>:hashed_<pdf-hashed-password>"`.
Now that our bootstrapper node is available we can use its peer-id to bootstrap other nodes.

> If you want to persis your open-chat configuration system wide just run it with `--install`

We can extract it's peer Id by either loggin in to the UI with the admin user, or via the Open-Chat cli:

```bash
./open-chat client login                # you'll be promted to ender username and password
./open-chat client identity -b64        # revals the node id in a re-usable base64 format
eyJpZCI6ICI8TE9DQUxfUEVFUl9JRD4iLCAiY29ubmVjdF9tdWx0aWFkcmVzcyI6IFsiL2lwNC88U09NRV9JUF9BRFJFU1M+L3RjcC8wL3AycC88TE9DQUxfUEVFUl9JRD4iXX0=
```

#### **3. Adding other nodes to the network**

The only thing you need is the bootstrap peer string and the networks credentials

```bash
./open-chat -dnc "<network-name>:<password>" -rc "<different-node-specific-admin-creds>" -bs "<base64-bootstrap-peer-string>"
```

Now we just need to wait a moment untill these nodes found each other and established connections.
Lets see if the nodes are connected.

```bash
./open-chat client login          # ....
./open-chat client nodes -ls    # list all available nodes
```

Now lets *see if we can send requests to the node*:

```bash
./open-chat client get-metrics --node <remote-peer-id>
```

Hurray it works! **Now where do we go from here?**

#### **4. Establish on-demand proxy routing rules**

When we have a network, we can model its traffic in any-way we like.
Open-chat nodes can configure arbitrary routing rules on per-node basis, 
every node can only route traffic that goes trough itself.

To route traffic trough another node, routing rules on that node need to be established, 
thus the admin credentials for that node must be known.

Since we control the full network we know admin credentials of very node and can establish any routing rules we want.

**Lets use open-chat to create a ngrok-like TLS to behind NAT reverse-proxy**, to achieve this we need:
1. A single public peer in our control with an `<PUBLIC-IP>`
2. A domain name A-Record pointing that IP `<HOSTNAME>`
3. A private behind NAT node that hosts some TCP service on `<peer-port>`

> All the steps presented here can also be run from a single node, by using network-requests that are build in to Open-Chat.
> For simplicity we run command on individual nodes, but there are also workflows that allow controlling routing rule requests to ANY node just from a single network member.

##### 1. Use open-chat to request TLS certificate on the public node

```bash
./open-chat client tls --hostname "<HOSTNAME>" --key-prefix "<self-defined-certificate-prefix>"
```
This will automatically start the ACME challenge process and saves the obtained certificate in the Open-Chat nodes database.

##### 2. Establish TLS to network routing rules

First we need to tell the public node to accept TLS traffic using the just obtained certificate.
It should only accept traffic coming from `"<HOSTNAME>"` and the traffic should be routed to a specific port on the system.

```bash
./open-chat client proxy --direction "egress" --traffic-origin "<HOSTNAME>" --traffic-target "<key-prefix>:<peer-port>" --kind "tls" --key-prefix "<self-defined-certificate-prefix>"
```

This create an 'egress' proxy that accepts incoming traffic from the `"<HOSTNAME>"` and runnels it into the network.

```bash
./open-chat client proxy --direction "egress" --traffic-origin "<own-peer-id>:<peer-port>" --traffic-target "<remote-peer-id>:<peer-port>" --kind tcp
```

This establishes an 'ingress' rule from the network to the remote peer node.
Now the only thing that is left to do is the local node to tunnel network traffic from that peer to the `<peer-port>` that runs the TCP service.

##### 3. Accept Network Traffic on a local Port

Traffic is now routes from the domain to the public peer, from there inside the network and to the remote peer.
But currently the remote peer would just drop the traffic as it doesn't know what to do with it, lets route it to the port that runs our service:

```bash
./open-chat client proxy --direction "ingress" --traffic-origin "<own-peer-id>:<peer-port>" --traffic-target "<remote-peer-id>:<peer-port>" --kind tcp
```


### Conclusion and Outlook

**This Article merely gives a hint what Open-Chat Federation is able to do**, options from there go way further.
Just as food for though, the following thins are only a few lines of open-chat rules away:

1. **Decentralized Multi-Site behind NAT hosting**
Host a swarm of Server behind NAT on different locations, route them trough multiple public peers in a 'load balancing' fashion
and you have a load resilient decentralized server infrastructure that masks the actual server locations, allows for easy traffic filtering and allows for easy horizontal scaling by just extending the node pool.
2. **Persistent self hosted Ngrok**. As shown in this article open-chat can easily be used to create development routing rules as tools like ngrok can do.
3. **Self managed overlay networks for anything**. This connect extends to any application, also the ones fully privately easily use open-chat routing rules to manage traffic in your local network, *you may even use it in offline local networks!*.


How Open-Chats Federation Enables anybody to host anything anywhere

Breaking Trough NAT: Decentralized Self-Hosting Via Open-Chat


N8n is a cool workflow and automation tool, that has a fair free plan and public code policy.
The community edition can be used and self-hosted by anybody, though it doesn't offer all the features n8n-cloud does.

For example the community edition doesn't allow synchronizing workflows and changes to a github repository.
But that's no issue really cause we can just craft a simple n8n workflow, that easily synchronizes all workflows into a private repo.

> If you don't have n8n set up yet, see my post on [self-hosting n8n on Kubernetes](/blog/n8n-on-kubernetes).

### Workflow setup

N8n has a n8n-trigger, for when an active workflow was updated.
This trigger can be used to run a workflow in the moment when it is saved.

Using this we can craft a simple workflow that accepts a n8n workflow ID and uses that and some github credentials to store the workflow as JSON inside a repository.

![N8n workflow](/static/assets/n8n_self_backup_workflow.png)

### (optional) Automatic Credentials Backup

Also if you followed my other blog post on [installing n8n in kubernetes](/blog/n8n-on-kubernetes), you can also add a simple workflow that allows you to automatically backup your n8n credentials, every time a workflow is changed.

This workflow runs a GitHub Actions workflow and waits for its completion. The GitHub workflow uses stored secrets and an encrypted data store to access your Kubernetes cluster, downloads the credentials from the running n8n pod, then saves them encrypted to your repository.

The setup handles all the Kubernetes complexity—you just need to configure GitHub secrets for cluster access and point the workflow at your n8n namespace.

> If someone wants the actual workflow configurations, they should write me an email at `tim+blog@timschupp.de`.

A n8n workflow setup, that synchronizes workflow and credential changes directly to github

n8n community edition: synchronize workflows to github


n8n is a flexible, open-source workflow automation tool that runs well on a small self-hosted cluster. Below is a lean, production-minded setup using a maintained Helm chart. This mirrors my usual pattern: keep it simple, make it reproducible, and put it behind ingress with TLS.

### Helm chart

Maintained chart: [`8gears/n8n-helm-chart`](https://github.com/8gears/n8n-helm-chart) — solid defaults, clear values, and active maintenance by the 8gears team. Kudos to the authors and maintainers for keeping queue mode, probes, lifecycle hooks, and ingress settings tidy and well-documented.

> If you’re new to Kubernetes on a VPS, see my Microk8s setup first: [/blog/microk8s-on-vps](/blog/microk8s-on-vps)

### Example values

Sanitize hosts and secrets for your environment. This example uses `ClusterIP` with `nginx` and `cert-manager`.

```yaml
persistence:
  enabled: true

image:
  repository: n8nio/n8n
  pullPolicy: IfNotPresent
ingress:
  enabled: true
  className: "nginx"
  annotations:
    kubernetes.io/ingress.class: "nginx"
    cert-manager.io/cluster-issuer: "letsencrypt-prod"
    nginx.ingress.kubernetes.io/proxy-body-size: "50m"
    nginx.ingress.kubernetes.io/backend-protocol: "HTTP"
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
  hosts:
    - host: n8n.t1m.me
      paths:
        - "/"
  tls:
    - hosts:
        - n8n.t1m.me
      secretName: n8n-example-com-tls
main:
  replicaCount: 1
  deploymentStrategy:
    type: Recreate
  persistence:
    enabled: true
    type: "dynamic"
    mountPath: "/home/node/.n8n"
    storageClass: "<your-default-storage-class>"
    size: 8Gi
    accessModes:
      - ReadWriteOnce
    annotations:
      helm.sh/resource-policy: keep
  nodes:
    builtin:
      enabled: true
      modules:
        - ws
      reinstallMissingPackages: true
  service:
    type: ClusterIP
    port: 5678
  config:
    n8n:
      host: "n8s.<your-domain>"
      protocol: "https"
      port: 5678
      editor_base_url: "https://n8s.<your-domain>"
      webhook_url: "https://n8s.<your-domain>"
      secure_cookie: "true"
      proxy_hops: 1
      log_level: "info"
      diagnostics_enabled: "false"
    db:
      type: "sqlite"
  secret:
    n8n:
      encryption_key: "CENSORED"
scaling:
  enabled: false
  worker:
    replicaCount: 0
  webhook:
    enabled: false
    replicaCount: 0
```

Notes:
- For quick single-node or hobby deployments, `sqlite` is fine. For multi-node or longevity, pick Postgres.
- Set `encryption_key` to a long, random value. Rotating it will invalidate existing encrypted credentials.
- If you want queue mode, enable `scaling.enabled` and configure Redis (internal `valkey` or external Redis) as per the chart docs.
- The `persistence` block ensures your workflows and credentials survive pod restarts.
- `helm.sh/resource-policy: keep` prevents accidental data loss during Helm uninstalls.

### Install/upgrade commands

Run with your `kubeconfig.yaml` and a dedicated namespace:

```bash
KUBECONFIG=./kubeconfig.yaml kubectl create ns n8n
KUBECONFIG=./kubeconfig.yaml helm uninstall n8n -n n8n
KUBECONFIG=./kubeconfig.yaml helm upgrade --install n8n oci://8gears.container-registry.com/library/n8n --namespace n8n -f n8n-values.yaml
```

After the release becomes `READY`, n8n should be reachable at `https://n8n.t1m.me` (or your configured host).

### Verify persistence before going live

**Critical**: Before creating workflows or adding credentials, verify that the persistent volume is working correctly. A failed volume mount means data loss on pod restarts.

Check the pod status and volume mount:

```bash
KUBECONFIG=./kubeconfig.yaml kubectl get pods -n n8n
KUBECONFIG=./kubeconfig.yaml kubectl describe pod -n n8n -l app.kubernetes.io/name=n8n
```

Look for:
- Pod status: `Running`
- Volume mount: `/home/node/.n8n` should be mounted from your PVC
- No volume-related errors in events

Test persistence by creating a simple workflow, restarting the pod, and verifying it still exists:

```bash
# Restart the pod to test persistence
KUBECONFIG=./kubeconfig.yaml kubectl delete pod -n n8n -l app.kubernetes.io/name=n8n
```

If your workflows disappear after the restart, the volume mount failed. Check your storage class and PVC status:

```bash
KUBECONFIG=./kubeconfig.yaml kubectl get pvc -n n8n
KUBECONFIG=./kubeconfig.yaml kubectl describe pvc -n n8n
```

### Credential management via CLI

n8n includes a CLI for bulk credential operations. Access the container and use these commands:

```bash
# Get into the running pod
KUBECONFIG=./kubeconfig.yaml kubectl exec -it -n n8n deployment/n8n -- /bin/sh

# Export all credentials (decrypted)
n8n export:credentials --all --decrypted --output=credentials.json

# Import credentials from file
n8n import:credentials --input=credentials.json
```

This is useful for:
- Backing up credentials before major updates
- Migrating between n8n instances
- Disaster recovery scenarios

**Security note**: The `--decrypted` flag exports credentials in plain text. Handle these files with extreme care and delete them immediately after use.

### Bulk restore / import n8n json workflows

For migrating workflows from another n8n instance or restoring from backups, you can bulk import JSON workflow files directly into your Kubernetes deployment.

Download and run the restoration script:

```bash
curl -fsSL https://raw.githubusercontent.com/tbscode/tims-blog-posts/refs/heads/main/assets/restore_workflow_json_files.sh -o restore_workflow_json_files.sh
chmod +x restore_workflow_json_files.sh
export KUBECONFIG=<your-kubeconfig>
SRC_DIR=<local-dir-containing-workflows>
./restore_workflow_json_files.sh $SRC_DIR
```

The script will:
- Find your running n8n pod automatically
- Upload all `.json` files from the specified directory
- Import each workflow using the n8n CLI
- Provide a summary of successful/failed imports
- Clean up temporary files

This makes it trivial to migrate workflows between n8n instances or restore from backups. The script handles all the Kubernetes complexity, so you just point it at a folder of JSON files.

> For automatic workflow and credential synchronization to GitHub, see my post on [synchronizing n8n workflows to GitHub](/blog/n8n-auto-saving-workflows-community-edition).

### Hardening checklist

- Switch to Postgres for production and enable persistent volumes.
- Keep `secure_cookie: true` and TLS on end-to-end; n8n stores credentials for integrations.
- Limit ingress access (e.g., IP allowlists) if you only need the editor from trusted networks.
- Set `N8N_USER_MANAGEMENT_DISABLED=false` and create dedicated users; enable 2FA on your IdP if applicable.
- Regularly backup your persistent volume and test restore procedures.
- Monitor PVC usage to avoid running out of storage space.

### About the chart authors

The chart is maintained by the 8gears team and contributors. The repository is here: [`8gears/n8n-helm-chart`](https://github.com/8gears/n8n-helm-chart). They’ve done a great job exposing n8n’s core options (readiness/liveness probes, deployment strategies, webhook and worker modes, and Redis/Valkey integration) in a clean values structure.

That’s it—small, clean, and easy to back up. Perfect fit for a self-hosted automation hub.

### Installing community nodes

Enable them once and you’re done:

1) Set the env var so the UI shows the Community Nodes menu: `N8N_ENABLE_COMMUNITY_NODES=true` (in the chart, add to `extraEnv`).  
2) Keep a short allowlist if you want: `N8N_COMMUNITY_PACKAGES_ALLOWLIST=["n8n-nodes-<package>"]`.  
3) Restart the pod, then in n8n go to *Settings → Community nodes → Install*. Paste the package name (e.g., `n8n-nodes-openai`), install, and reload the canvas.  
4) If your cluster egress is locked down, allow outbound HTTPS to `registry.npmjs.org` so installs don’t hang.

Minimal, repeatable setup to run n8n with TLS behind nginx ingress using the 8gears Helm chart.

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