Comparison of Video Call Services for Cross-Platform React Web/Native Apps


### Full Setup Lenovo Yoga 9 Pro Regolith Linux (Ubuntu 23.10 + i3)

This is my setup guide for Linux on my new laptop. I'm using the newest 23.10 Ubuntu because I want to.
This blog intends to report how I got things set up & the open issues I still have.

> Don't hesitate to contact me if you have possible solutions or questions regarding my setup, cheers :)

This is the labtop in question: https://geizhals.de/lenovo-yoga-pro-7-14aph8-storm-grey-82y80020ge-a3000891.html?hloc=at&hloc=de

- 14.5", 2560x1600 (WQXGA), 16:10, 208ppi, 90Hz, non-glare (matt), IPS, 350cd/​m², Blaulichtfilter, Dolby Vision, 1.500:1, 100% sRGB 
- AMD Ryzen 7 7840HS, 8C/16T, 3.80-5.10GHz, 16MB+8MB Cache, 35W TDP, 54W cTDP, Codename "Phoenix" (Zen 4, TSMC 4nm) 
- AMD Radeon 780M (iGPU), 12CU/768SP, 2.70GHz, Architektur "RDNA 3" (Phoenix) ( no additional GPU )
- 32GB LPDDR5X-6400 (32GB verlötet, nicht erweiterbar) 


## Open Issues:

1. Keyboard backlight levels not working correctly
It goes from bright to medium to bright to out. But medium is also very bright.
2. Master sound level not working (generally sound seems off)
I've also played with a lot of different output options in `pavucontrol`,
some seem to be completely broken and sound super metallic, while others sound pretty good at full volume but then start sounding weird when leveling down the audio.
This is significantly annoying but ok since it can be worked around by leveling the specific output sound level instead of using the volume buttons.

## Initial setup

The laptop comes with Windows originally. So I:

- create a boot USB with Ubuntu 23.10.
- disable BitLocker in PowerShell
- disable hibernation in PowerShell
- in Windows prepare an unformatted partition of the hard drive
- in BIOS disable secure boot
- boot & install Ubuntu 23.10
- install regolith-desktop using package: https://regolith-desktop.com/

## Configuring Regolith

Basic Regolith / i3 looks can be configured in `~/.config/regolith3/Xresources`.
E.g.: I like to adjust gap and border sizes & colors:

```
wm.gaps.inner.size: 1
wm.window.border.size: 3
wm.client.focused.color.child_border: #AAD3E9
```

## Resolution

- Falsely detected out of the box required xrandr adjustments.
- issues with fractional scaling if not on Ubuntu desktop (so i3)

I found that the i3 window header and bar sizes seem to depend on the dpi set in `~/.Xresources`.
`Xft.dpi: 100`. Weirdly, a higher value causes bigger i3 bar and UI and a lower one smaller.

> I found the cursor size set here to have no effect `Xcursor.size: 5`

To view the current resolution config `xdpyinfo | grep -B 2 resolution`:

```
screen #0:
  dimensions:    2560x1600 pixels (677x423 millimeters)
  resolution:    96x96 dots per inch
```

## External Monitors (& USB monitors)

At first, I thought the external monitors were not working but I soon realized that they were detected but not set to `active`, simply going into `arandr`, setting them to active, and then applying the configuration made all external monitors work as expected.

### Persisting xrandr settings using autorandr

`sudo apt install autorandr` just set the correct settings using `xrandr` or a UI like `arandr`,
then save the profile as default `autorandr --save default`

### Gnome Scaling Factor

I wasn't yet able to get fractional scaling working on Ubuntu yet.
But I found that the overall scale can be set from the command line using:

```bash
gsettings set org.gnome.settings-daemon.plugins.xsettings overrides  "[{'Gdk/WindowScalingFactor', <1>}]"
gsettings set org.gnome.desktop.interface scaling-factor 1
```

It accepts scaling factors `1`, `2`, `3`

## Setting up Keepmenu as password manager

I love using Keepmenu to easily manage a local password database.

- https://github.com/firecat53/keepmenu

I use it with `rofi`; this is my config: `~/.config/keepmenu/config.ini`:

```ini
[dmenu]
dmenu_command = rofi -show drun -dpi 1

[dmenu_passphrase]
nf = #222222
nb = #222222
rofi_obscure = True

[database]
database_1 = XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
keyfile_1 = 
pw_cache_period_min = 30
autotype_default = {USERNAME}{TAB}{PASSWORD}{ENTER}
```

### Setting up the keybinding

The easiest way is to just use the GNOME system keybindings like described in: https://regolith-desktop.com/docs/using-regolith/configuration/

Just open the GNOME settings `Super+c` & open the keybindings editor in the 'Keyboard' settings.

You could also set it up using an i3 config e.g.:

```bash
bindsym $mod+Shift+K exec "DO SOMETHING"
```

## Setup screenshot command

`sudo apt install gnome-screenshot`

I like to map it to `Super+Shift+U`

Then setup a keyboard shortcut like this: `gnome-screenshot -acf /tmp/test && cat /tmp/test | xclip -i -selection clipboard -target image/png`
[as per this stack overflow answer](https://askubuntu.com/questions/1196914/gnome-screenshot-cant-copy-to-clipboard-in-ubuntu-18-04).

I saw only this stategy working, copying directly with `-acf` wan't working for me.


A comprehensive setup guide for setting up a Lenovo Yoga 9 Pro with Ubuntu 23.10 and Regolith i3, including resolution and external monitors configuration, and workarounds for common issues.

Full Setup Guide: Lenovo Yoga 9 Pro with Regolith Linux (Ubuntu 23.10 + i3)


- [the python script](https://github.com/tbscode/tims-blog-posts/blob/main/assets/send_slack_message.py)

Need to notify your team about deployment status on Slack? Automate the process using the slack_sdk Python package with GitHub Actions. Here's how to set it up step by step:

### Creating Your Slack App for Notifications

1. **Create a Slack App**: Go to the [Slack API](https://api.slack.com/apps) section, select 'Create New App', input a name, and choose your workspace.

2. **Set Permissions**: In the Slack app settings, navigate to 'Features' > 'OAuth & Permissions'. Under 'Scopes', add `chat:write` for your bot to send messages. Once you save changes, click 'Install to Workspace'.

3. **Retrieve Your Slack Bot Token**: After installing the app to your workspace, grab your 'Bot User OAuth Token' from the 'OAuth & Permissions' page. Securely store this token as it will be used to authenticate your GitHub Actions script.

### Setting Up GitHub Actions to Send Slack Notifications

First, include your Slack API token as a secret in your GitHub repository:

1. In your GitHub repo, go to 'Settings', then 'Secrets'.
2. Select 'New Repository Secret', and add your Slack API token under `SLACK_API_TOKEN`.

Now, integrate the `send_slack_message.py` script with your deployment process:

```python
# send_slack_message.py
import os
from slack_sdk import WebClient

client = WebClient(token=os.environ["SLACK_API_TOKEN"])

response = client.chat_postMessage(
    channel=os.environ["CHANNEL_ID"],
    mrkdwn=True,
    text=os.environ["MESSAGE"],
)

print(response["message"]["text"])
```

Add a GitHub Actions step in your workflow to execute the notification script:

```yaml
jobs:
  deployment:
    runs-on: ubuntu-latest
    steps:
      # ... previous steps for deployment ...
      
      - name: Notify Slack Channel
        env:
          SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }}
          CHANNEL_ID: 'your-channel-id'
          MESSAGE: "*Deployment Update*\nImage: ${{ steps.build.outputs.IMAGE_URL }}\nURL: ${{ steps.deploy.outputs.WEBSITE_URL }}"
        run: |
          pip install slack_sdk
          wget -qO- https://raw.githubusercontent.com/tbscode/tims-blog-posts/main/assets/send_slack_message.py | python3 -
```

Replace `your-channel-id` with the actual Slack channel ID where you wish to send notifications. 

> Replace the script url with a self hosted fork of [send_slack_message.py](https://github.com/tbscode/tims-blog-posts/blob/main/assets/send_slack_message.py)

Remember to update the `MESSAGE` variable with relevant details for your deployment, such as image URL or site URL.

Executing the GitHub Action with these configurations will automatically send your predefined message to the designated Slack channel, informing your team about the latest deployment status.

Your GitHub Actions workflow now works seamlessly with Slack for real-time notifications!

Set up Slack notifications for deployments using GitHub Actions and the Python slack_sdk package, keeping your team updated automatically.

Send Slack Notifications on Deployments with GitHub Actions


# Solid Authentication Overview

> A presentation prepared for the
> 'Data Ecosystems Lab'

## Content

This blog article delves into several key aspects of Solid authentication and OpenID Connect, providing insights into the mechanisms and practical implementation details:

- **Introduction to Solid and OpenID Connect**: An overview of the Solid framework and how OpenID Connect (OIDC) serves as a foundational technology for secure authentication.
- **The Solid Community Server's Authentication Process**: A closer look at how the Solid Community Server implements OIDC and the intricacies involved in user verification.
- **Reverse Engineering Authentication with Python**: Step-by-step guidance on how to reverse engineer client authentication processes using the Python `requests` library.
- **Jupyter Notebook Demonstration**: A practical example showcasing the authentication flow within a Jupyter notebook, accessible for interactive learning and experimentation.
- **Flow Charts and Visual Aids**: The inclusion of original and comprehensive flow charts to visualize the authentication sequence as defined by the Solid project.

> If you want to deploy solid on a self hosted kubernetes cluster [checkout this blog post](todo)

## Presentation Slides

<iframe src="https://docs.google.com/presentation/d/e/2PACX-1vR9IbBdC5t9iGfPpiIm6voYEIJvfLBghO4rQoI1J57_cqfim40nndh1kjCajXyoTt77bQVvMgLaLkDJ/embed?start=false&loop=true&delayms=5000" frameborder="0" width="960" height="569" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>

## Flow Chart

- To view the original flow chart as published by the Solid project, please visit this link: [Solid OIDC Flow Chart](https://solidproject.org/TR/oidc)
- For a full-size preview of the flow chart, click here: [Full Size Flow Chart Preview](https://viewer.diagrams.net/?tags=%7B%7D&highlight=0000ff&edit=_blank&layers=1&nav=1&title=diagramm.xml#R7V3bcptIGn6WuVDV7oUozodLW46SbM3UeOKkUnuVQqIlsUFqBpDszNNvNydB8yNAAoRkchHbTQMN%2Ff3nAxNptn376Jnu5g9sIWci8tbbRHqaiKKgqgb5QUd%2BRSOywUcDa8%2B24knHgRf7HxQPJtP2toX83MQAYyew3fzgEu92aBnkxkzPw6%2F5aSvs5O%2FqmmtUGHhZmk5x9LttBZtoVFf44%2FgnZK83yZ0FPj6yNZPJ8YC%2FMS38mhmSPkykmYdxEP22fZshh7685L1E581LjqYL89AuqHOC%2BYfh%2FTpIbwdNdP%2FGb389ffn0YRpf5WA6%2B%2FiBJ6LqkOs9rjC5LHlh5jI6oP69pyt9nOHtCnuBaR6H6PaagTmlp0x9b5mbvwkCulUPdGHinE7xuTXGaweZru1zS7wlw0ufTJmvzK3tUJyA91DX9Oc3H3nJEuPrJYfi1xz8SvaOvHGX%2FrrfOr%2FbK%2BTYO%2FLXo4s8e4sCchnpyYmHn49jj68bO0AvbvTcrwTV4UNsHfKXQH4lQAtMcgo9nSd%2FWx52v5reGgXxwBI7jun69iJcBh3x0HLv%2BfYBfUF%2BBHA6ivcBvfUsBS4ddMljkxE%2F8Mg96CAy%2FeAV%2BfT5duj1g0XAmTyg9nh8PSg9cHxj9J2nx50Fzpydea0zZlLJBQ7IC2xCFiWnkgc8ICt3Lp%2Bb4OH9zirM0J4yh6L3W4R1glGyAvSWGYph%2FhFhsnXeLzIlPipLnCBFJyVcJ6bB1yMJp4S6yZCvnhCrGbONdXrxI2WRX2LiakBoIkBoDFwzIHMx2f1wCcrjRHliUEswssFrvDOdLG5BPAUhMl8oIfhFSniHYCvC6yRfrI85JQc4DQJcEW%2Ba0hXelCrG3gPHrik8Iu79SGX1yNvvjNxa5%2B0iL3K6UMncxV6Zu3ZrxEbePyU1%2FjtafLbqklyGUBLEPDj2ekfGFjgIyErpha0HqnXTMQcvf%2BbgSxCfh2oCS%2BkphWAKt5i8Uvzwp%2FDj470XP%2F1J8RskxFw2T43m0TWfRKOHHDMgVJ%2B3NwBcxac%2BU4F%2BRLFg5MWFIDHQjB4oPuuITvJuzV%2BZabGeUHqfVOgk91EYG4Fdl8rMl0%2FPT2kPvj75JVoxc3ayfLxa%2BSiYsOSXvufzKVJvpG7VgTN20Y6KBNPfpPyMHI4tVv0KQN8j5eXpBf81fVlMZ%2Fajo60%2B4uTBuwawqDIAVhkAR5R2MYAlFpBqLYAlSplYscoSMjsDhac243bkQiwP%2BGcPH2zrqI0tvGTGw57IWJF%2FQQS7wGGiBUUILZ8yanh3rOHFb0g5ycQaWFWyxuVJGFL2JEjZ41tQ9kCiNhqJltGS78%2BSP8mDa2OOlThGPVNeEFoA3M7531ravL18M5T%2FPqFv%2F3w7vO6nYjNlZkRct4grIKkuCE8gjpPyJq0hJmyvAnWJ26l1LidADstB6y4e%2BntP8ZdatfwTXu63KFQx7t7AjQ3XrIF7kpn0biCwns6WLFxJOcsCLTMQ2FWWmDEdmbIwJd6cd%2BlIiX9%2BnpFTaHB2Za%2FPNwXujDBPqZWd06WWpxexK7pkPENihSdJUhRwXTXpmL3bEOm40htAbE%2Fs5YhsIkp8%2BC9Hi0fKSAYj4g9tL0LrvMC7bxF5NSXg%2FgL5Hz98nVCH%2FNxCWzx1MRHYc9fDK5tqffOl6ZEBaYuaegsIiZqOgxy89swto%2FvmjjVwGqzsN5Q4%2BqK%2FHWeW7pW00pdouaTGb%2BDhnyhzZKErclumsGjAgi6rIYpQTLsNuwQEtHiDgmlt%2B1HkY%2BbYGc1wFEkDEklSR8EQiYnRJ%2Fc5T1UsrHIAIqahp%2BCOwh5GEdLXjO%2BxTqRUG2o7PMJoTZLcCNISfHZf4ZGEgkaNKJQxz3%2B%2BxCoRh%2B0l1YeIvBr1nwTqYomUyKV9pOGCXjQgSaoCcBPtxN7ZgenQ10yAH6snvBnG2zrRSXY49OS2yL99wtYCVuUJB%2Be2kyyNrCD%2Ba1gpHTHOeI7n4yVcmuTBM4g1utFrROY%2BMl%2BR5CE2nK8p4HPUEzKiUvEWBqA3SVBMrz9BNDQzKX9XHzu2pXA%2BYSSmMyX35Sx0CA%2BNkimiH0HnGBKBYoZ6v7JJrpRN7wrTzLMP9ZmixXIcV0pdt%2B8vsxSkWzJElbq4kFT1QqpM%2FGPCAGhQbosGz4G675q7M6EeXSjYutO9701DCTCR%2Ba1pO1wgbLktPfMr8gNBlH7LIC6643lpXvcHxPjoVJBY8SDLxei%2BmJnWCzgVyPpuxaLox1mUMzZiN1XW1uDr2xqMbVDDqBDhLb%2FQYmCywuWaqnJTg0HQNPA%2BdRX6Qk5eTcOmLXVdaZaCdztuzu7dkuzW1Q2UNve1S%2BB96kFMYSzaukHq1vzorTkmh6MGV%2BsBRokakEZquVfy7qY%2Fd%2FiVSPk5JRnbmka5HXuP4BLvbkDo9xSt1QtCH0pd7ixgC%2Bd%2BVaH6ltCql6A1ahBAKxioaURYVL7IrXmkN5%2FgXgG%2BEpwAaCp3dKscE2rUgVzQEm2xjRJHEDuV3oQ7ws4HWh1JkfM7XtvUhJp5yPJvCEHF%2FgcqYAx3CKFTjvgxjX0QaeyXibeCDSCI9RLWRa2rjHV5LMy5Y3zJIsDBIOWJLfFsD18j%2F7obfCl8AV9gi6Be8VWZTtnECUwAsfO3Nj3NibUYqqF1lEoQxvq%2Fbuxdu46SnIsvdi6Wu%2FhOdhjp37Ui6zSVIPNPzV%2BzraxGNvofOxFLE%2B2ZlhCyXjHfYOc38%2BQUnYWMAjCA%2BL9y3Vjp0BLRyGTbcsn%2FMecYQ%2F3HVJsCnFVI7%2B01jlMZZOwyeFhmStM%2BEeQ9Ex4f0Hy2bNtG0IA%2BJ3R4hl19YV%2FB%2FN6nburs3iu97v1YUwQyrtAp7W1H1nWKdSlQhkRXrMvfLIld8pNoerP51zfnO7%2F7tAe7z16miA4yjpdPZgXfhNSRUlqIQiiSwWmMJto45leqLCp8rRt2HR%2BubH55DYn4Ja2KnkXcKY6Y8XNMnc5JQRr%2FsFwi%2F7S3%2BSaEpViEAuTD0cCUrDac0CChiW1CIxUz79SorizLSnyylaWGcm%2BFWUVU1mwK19gwl%2FKGs6JVVMDrOiugGzW7g3hvza58HdnaIAEOkjd%2F3tmBHdbeWGTWV6KF0ct9QZbtUUfszfNiRWBjyooG2K2i2FV6LAiFcoenZR9qb%2FHCXP5ch290Ghs7dKftaEcjAyQz7q0X%2F4o8zXzy49%2BxMUOhklgZD6GYTnB3PF4ja3aiPEq8RF3%2BSS2ftSQ%2Fo0IuYo%2FIE%2B0xKu%2F64cUA%2B7H3bOrbPo0qEH65N%2FXebBpB4TlVqsR1NqWin84ILX1Wo2UmZ1GHzIo8nmpu6XbuFj79kWqeX8L2CEkmF%2F9hZ7mRWLgpZifoeU4H9uKHvDRt6Jxw55cbasbfBryStk%2BffX9%2FSQfYayNHAJEDJTV0lXVVzkdccKtoVHiarCzcLLIyBRZT12dIrofoflGpbO4simR%2BohJhbaEfyw2VSjtiDFAVlieHo4PEUAnoGEfVtE2opS32tkOPJmHOvedcrrClw26b6Lwoq4tFpwQ0AAL5GtuopD0xN5YYMmSRJC9kBOkPdBSjxxOG7pO2TKSvQP1NXeposWqJ4WqFXFewdFbtrGwP5Lo31NfqfOb7gpzQtOXnYQUpMW%2BoF3LvOti0ehXZLSdLaxCA%2Bs6Wruwn0ISvuB7euuFJ0U754c5lqi067142oE4hhdBKfay019mDqdPTtG48iIIgc%2FCdyr%2FfYrBnNPIhFvMuC8%2FWrwsR1orfaeXqOdhvGel6PxWpWqMkMwC17DrbizjCiIRyg89m%2BDGfT2Qxb1Fwjry%2BtTZRoDPa6Et2sDxd7yj%2BxLaF0qu%2B%2FSXI4LpOyJpT129Ms4X3MIDEULHSc3dnlisNVqRuu9sxV1VZU3UNMlclQZYUOd6WbIAi%2FNeOGctSGvjJGOiLlJ1ZsNW9xocGQTuNwGZceRMxjN5BvRBvt2PzFbQ4oDghcRS23mOZFW%2FJnc6uF2BXOgQD5C4akAwDiGq97W3eGbkARPVCILIr7dimAIPIo5Xbg5UrCwaX9K%2FpuvXS8VZnd18CVttxgq1cmd%2FQWuCn2JRmcKpLU4vgOo%2FUQmvXpC15ksokzeM8phAsp5pWDsdS6CkxSSwk3MlAv3LR6DewlUiQdtxUx898HTX0XO766Jmq9kxdw%2BPUVcazYLDRB6XKhyQ0PkMrPM2FfqcrJ0nDhAplVoy6X8GTe73enErNuNilCiJTNdAc3TVjk60hF6pGvkPTuE7xi9oJEKkNrec%2FF6wkdTatG9ECl7StYG5V14qWyy7QASDLKyvHCmmgQnoZ6mxjhfSJCmnoG0P9Vkg3UwTGplMDbzrFMG4NBFgRX%2ByH2doLFDUT1yO%2BusXXRbmQRZe1XrOpWWfV1mNLxiGh6%2BKWjAz3UsEGIgC82uiZB%2BKrWUvGgRob59sWBUOlh4Cd0tFHKYoBO6XRZykUvmKdHdsZQke%2B1cSlygdRVfo7bTXRB9ALypnBaUItEDUFuyzznFR6t9IOEUrxLKX6LKF4llx9lnjWCjWt%2Bl6VhGywPgNoJwbQyqK6rvdd%2BRZy3y8Oex40Yll3718Q9CKwIQ1K7jMkCNl%2F91Nh7JF3ZKMDzSmcqLEo%2FRGh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## Jupyter Notebook

- For a hands-on demonstration of the reverse-engineered authentication using the Python `requests` library, access the Jupyter Notebook here: [Presentation: Reverse-Engineered Authentication Notebook](https://colab.research.google.com/drive/17H9eaPhxgqb4yonFpfEle39pQjWghL_o?usp=sharing)


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Exploring the workings of OpenID Connect, the Solid Community Server implementation, and reverse-engineering client authentication with Python requests.

Understanding Solid Authentication


## Intro 

Sentry is an incredibly nice error tracking tool; it can significantly increase the speed of bug fixing and debugging. It also offers a bunch of other features. You should also check out [their service](https://sentry.io)

Since I like to know how the stuff works and willing to take it appart; I would always prefer to self-host if I can. This also gives me more comfort about controlling my own data which by the way also aids GDPR compliance (though this is also possible using Sentry's service directly).

Fortunately, there [still is a Helm chart](https://github.com/sentry-kubernetes/charts/tree/develop). 
'Still', because for example: [the deprecated Posthog chart](https://github.com/PostHog/charts-clickhouse), but also the originally (officially maintained?) [sentry chart](https://github.com/helm/charts/tree/master/stable/sentry) seems deprecated now.

## Setup

I prepared a script that will:

1. Configure an ingress with a cluster issuer annotation to serve under a specific `$HOSTNAME`
2. Configure a Postgres password
3. Enable session recordings and its interface

## Installation

Use the following script to install Sentry:

```bash
./install_sentry_microk8s.sh \
  K8_NAMESPACE="<installation-namespace>" \
  RELEASE_NAME="<release-name>" \
  SENTRY_HOSTNAME="<...>" \
  SENTRY_HOSTNAME="<...>" \
  POSTGRES_PASSWORD="<...>" \
  SMTP_USERNAME="<...>" \
  SMTP_PASSWORD="<...>" \
  SMTP_PORT="<...>" \
  SMTP_FROM_EMAIL="<...>" \
  SENTRY_ADMIN_EMAIL="<...>" \
  SENTRY_ADMIN_PASSWORD="<...>"
```

I found out about having to set and update the admin Postgres password using a secret; [in this Github issue](https://github.com/sentry-kubernetes/charts/issues/554).
That's why we create a simple secret for the Postgres-password here:

```bash
apiVersion: v1
kind: Secret
metadata:
  name: postgres
  namespace: $K8_NAMESPACE
stringData:
  postgres-password: "$POSTGRES_PASSWORD"
```

For enabling the session feature and directly setting the Python config, I read [over here](https://github.com/getsentry/self-hosted/issues/2057) and [here](https://github.com/getsentry/self-hosted/blob/master/sentry/sentry.conf.example.py) and [here](https://github.com/sentry-kubernetes/charts/issues/918), ending up with:

```yaml
config:
  sentryConfPy: |
    SENTRY_FEATURES["organizations:session-replay"] = True
    SENTRY_FEATURES["organizations:session-replay-ui"] = True
```

Ingress setup follows a pretty standard procedure:

```yaml
nginx:
  ingress:
    annotations:
      cert-manager.io/cluster-issuer: "letsencrypt-prod"
    enabled: true
    hostname: $SENTRY_HOSTNAME
    ingressClassName: "public"
    tls: true
    extraTls:
      - hosts:
          - $SENTRY_HOSTNAME
        secretName: sentry-tls
```

As in some of my other scripts, I like to assume the cluster issuer is named `"letsencrypt-prod"`. To me, that's a reasonable sacrifice for adding fewer inputs ;).

Also, **to get the install to succeed** I needed to increase the `hooks.activeDeadlineSeconds`; otherwise, it would time out and the installation would fail.

```yaml
hooks:
  activeDeadlineSeconds: 3500
```

## Open Issues

Currently, I still experience one of the metrics services failing. I'm not sure about the impact yet, but tracking seems to work just fine:

```bash
microk8s kubectl logs sentry-snuba-metrics-consumer -n sentry
snuba.clickhouse.errors.ClickhouseWriterError: Method write is not supported by storage Distributed with more than one shard and no sharding key provided (version 21.8.13.6 (official build))
```

seems like an issue with my storage provisioning possibly; no impact seen so-far.


Self Hosted Error Tracking and Reporting using Sentry Kubernetes and Helm

Self Host Sentry using Helm


Since launching [Msgmate.io](https://msgmate.io) at the end of March, we've seen a lot of activity and received a myriad of feedback regarding its services and development.

However, the development of the direct Msgmate feature has been quite restricted. As a result, the updates that are visible to the public have also been very limited. Therefore, I am writing this article to shed some light on what's in store for Msgmate's future.

Please note that this article is somewhat technical in nature!

### Statistics

<div style="
    display: flex;
    flex-wrap: wrap;
    justify-content: space-around;
">
    <div style="
        background-color: #2C333A;
        padding: 20px;
        border-radius: 10px;
        width: 20%;
        text-align: center;
        min-width: 320px;
    ">
        <h2 style="color: #E88388; font-size: 24px;">
            <strong>Registered Users:</strong>
        </h2>
        <p>300</p>
    </div>
    <div style="
        background-color: #2C333A;
        padding: 20px;
        border-radius: 10px;
        width: 20%;
        text-align: center;
        min-width: 320px;
    ">
        <h2 style="color: #E88388; font-size: 24px;">
            <strong>Messages Exchanged:</strong>
        </h2>
        <p>6,331</p>
    </div>
    <div style="
        background-color: #2C333A;
        padding: 20px;
        border-radius: 10px;
        width: 20%;
        text-align: center;
        min-width: 320px;
    ">
        <h2 style="color: #E88388; font-size: 24px;">
            <strong>Tokens Used:</strong>
        </h2>
        <p>579,807+</p>
    </div>
    <div style="
        background-color: #2C333A;
        padding: 20px;
        border-radius: 10px;
        width: 20%;
        text-align: center;
        min-width: 320px;
        margin-bottom: 0 !important;
    ">
        <h2 style="color: #E88388; font-size: 24px;">
            <strong>Images Generated:</strong>
        </h2>
        <p>192</p>
    </div>
</div>

Since the alpha launch in March, we've had 300 users register. A total of 6,331 messages have been exchanged, using over 579,807+ tokens for prompts and completions. Furthermore, a total of 192 images have been generated.

> I've received feedback indicating interest in such a service.
> However, there were also critical comments concerning the current reliability and limited range of features available.

Rest assured, I am taking all feedback seriously and am actively working to bring you a better and enhanced user experience.

<strong style="color: red;">For now we limited msgmate.io alpha registrations untill we scaled our bot service to handle more chats in paralel</strong>


### Development in the Software Stack

My interest in large language models and their immediate applications in everyday life led me to initiate Msgmate as an experiment. Having considerable experience in development operations and infrastructure, I've managed to evolve my stack through several iterations based on the backend + infrastructure definitions I've developed over the years.

**Here's a look at the evolution of the stack:**

|Iteration|Link|Description|
|--|--|--|
|1|[Django Clean Slate](https://github.com/tbscode/django-clean-slate)|First stack iteration which involved Django + Webpack + React.|
|2|[Django NextJS Setup](https://github.com/tbscode/django_nextjssetup)|First experimental combination of Django + NextJS + React.|
|3|[Tiny Django](https://github.com/tbscode/tiny-django)|Evolution into the first version of 'Tim's Stack' initially dubbed 'tiny-django'.|
|4|[AnySearch Stack](https://github.com/tbscode/anysearch-stack)|First Live application using the stack for a submission to the tum.ai hackathon. The stack proved to be instantly viable for rapid development and easy deployment to microk8s Kubernetes clusters.|
|5|[Tim's Stack Anystack](https://github.com/tbscode/tims-stack-anystack)|Clean re-write of the stack, adding automation documentation, and more. During the Bunnyshell Jamstack Hackathon, I had the unique opportunity to|
|6|[Tim's Stack V2](https://github.com/tbscode/tims-stack-v2)|Dawn of a new age ;) Finalization work on the stack has started with the objective of making it out-of-the-box ready, usable state.|

All these updated work towards shaping the backend that msgmate.io uses and making it more robust and scalable! The bigger beta update planned for msgmate will be using the update Tims Stack V2 and there are plans for launching an android and ios app along side the new backend and interface.

### The Stack Won 2. Place!

As mentioned in the Above list [I've participated in a Hackathon using my Stack](https://blog.t1m.me/blog/tims-stack). 

> 🥳 Checkout [the hackathon page](https://devpost.com/software/tim-s-stack-anystack).

My efforts in the Bunnyshell hackathon didn't go to waste, as we managed to secure second place and win `$4000`, which I'll be reinvesting in the development of Msgmate.io!

### Msgmate.io Vision

I envision msgmate.io as the premier platform for innovators and casual users a like - who wish to engage with large language models - automated agents - and seamlessly incorporate them into their applications and everyday life. 
Msgmate should provide user-friendly developer APIs, websockets, and client applications. Fundamentally, interaction with language models and agents should be as straightforward as possible. Yet beneath the surface, the platform should offer abundant interfaces and configuration options.

### Development in the Team

Given my commitments to other projects, the time I can invest in Msgmate development is limited.
So, to advance this service further, I need more development power!

Using the prize money from the hackathon, I've already hired a NextJS + React developer to help me revamp the Msgmate interface. 

**However, I still need more developers motivated to work on LLm tooling and software.**

> I am looking for motivated (self) taught developers or students who have experience working on their own projects.
> You need to be a fast learner. Having knowledge of Docker, NextJS, React, Django would be advantageous but not a requirement.
> If you are interested in collaborating with us, get in touch at tim.timschupp@gmail.com

### Want to Stay Updated? Have Feedback or Issues to Report?

We've set up a simple form over at [`msgmate.io/newsletter`](https://msgmate.io/newsletter/). By signing up, you'll receive the next update straight to your inbox.

Feel free to add a comment to the signup request for any feedback or bugs. You can fill out the newsletter request as often as you like! Happy messaging with Msgmate.io!

A progress and state update on the development of my msgmate.io plattform

Msgmate.io Development Update Oct 2023


Different command I've used over the year to quickly create pdfs from local note files.

## Raw Pandoc

```bash
pandoc -V geometry:margin=1cm <mardown-file> -o <pdf-file>
```

## Pandoc + `wkhtmltopdf`-engine

- html support, e.g.: tables etc
- dynmaic margin

```bash
pandoc --pdf-engine=wkhtmltopdf -V papersize=a4 -V margin-top=0.1 -V margin-left=0.1 -V margin-right=0.1 -V margin-bottom=0.1 --from markdown-markdown_in_html_blocks+raw_html <markdown-file> -o <pdf-file>
```

## Quick TXT file to dense readable pdf

```bash
pandoc --wrap=preserve $1 -V geometry:margin=1cm -V fontsize=8pt -o $1.pdf -f markdown+hard_line_breaks
```

## Github markdown using `grip`

```bash
sudo snap install grip
grip <markdown-file>
```

then visit the browser page and export the page as pdf.
Or `curl -X <page> > rendered.html`.

## HTML to pdf with

```bash
pandoc --pdf-engine=wkhtmltopdf rendered.html -o <pdf-file>
```

## TXT to pdf with `enscript` and `ps2pdf`

```bash
find . -type f -name $1 | while read ONELINE; do enscript "$ONELINE" -p "$(echo "$ONELINE" | sed 's/.txt/.ps/g')"; done
find . -type f -name "$(echo "$1" | sed 's/.txt/.ps/g')" | while read ONELINE; do ps2pdf "$ONELINE" "$(echo "$ONELINE" | sed 's/.ps/.pdf/g')"; done
```

A collection of strategies to quickly produce pdf's from markdown files

PDF's from markdown, pandoc and more


Simulate a webcam even in your deveice doesn't have one!
The video input can even work to simulate video inputs on remote development devices.

You'll need to install and anable `v4l2loopback`.

```bash
sudo apt install v4l2loopback-dkms
sudo modprobe v4l2loopback
```

## Live video from screen

```bash
sudo ffmpeg -f x11grab -r 15 -s 1280x720 -i :1 -vcodec rawvideo -pix_fmt yuv420p -threads 0 -f v4l2 /dev/video2
```

## From Video File


```bash
ffmpeg -re -i <some-video>.mp4 -map 0:v -f v4l2 /dev/video2
```

> Where to go from here? Loop a video of you listen patiently in your zoom meetings? ;)

How to use `ffmpeg` to funnel screen or video data as video input.

Simulate secondary webcam on linux


This guide illustrates the steps required to build, push, and deploy a Next.js app on Kubernetes using docker files for defining development and production Next.js images. These docker compose files allow for the building and pushing of images, with a Next.js config able to export static files. A simple helm chart will create deployment, service, and ingress for exposing our app.

> Template repo containing all the required code and configs can be [found on my github](https://github.com/tbscode/nextjs-helm-template).

### Prerequisites

This tutorial can also be adapted to work with any managed cluster as I've been using similar setups to quickly deploy Next.js apps for a while.

For your cluster you'll need:

- `cert-manger` cluster issuer with name `letsencrypt-prod` setup.
- `nginx-ingress` installed.
- `helm` installed.
- Public IP and Host Url connected.

Further, a private container registry is required.

> If you don't have a Kubernetes cluster ready follow [My Blog Post on Microk8s Private Cluster Setup](/blog/microk8s-on-vps).

### 1. Start a Next.js App

Begin by creating a simple Next.js app:

```
npx create-next-app@latest
What is your project named?  frontend
Would you like to use TypeScript? Yes
Would you like to use ESLint?  Yes
Would you like to use Tailwind CSS?  Yes
Would you like to use `src/` directory?  No
Would you like to use App Router? (recommended)  Yes
Would you like to customize the default import alias (@/*)?  No
```

We named the app `./frontend` so the respective folder is created.

### 2. Dockerize the Application

Two docker files can be created:

1. [`./frontend/Dockerfile`](https://github.com/tbscode/nextjs-helm-template/blob/main/frontend/Dockerfile): A development docker file that mounts frontend `./frontend` into the container for hot-reload.
2. [`./frontend/pro.dockerfile`](https://github.com/tbscode/nextjs-helm-template/blob/main/frontend/prod.dockerfile): A production docker file that statically builds the entire application and Next.js in a container.

The development docker file is straightforward:

```Dockerfile
FROM node:16-alpine

WORKDIR /frontend
COPY ./package.json .

RUN apk add curl

RUN npm i --save-dev

ENTRYPOINT ["npm", "run", "dev"]
```

Here, we execute `npm i` and then `npm run dev`. When mounted to the host, the `./node_modules` directory will also be present on the host.

The production docker file, which is based on the Next.js example docker file, can be found [here.](https://nextjs.org/docs/pages/building-your-application/deploying#docker-image)

```dockerfile
FROM node:16-alpine AS deps
RUN apk add --no-cache libc6-compat curl
WORKDIR /app

COPY package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
RUN \
    if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
    elif [ -f package-lock.json ]; then npm i; \
    elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
    else echo "Lockfile not found." && exit 1; \
    fi

FROM node:16-alpine AS builder
WORKDIR /app
COPY . .
RUN apk add --no-cache curl
RUN npm i
RUN npm install --unsafe-perm -g sharp

ENV NEXT_TELEMETRY_DISABLED 1

RUN yarn build

FROM node:16-alpine AS runner
WORKDIR /app

ENV NODE_ENV production
ENV NEXT_TELEMETRY_DISABLED 1

RUN addgroup --system --gid 1001 nodejs
RUN adduser --system --uid 1001 nextjs

COPY --from=builder /app/styles ./styles
COPY --from=builder /app/public ./public

COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static

USER nextjs

EXPOSE 3000

ENV PORT 3000

CMD ["node", "server.js"]
```

### 3. Compose Files for Development

Creating two compose files can pave the way for convenience:

1. [`docker-compose.yaml`](https://github.com/tbscode/nextjs-helm-template/blob/main/docker-compose.yaml): A development compose file.
2. [`docker-compose.pro.yaml`](https://github.com/tbscode/nextjs-helm-template/blob/main/docker-compose.pro.yaml): A production/ Local Infrastructure compose file.

Development is now always as straightforward as executing `docker-compose up`.

### 4. Creating a Helm Chart

To effectively manage the deployments and services we require, we prefer to create a simple Helm chart that can easily be adapted, installed, and updated. Our Helm chart will need the following components:

1. Frontend Deployment
2. Registry Image Pull Secrets
3. Frontend Service
4. Ingress

```
microk8s helm create ./helm
cd ./helm/template && rm -rf *
```

Everything will be deployed to a `rootNamespace`.

We have reset the template and created a simple setup for now [`./helm/template/frontend.yaml`](https://github.com/tbscode/nextjs-helm-template/blob/main/helm/templates/frontend.yaml).

```yaml
{{- if .Values.registryAuth.use }}
kind: Secret
type: kubernetes.io/dockerconfigjson
apiVersion: v1
metadata:
  name: dockerconfigjson-github-com
  namespace: {{ .Values.rootNamespace }}
stringData:
  .dockerconfigjson: >
    {{
      (
        dict "auths"
        (
          dict {{ .Values.registryAuth.registry }}
          (
            dict "auth" .Values.registryAuth.token
          )
        )
      )
      |
      toJson
    }}
{{- end }}
```

We've created a simple flag `.Values.registryAuth.use` to check if registry authentication should be used (as we wouldn't need it for local microk8s deployment).
We use `.Values.registryAuth.use` to define the registry that should be used.

Now we define a simple deployment for our nextjs backend:

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: frontend-container
  namespace: {{ .Values.rootNamespace }}
spec:
  replicas: 1
  selector:
    matchLabels:
      app: frontend-container
  template:
    metadata:
      labels:
        app: frontend-container
    spec:
      containers:
        - name: frontend-container
          image: {{ .Values.frontend.imageURL }}
          ports:
            - containerPort: 8000
          envFrom:
            - secretRef:
                name: frontend-secrets
      {{- if .Values.registryAuth.use }}
      imagePullSecrets:
        - name: dockerconfigjson-github-com
      {{- end }}
```

Note that we inject the `imageURL` and the registry pull secret if required. We go on to expose the port `3000` to the cluster through a simple service.

```yaml
apiVersion: v1
kind: Service
metadata:
  name: frontend-service
  namespace: {{ .Values.rootNamespace }}
  labels:
    app: frontend-container
spec:
  type: ClusterIP
  ports:
    - port: 3000
      targetPort: 3000
  selector:
    app: frontend-container

```

It's also advisable to create a secret that injects some general environment variables listed in `values.yaml:frontend.env.*`

```yaml
apiVersion: v1
kind: Secret
metadata:
  name: frontend-secrets
  namespace: {{ .Values.rootNamespace }}
type: Opaque
data:
{{- range $key, $value := .Values.frontend.env }}
  {{ $key }}: {{ $value | b64enc }}
{{- end }}
```

Finally, we set up an ingress:

```yaml
{{- if .Values.ingress.use }}
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: frontend-ingress
  namespace: {{ .Values.rootNamespace }}
  annotations:
    nginx.ingress.kubernetes.io/proxy-connect-timeout: '3600'
    nginx.ingress.kubernetes.io/proxy-read-timeout: '3600'
    nginx.ingress.kubernetes.io/proxy-send-timeout: '3600'
    nginx.ingress.kubernetes.io/server-snippets: |
      location /ws/ {
        proxy_http_version 1.1;
        proxy_redirect off;
        proxy_buffering off;
      } 
    {{- if .Values.ingress.certManager }}
    cert-manager.io/cluster-issuer: letsencrypt-prod
    {{- end }}
    kubernetes.io/ingress.class: public
spec:
  {{- if .Values.ingress.certManager }}
  tls:
    - hosts:
      - {{ .Values.ingress.host }}
    secretName: frontend-ingress-tls
  {{- end }}
  rules:
  - host: {{ .Values.ingress.host }}
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: frontend-service
            port:
              number: 3000
{{- end }}
```

We've introduced some flags to set up `tls` if required and some annotations to facilitate websocket forwards.

#### Configuration via `Values.yaml`

Finally, to tie this all together, we create some default [`values.yaml`](https://github.com/tbscode/nextjs-helm-template/blob/main/helm/values.yaml)

```yaml
rootNamespace: "default"
registryAuth:
  registry: "localhost:32000"
  use: false
  token: ""
ingress:
  use: true
  certManager: false
  host: "localhost"
frontend:
  imageURL: "localhost:32000/frontend:registry"
  env:
    HOSTNAME: "localhost"
```

This can be used to control all the configuration options we just created.

### 5. Testing on Local Microk8s

It's always good to ensure that we can also deploy the entire infrastructure locally. For this, we can use a simple local `microk8s` installation.

```
microk8s enable ingress dns registry
docker-compose -f docker-compose.pro.yaml build
docker-compose -f docker-compose.pro.yaml push
```

Since we set up the template `.env` earlier, we're already configured to push and pull the image from `localhost:32000/frontend:registry`.
So now, all we need to do is install our newly created chart:

```
microk8s helm install next-js-frontend ./helm/ --set rootNamespace="nextjsfront"
```

Since we also set up `$ROOT_URL="http://localhost"`, the ingress will be configured to route to `http://localhost`. Now let's check the browser:

![Next page on localhost via microk8s](/static/assets/nextjs-page.jpg)

### 6. Deploy to Private Cluster

For the capability to deploy to a private cluster, you'll need a package registry available.

> If you quickly want a cheap registry, you can [check out my blog post](/blog/microk8s-gittea) on deploying Gitea in a private k8s cluster.

#### Modify Environment for Production

Firstly, we need to modify the environment to push to our private registry with a modified tag.

```bash
rm .env
echo "FRONTEND_IMAGE=\"<your-frontend-image-url>\"" >> .env
echo "ROOT_URL=\"<your-host-url>\"" >> .env
```

An example of an image URL is `my-gitea.example.com/gitea-private-user/package:latest`.
A `ROOT_URL` could be something like `nextjs.example.com`.

Note that you can restore the old default `.env` at any time using `git checkout .env`.

We also need to encode access credentials to our registry so we can inject them into our secret.

```bash
echo "<your-registry-user>:<your-registry-password>" | base64
```

```bash
docker-compose -f docker-compose.pro.yaml build
docker-compose -f docker-compose.pro.yaml push
KUBECONFIG="./kubeconfig.yaml" microk8s helm install next-js-frontend ./helm/ \
  --set rootNamespace="nextjsfront" \
  --set ingress.use=true \
  --set frontend.imageURL="<your-frontend-image-url>" \
  --set certManager.use=true \
  --set registryAuth.use=true \
  --set registryAuth.registry="<your-registry-host>" \
  --set registryAuth.token="<base64-encoded-token>"
```

> It works perfectly! In fact, this blog was deployed using this exact technique. 

### 7. Repository Automations

Now, we have a simple process to deploy the repository using GitHub Actions. 
These actions can also run on your own runners using Gitea Actions.

We create one simple workflow [`.github/workflow/deploy.yaml`](https://github.com/tbscode/nextjs-helm-template/blob/main/.github/workflow/deploy.yaml)

```yaml
name: Deploy NextJs App
    
on:
  workflow_dispatch:

permissions:
  contents: read
  pages: write
  id-token: write

jobs:
  deploy:
    runs-on: ubuntu-latest
    name: Deploy NextJS App
    steps:
      - uses: actions/checkout@master
        with:
          token: ${{ secrets.BOT_PAT }}
          submodules: recursive
          ref: ${{ github.event.pull_request.head.sha }}
      - name: Setup Kubeconfig
        id: setup_kubeconfig
        run: |
          touch .env; rm .env
          echo "${{ secrets.KUBE_CONFIG_BASE64 }}" | base64 -d > kubeconfig.yaml
      - name: Setup Environment
        id: setup_env
        run: |
          touch .env; rm .env; touch .env
          RANDOM_STRING="$( openssl rand -hex 3 )"
          FRONTEND_IMAGE="${{ secrets.CONTAINER_REGISTRY }}/${{ secrets.GITTEA_USER }}/nextjs-app:build-$RANDOM_STRING"
          echo "FRONTEND_IMAGE=\"$FRONTEND_IMAGE\"" >> .env
          echo "frontend_image=$FRONTEND_IMAGE" >> $GITHUB_OUTPUT
      - name: Build And Push Image
        run: |
          echo ${{ secrets.GITTEA_PASSWORD }} | docker login ${{ secrets.CONTAINER_REGISTRY }} -u ${{ secrets.GITTEA_USER }} --password-stdin
          DOCKER_BUILDKIT=1 docker-compose -f docker-compose.pro.yaml build
          DOCKER_BUILDKIT=1 docker-compose -f docker-compose.pro.yaml push
      - uses: azure/setup-helm@v3
        with:
           version: 'latest'
           token: ${{ secrets.BOT_PAT }}
        id: install-helm
      - uses: azure/setup-kubectl@v3
        with:
           version: 'latest'
        id: install-kubectl
      - name: Setup Cluster Connection
        id: cluster_connection
        run: |
          echo "Test"
          REGISTRY_AUTH=$(echo -n "${{ secrets.GITTEA_USER }}:${{ secrets.GITTEA_PASSWORD }}" | base64)
          KUBECONFIG=./kubeconfig.yaml kubectl create namespace nextjsnamespace || true
          cat << EOF | while read eval_command; do yq -i eval "$eval_command" ./helm/values.yaml; done
            .rootNamespace = "nextjsnamespace"
            .ingress.host = "${{ secrets.BOT_PAT }}"
            .frontend.imageURL = "${{ steps.setup_env.outputs.frontend_image }}"
            .ingress.certManager = true
            .registryAuth.token = "$REGISTRY_AUTH"
            .registryAuth.use = true
          EOF
          KUBECONFIG=./kubeconfig.yaml helm upgrade next-js-app ./helm/ --set rootNamespace="nextjsnamespace" --install
```

Using the following secrets configured in your GitHub account:

- `BOT_PAT`: GitHub bot access token
- `GITTEA_USER`: Private registry user
- `GITTEA_PASSWORD`: Private registry password or auth token
- `CONTAINER_REGISTRY`: Container registry host address
- `KUBE_CONFIG_BASE64`: Base64 encoded kubeconfig to connect to the k8s cluster

#### Automation Steps

1. Clone the repo and its sub-repos using `actions/checkout@master`.
2. Setup the `.env` with a random image tag
3. Build and push the production image using the generated tag
4. Setup Helm and kubectl using the Azure actions `azure/setup-helm@v3`, `azure/setup-kubectl@v3`
5. Modify `values.yaml` with our new configuration and update the Helm installation

> Opportunities from here are limitless; you can easily have any pull to the main deploy a feature environment or connect a backend to your Next.js app.

Simple steps to build, push, and deploy a Next.js app on Kubernetes

Quickly Deploy Any Next Js App On Kubernetes


Deploying Postgres on Kubernetes need not be complex. With automation in place, you can deploy a secure and isolated instance of PostgreSQL within your Microk8s cluster using Bitnami's Helm chart. The updated script covers everything from provisioning TLS certificates to configuring the database for secure connections.

### Preparing the Environment

The process starts with some initial setup tasks. We create a Kubernetes namespace for the PostgreSQL instance and establish the baseline for our commands:

```bash
#!/bin/bash

KUBECMD_PREFIX="${KUBECMD_PREFIX:=microk8s}"
CERT_MANAGER_NAMESPACE="cert-manager"

$KUBECMD_PREFIX kubectl create namespace $K8_NAMESPACE || true
```

### Configuring SSL/TLS Encryption

With cert-manager installed in our cluster, we can automatically provision and manage TLS certificates. This is a crucial step in ensuring that our database communication is encrypted:

```bash
cat <<EOF | $KUBECMD_PREFIX kubectl apply -f -
apiVersion: cert-manager.io/v1
kind: Certificate
metadata:
  name: $RELEASE_NAME-postgresql-tls
  namespace: $K8_NAMESPACE
spec:
  secretName: $RELEASE_NAME-postgresql-tls
  issuerRef:
    kind: ClusterIssuer
    name: letsencrypt-prod
  dnsNames:
  - $DB_HOSTNAME
EOF
```

It's imperative to wait for the certificate to become ready. This script assures that by incorporating a timeout for the process:

```bash
$KUBECMD_PREFIX kubectl -n $K8_NAMESPACE wait --for=condition=ready certificate $RELEASE_NAME-postgresql-tls --timeout=300s
```

### Installing PostgreSQL with Helm

Having confirmed that the TLS certificate is in place, we proceed with the Helm chart installation:

```bash
$KUBECMD_PREFIX helm install $RELEASE_NAME oci://registry-1.docker.io/bitnamicharts/postgresql \
    -n $K8_NAMESPACE \
    --set global.postgresql.auth.username="$DB_USERNAME" \
    --set global.postgresql.auth.password="$DB_PASSWORD" \
    ...
    --set tls.certificatesSecret="$RELEASE_NAME-postgresql-tls" \
    --set tls.certFilename="tls.crt" \
    --set tls.certKeyFilename="tls.key"
```

The script passes the appropriate values to Helm, signaling it to deploy Postgres with TLS enabled.

### Networking Configuration

The `ConfigMap` and `Service` definitions are applied to route external traffic to the Postgres instance:

```bash
read -r -d '' INGRESS_CONFIG << EOM
apiVersion: v1
kind: ConfigMap
metadata:
  namespace: ingress
  name: nginx-ingress-tcp-microk8s-conf
data:
  5432: "$K8_NAMESPACE/$RELEASE_NAME-postgresql:5432"
---
apiVersion: v1
kind: Service
metadata:
  name: $RELEASE_NAME-nodeport
  namespace: $K8_NAMESPACE
spec:
  type: NodePort
  ...
  nodePort: $TARGET_PORT
  ...
EOM

$KUBECMD_PREFIX kubectl apply -f - <<< "$INGRESS_CONFIG"
```

### Testing the Database Connection

Once deployed, verify the secure connection using Python and the `psycopg2` library. The script aims to establish a connection, execute a command, and then gracefully terminate the connection:

```python
import psycopg2

conn = None
try:
    conn = psycopg2.connect(
        dbname='...',
        user='...',
        password='...',
        host='...',
        port='...',
        sslmode='require'
    )

    print("Success: Connected to the database!")
    cur = conn.cursor()
    cur.execute("SELECT version();")
    record = cur.fetchone()
    print("You are connected to - ", record, "\n")
    cur.close()
except (Exception, psycopg2.DatabaseError) as error:
    print("Error: ", error)
finally:
    if conn is not None:
        conn.close()
        print("Database connection closed.")
```

By requiring `sslmode='require'`, the script confirms that the connection is not only secure but also compliant with best practices.

### Wrapping Up

The combination of Microk8s's simplicity and Helm's power simplifies the deployment of a robust PostgreSQL database. TLS encryption, managed by cert-manager, makes sure the data remains secure in transit. For developers and administrators looking to setup PostgreSQL on Kubernetes, the process is now streamlined, automated, and immeasurably more secure than before.

Do check out the full deployment script and accompanying resources on the [GitHub repository](https://github.com/tbscode/tims-blog-posts/blob/main/assets/postgress_create_dynamic_k8s.sh) and ensure your databases are secure from setup to daily operations.

This guide provides a step-by-step approach to installing a PostgreSQL database using Bitnami's Helm chart on a Microk8s cluster, including TLS configuration for enhanced security.

Setting Up a Secure Bitnami PostgreSQL Instance on Microk8s


Every now and then, when setting up a simple staging environment, the need for a quick-setup database that retains state consistently arises. In such situations, I often lean towards MariaDB, specifically utilizing the [Bitnami Helm Chart](https://github.com/bitnami/charts/tree/main/bitnami/mariadb), with slight tweaks to achieve the following:

- An auto-set, namespace setup
- Accessibility through a single host subdomain + port
- Easy hourly backups to a specified host path
- Exposure through TCP to the cluster host

### Prerequisites

This post is operating under the assumption that you have a `microk8s` Kubernetes cluster already set up, complete with `cert-manager`, `ingress`, `dns` and an active and configured `letsencrypt-prod` cluster issuer.

> Note: To jumpstart your setup, you might find [this Blog Post on Microk8s Private Cluster Setup](/blog/microk8s-on-vps) quite helpful.

### TL;DR 

For a swift deployment of this configuration:

1. [Grab this script](https://github.com/tbscode/tims-blog-posts/blob/main/assets/create_mariadb.sh)
2. Then run it as follows:

```bash
./create_mariadb.sh \
  RELEASE_NAME="<release-name>" \
  K8_NAMESPACE="<installation-namespace>" \
  DB_USERNAME="<db-username>" \
  DB_PASSWORD="<your-super-secure-password>" \
  DB_NAME="<db-name>" \
  TARGET_PORT="<desired-port>" \
  HOST_BACKUP_PATH="<your host backup volume>"
```


> Remember to expose the desired port through your firewall with a `sudo ufw allow $TARGET_PORT` command.
  
#### Uninstallation Process:

Execute the following command: 

```
microk8s helm uninstall $RELEASE_NAME
```

#### Connecting to the Database

Use this command to establish a connection to the database:

```bash
docker run -it --rm  mysql mysql --host <your-host-domain> --port <your-port> --user <your-user> --password --protocol=TCP
```

Or use this with `--network="host"` if your testing a local server.
Remember to enter the admin password when prompted.

#### Backing up the Database

Here's a simple docker command for accomplishing this:

```bash
docker run --network="host" --rm mysql:5.7 mysqldump -h localhost -P 30004 -u <your-user> -p<your-admin-password> --protocol=TCP <your-db-name> > backup.sql
```

#### Host Backup Strategy

A straightforward example of a cron job that allows your database to be backed up regularly is created as follows:

1. First, create a secret to hold your MariaDB credentials:

```bash
microk8s kubectl create secret generic mariadb-creds --from-literal=password='$DB_PASSWORD' --from-literal=username='$DB_USER' -n $K8_NAMESPACE
```

2. Then, define a CronJob in a yaml file:

```yaml
apiVersion: batch/v1beta1
kind: CronJob
metadata:
  name: backup-job
spec:
  schedule: "0 * * * *"
  jobTemplate:
    spec:
      template:
        spec:
          volumes:
            - name: backup-volume
              hostPath:
                path: "$HOST_BACKUP_PATH"
          containers:
            - name: backup-container
              image: mysql:5.7
              volumeMounts:
                - name: backup-volume
                  mountPath: /backup
              env:
                - name: MYSQL_PWD
                  valueFrom:
                    secretKeyRef:
                      name: mariadb-creds
                      key: password
                - name: MYSQL_USER
                  valueFrom:
                    secretKeyRef:
                      name: mariadb-creds
                      key: username
              command: ["sh", "-c", 'mysqldump -h database-host -P 30004 -u $MYSQL_USER --protocol=TCP database-name > /backup/backup-$(date +%Y%m%d-%H%M%S).sql']
          restartPolicy: OnFailure
```

With this configuration in place, you would have a CronJob that executes hourly backups of the database.



This straightforward guide will walk you through deploying and exposing a basic MariaDB database on your microk8s Kubernetes cluster.

MariaDB on Kubernetes with Backup Cron-job


> Are you tired of overpriced package registries? Rapidly deploy your own git and package registry - with [Gitea](https://about.gitea.com/) - using [their helm chart](https://gitea.com/gitea/helm-chart).

### Prerequisites

This post assumes that you have a `microk8s` cluster set up with `cert-manager`, `ingress`, and `dns` configured, and a cluster issuer `letsencrypt-prod` ready.

> To get started, feel free to follow my [Blog Post on Microk8s Private Cluster Setup](/blog/microk8s-on-vps).

## Setting it Up

> **Please note** - This is meant for a quick temporary private package. There is **no persistence intended.**

For easy installation, you can use [this simple script](https://github.com/tbscode/tims-blog-posts/blob/main/assets/install_gitea_microk8s.sh)

```bash
./install_gitea_microk8s.sh \
  K8_NAMESPACE="<installation-namespace>" \
  RELEASE_NAME="<release-name>" \
  ADMIN_USERNAME="<...>" \
  ADMIN_PASSWORD="<...>" \ 
  INGRESS_HOST="<host-url>"
```

In the above script, `$INGRESS_HOST` represents a subdomain you've configured a DNS entry for, pointing to your server, for instance: `my-git.example.com`.

### Configuration

First, to ensure that this doesn't consume too many resources, we disable the high-availability database and use the default one instead.

```bash
microk8s helm install $RELEASE_NAME gitea-charts/gitea \
    -n $K8_NAMESPACE \
    --set postgresql-ha.enabled=false \
    --set postgresql.enabled=true \
```

Next, we establish some credentials for our default admin user:

```bash
    --set gitea.admin.username="$ADMIN_USERNAME" \
    --set gitea.admin.password="$ADMIN_PASSWORD" \
```

We then enable and configure the ingress. Please note we are increasing the `proxy-body-size` to 1GB to allow us to push larger packages.
```bash
    --set ingress.enabled=true \
    --set "ingress.annotations.kubernetes\.io/ingress\.class=public" \
    --set "ingress.annotations.cert-manager\.io/cluster-issuer=letsencrypt-prod" \
    --set "ingress.annotations.nginx\.ingress\.kubernetes\.io/proxy-body-size=1g" \
    --set ingress.hosts[0].host="$INGRESS_HOST" \
    --set ingress.hosts[0].paths[0].path=/ \
    --set ingress.hosts[0].paths[0].pathType=Prefix \
    --set ingress.tls[0].secretName="git.$RELEASE_NAME-tls" \
    --set ingress.tls[0].hosts[0]="$INGRESS_HOST" \
```

> To make packages of the admin user private, sign in at `$INGRESS_HOST`, navigate to Settings > Profile > Visibility, and set it to Private.

### Uploading a Package

You can push a package to a path under your Gitea user as follows:

```bash
echo "<your-admin-password" | docker login $INGRESS_HOST -u <your-gitea-admin> --password-stdin
docker tag <image-id> $INGRESS_HOST/<your-gitea-admin>/<some-package-name>
```
### Authenticating Deployments for Pulling Images

It's essential to authorize your deployments to pull your private container images. You can create a simple image pull secret in your Helm chart as outlined below.

```yaml
kind: Secret
type: kubernetes.io/dockerconfigjson
apiVersion: v1
metadata:
  name: dockerconfigjson-github-com
  namespace: {{ .Values.rootNamespace }}
stringData:
  .dockerconfigjson: >
    {{
      (
        dict "auths"
        (
          dict "$INGRESS_HOST"
          (
            dict "auth" .Values.registryAuth.token
          )
        )
      )
      |
      toJson
    }}
{{- end }}
```

> If you're using Github packages, for instance, `$INGRESS_HOST` would be `ghcr.io`

Create a token by Base64 encoding the `user:password` string:

```bash
echo "<gitea-admin>:<gitea-admin-password" | base64
```
This will allow you to pull from your private repositories.

### Setting up gitea action runners via helm



This guide will demonstrate how you can effortlessly host Gitea on a private Kubernetes cluster and utilize it as a package registry.

Self-Hosted Git Server with Gitea on Kubernetes


> Discover the cheapest and easiest way I've found to create personal Kubernetes clusters.

This blog post provides a step-by-step guide to setting up a private Kubernetes cluster on a Virtual Private Server (VPS), with the following features ready and configured:

- VPS firewall setup to expose HTTP/HTTPS & kubeapi server
- MetalLB configured for load balancing and exposing VPS IP
- Ingress and cert-manager setup for quick & automatic TLS management
- Kubeapi server configured for restricted remote access

The **prerequisites** for this tutorial are: **A VPS with a Public IP**, **A domain with a configurable DNS**.

## Step 1: Create a base user

Connect to your VPS. In a terminal, create a user and add them to the sudoers.

```bash
adduser <user-name>
usermod -aG sudo <user-name>
```

## Step 2: Create SSH keys

On your local machine, create SSH keys to access the server.
Set a secure password for the key.

```bash
ssh-keygen -b 2048 -t rsa
chmod 600 <key-file> <key-file>.pub
```

Copy the content of `<key-name>.pub` to your VPS at `/home/<user-name>/.ssh/authorized_keys`.

Now we edit the ssh config to require the ssh key, edit `/etc/ssh/sshd_config`

```bash
PasswordAuthentication no # yes before
PubkeyAuthentication yes # no before
```

Then restart the ssh service `service ssh restart`

> Note: these are minimal ssh setup setups and depending on the environment extra steps should be taken to make ssh more robust agains outside attacks.

## Step 3: Set up and enable the firewall

Set up the firewall to allow SSH, HTTP, HTTPS connections and also expose port `16443` which will be used to access the Kubeapi server.

```bash
ufw allow ssh
ufw allow http
ufw allow https
ufw allow 16443
ufw enable
```

Now disconnect from your server and reconnect as the new user:

```bash
ssh -i "<private-server-key>" <user-name>@<server-IP>
```

## Step 4: Install and set up microk8s

```bash
sudo snap install microk8s --classic --channel=1.28
sudo microk8s enable rbac
sudo microk8s start
microk8s enable ingress dns hostpath-storage cert-manager
```

If you want to use the current user to manage microk8s; this is not recomended for production clusters

```
sudo usermod -a -G microk8s <your-user>
```

Now we create a default cluster issuer: `letsencrypt-prod`:

```bash
microk8s kubectl apply -f - <<EOF
---
apiVersion: cert-manager.io/v1
kind: ClusterIssuer
metadata:
 name: letsencrypt-prod
spec:
 acme:
   email: <your email>
   server: https://acme-v02.api.letsencrypt.org/directory
   privateKeySecretRef:
     name: lets-encrypt-priviate-key
   solvers:
   - http01:
       ingress:
         class: public
EOF
```

We also need to enable some additional rules for the firewall ([see this github comment](https://github.com/canonical/microk8s/issues/2418#issuecomment-877350375)):

```bash
sudo ufw allow in on cali+
sudo ufw allow out on cali+
```

To check for general configration issues use `microk8s inspect`
This can give you some easy setup configuration infos and warnings such as:

To enable ip forwarding:

```
sudo iptables -P FORWARD ACCEPT
sudo apt-get install iptables-persistent
```


No to konfigure kubeserver access through the host dns, edit `/var/snap/microk8s/current/certs/csr.conf.template`:

```bash
[ alt_names ]
DNS.1 = kubernetes
DNS.2 = kubernetes.default
DNS.3 = kubernetes.default.svc
DNS.4 = kubernetes.default.svc.cluster
DNS.5 = kubernetes.default.svc.cluster.local
DNS.6 = k8s.<your-domain>
```

Refresh the server certificate:

```bash
sudo microk8s refresh-certs --cert server.crt
```

View the updated client certificate: `microk8s config > kubeconfig.yaml`.
Open the `kubeconfig.yaml` and edit the `server:` entry.

```bash
...
- cluster:
    certificate-authority-data: XXXXXXXXCENSOREDXXXXXXX
    server: <pub-IP> <---- put k8s.<domain> here
...
```

Now you can connect to the Kubernetes cluster from your local machine:

```bash
KUBECONFIG="./kubeconfig.yaml" kubectl get pods --all-namespaces
```

## Step 5: Setting Up MetalLB

First, prepare `<your-domain>`. 
Create a specific or one wildcard DNS entry:

```bash
<your-domain> -> <vps-ip>
*.<your-domain> -> <vps-ip>
```

Enable `metallb` via `microk8s enable metallb:<your-ip>-<your-ip>`.

To make `metallb` work correctly, apply the patch described in [this GitHub comment](https://github.com/canonical/microk8s/issues/824#issuecomment-1003284063).


```bash
#!/bin/sh

command -v kubectl > /dev/null 2>&1 && KUBECTL=kubectl
command -v microk8s.kubectl > /dev/null 2>&1 && KUBECTL=microk8s.kubectl

INGTMPFILE=$(mktemp -t ingress_daemonset.XXXXXXXX)

trap "rm -f ${INGTMPFILE}" 0 1 2 3

${KUBECTL} -n ingress get daemonset nginx-ingress-microk8s-controller -o yaml | \
    sed -e 's|- --publish-status-address=.*|- --publish-service=$(POD_NAMESPACE)/ingress|' > ${INGTMPFILE}

${KUBECTL} diff -f ${INGTMPFILE}
if [ $? -eq 0 ]; then
    echo "No changes need to be made"
else
    ${KUBECTL} apply -f ${INGTMPFILE}
fi
```

You can now use LoadBalancer nodes that distribute the `<VPS-IP>`.

## Some Tips

#### Enabling tab completion for microk8s

`vim ~/.bash_aliases`

```bash
# add the fllowing
alias kubectl='microk8s kubectl'
export LC_ALL=en_US.utf-8
export LANG=en_US.utf-8
source /usr/share/bash-completion/bash_completion
source <(kubectl completion bash)  
```

#### Patching External Traffic Policies

```bash
kubectl patch svc nodeport -p '{"spec":{"externalTrafficPolicy":"Local"}}'
```

#### Testing the preservation of client IP adresses

```bash
kubectl expose deployment source-ip-app --name=loadbalancer --port=80 --target-port=8080 --type=LoadBalancer
```

## Conclusion

You now have a Kubernetes cluster with services that can be exposed via ingress, have a load balancer, and cert manager ready. You should be able to access these services through a host domain. 

> The possibilities are endless from here ;^)

You even have the option to connect this to multiple VPSs to extend your cluster. More on that in a future blog post.


A step-by-step guide to setting up a fully configurable private Kubernetes cluster

Setting up a Private Kubernetes Cluster on Your Own VPS Using Microk8s


> UPDATE: We made 2. Place! 🥳 Checkout [the hackathon page](https://devpost.com/software/tim-s-stack-anystack).

<iframe width="560" height="315" src="https://www.youtube.com/embed/_06vvSltvvY?si=ZMTgD9l2TFNTTa_2" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

> Checkout the [full stack code](https://github.com/tbscode/tims-stack-anystack) on my repo

## Introduction

I recently participated in the Bunnyshell hackathon, where I successfully orchestrated a dynamic cross-platform web app stack. I want to share with you in detail my development process, core features, and the benefits of this stack.

This stack is primarily designed for dynamic, real-time web apps, with a specific inclination towards mobile clients. One of the prominent features is that any changes made in the backend can be directly and instantly transmitted to the clients via a WebSocket connection, thanks to Redux for smooth state management.

## Architecture

I used a Docker container-based architecture using Helm for my application. Docker simplifies the process of managing my application's lifecycle, while Helm assists in deploying my Docker images into a Kubernetes cluster.

Here is an image representation of my stack's architecture:

![stack overview](/static/assets/overview_graph.png)

## Building the Stack

The stack incorporates:

- **Django Backend**: Django is utilised for the backend due to its simplicity and rapid development capabilities. Features like Celery for task management and DRF Spectacular for API documentation are added advantages.

- **Next.js + React Frontend**: Next.js powers the frontend, providing performance benefits, SEO capabilities, and better developer experience.

- **Postgresql & Redis**: They act as primary databases for data and celery respectively.

- **Documentation**: Pdoc3 was used to generate code documentation from backend code.

## Configuring the Stack on Bunnyshell

Bunnyshell significantly simplifies cloud management and deployment automation making it a breeze to set up a Kubernetes cluster and to deploy my application there.

In addition to this, I also exemplify setting up a private Microk8s cluster. Microk8s serves as a lightweight, lean, and efficient Kubernetes distribution, enabling quick and easy deployment of Kubernetes resources even on a low-end VPS.

I hope you find this stack setup helpful and efficient for your next big project. The GitHub repository contains all the code with appropriate comments and READMEs for further exploration.

Happy Coding!

## Technologies

The following table provides a quick overview of the key technologies integrated into the stack:

| Component | Technology | Purpose |
|-----------|------------|---------|
| Frontend  | Next.js + React | Rich UI |
|           | Tailwind CSS + DaisyUI | Styling |
|           | Redux | State Management |
|           | Capacitor | Native integrations and iOS/Android PWA export |
| Backend   | Django | Application Framework |
|           | Celery | Task Management |
|           | Django REST Framework + django_rest_dataclasses | REST API Development |
|           | DRF Spectacular | API Documentation |
|           | Django Channels | Managing WebSockets |
| Documentation | Pdoc3 | Code Documentation |
| Database  | PostgreSQL | Primary Backend Database |
|           | Redis | Broker for Celery and Django Channels |
| Containerization | Docker | Container Creation and Management |
| Deployment and Orchestration | Helm + Bunnyshell | Simplified deployment, Scaling and Management |

Each of these technologies has been selected for its specific capabilities that contribute to the efficient operation of the entire web application stack.

This article showcases a high-performing web app stack I created for a recent Bunnyshell hackathon, using Docker, Kubernetes, Microk8s, Django and Next.js.

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