PosteRazor, is super easy to learn.

It can make wall posters, by printing the A4 segment sheets, and then gluing them together.

https://posterazor.sourceforge.io/

I saw Krita was already suggested. Also give Inkscape a go. It’s meant for vector graphics instead of raster. (like SVG vs jpeg).

Prompting claude for python scipts you feed into FreeCAD to build 3d models, dimensional drawings and for everyhung you coild ever imagine.

libreoffice if your using excel.

:wq!

Ardour, it’s a FOSS Digital Audio Workstation

https://ardour.org/

Godot. Start with an idle game, move onto more. Just don’t make a horror game, way too many of those and a lot of them are bad.

Obsidian (Free but not OSS) and PKM. Might help you improve how you build your knowledge, or might just trap you in an endless hunt for the perfect system.

NixOS if you’re looking for an endboss kinda thing.

Python, and you kind of want to take the long way around.

Get it up and running in its own virtual environment. On Linux and Windows this’ll just be a directory its in, and once you’ve activated that install via a terminal command everything stays self contained. It has a package manager called “pip” that will handle package and dependency management. Use pip to install spyder, pandas, matplotlib, numpy, scipy, and openpyxl. If you install anaconda this is what it’s doing under the hood, but there are licensing hiccups with using their package repos.

Anyway, however you get up and going, numpy is fast numerical storage, scipy is a lot of scientific algorithms, pandas is a data analysis library that rides on top of numpy, and openpyxl is an interface to excel files from python. Pandas will get you one line CSV file reads and writes and more complex manipulation of Excel spreadsheets. Openpyxl gets you cell by cell manipulation of a spreadsheet. Spyder is a development environment.

It can do much, much more.

I asked an LLM for a small python example with pandas and matplotlib. Load it into spyder and run it and see what happens (tip, go into the settings/preferences, IPython Console, Plotting, and change the Graphics Backend to “Qt” to get the plots in their own window).

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def generate_damped_signal():
    # 1. Setup Parameters
    fs = 50                 # 50Hz Sampling frequency
    t_max = 6.0             # Run for 6 seconds to clearly visualize it hitting 0 at 5s
    freq = 2.0              # Oscillation frequency in Hz
    
    # 2. Generate Independent Time Axis
    # 50 samples per second from 0 to t_max
    time = np.arange(0, t_max, 1/fs)
    
    # 3. Generate Dependent Damped Signal Axis
    # Using an exponential decay constant of 1.0 ensures that at t=5 seconds,
    # e^(-5) drops down to ~0.006, effectively decaying the signal to zero.
    amplitude = np.exp(-time) * np.cos(2 * np.pi * freq * time)
    
    # 4. Create Pandas DataFrame
    df = pd.DataFrame({
        'Time_Seconds': time,
        'Signal_Amplitude': amplitude
    })
    
    # 5. Export DataFrame to CSV File
    csv_filename = "damped_signal.csv"
    df.to_csv(csv_filename, index=False)
    print(f"Successfully generated DataFrame and saved to '{csv_filename}'")
    
    # 6. Plot the Data Using Matplotlib
    plt.figure(figsize=(10, 5))
    plt.plot(df['Time_Seconds'], df['Signal_Amplitude'], label='Damped Signal', color='cyan', linewidth=2)
    
    # Visual Anchors for the 5-second decay mark
    plt.axvline(x=5.0, color='red', linestyle='--', alpha=0.7, label='5-Second Decay Target')
    plt.axhline(y=0.0, color='gray', linestyle='-', alpha=0.5)
    
    # Labeling and Grid Configuration
    plt.title('Damped Signal Decay Over Time (50Hz Sampling Rate)')
    plt.xlabel('Time (Independent Axis - Seconds)')
    plt.ylabel('Signal (Dependent Axis - Amplitude)')
    plt.grid(True, linestyle=':', alpha=0.6)
    plt.legend()
    
    # Display the Plot Window
    plt.show()

if __name__ == "__main__":
    generate_damped_signal()

Kubernetes (ala k3s) you can make any collection of computers a cluster and play with automation of apps and services

KiCAD play with circuit board design with FOSS.

FPGA programing! Tons of use case for when you need near line speed but can’t afford the time and money it takes to build a full ASIC.

Make sick beats with milkytracker.

I honestly still on my to-learn list as well. But it does seem to be a solid choice.

Nushell

Blender

Godot

Docker and Docker Compose. Once you know that, you’ll be able to self host almost any FOSS.

Ansible

Bash. If you use Linux.

OpenFOAM. If you ever want to figure out the aerodynamic qualities of a cow. This may take more than a weekend.