diff --git a/docs/apps/FastFourierTransforms/1d/app.py b/docs/apps/FastFourierTransforms/1d/app.py
deleted file mode 100644
index fbf5227..0000000
--- a/docs/apps/FastFourierTransforms/1d/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import matplotlib.pyplot as plt
-from palmerpenguins import load_penguins
-from shiny.express import input, render, ui
-
-ui.input_slider("n", "Number of bins", 1, 100, 20)
-
-@render.plot(alt="A histogram")
-def plot():
-    df = load_penguins()
-    mass = df["body_mass_g"]
-
-    fig, ax = plt.subplots()
-    ax.hist(mass, input.n(), density=True)
-    ax.set_title("Palmer Penguin Masses")
-    ax.set_xlabel("Mass (g)")
-    ax.set_ylabel("Density")
-
-    return fig
\ No newline at end of file
diff --git a/docs/apps/FastFourierTransforms/1d/requirements.txt b/docs/apps/FastFourierTransforms/1d/requirements.txt
deleted file mode 100644
index a46db3e..0000000
--- a/docs/apps/FastFourierTransforms/1d/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-numpy
-palmerpenguins
-matplotlib
\ No newline at end of file
diff --git a/docs/apps/FastFourierTransforms/2d/app.py b/docs/apps/FastFourierTransforms/2d/app.py
deleted file mode 100644
index fbf5227..0000000
--- a/docs/apps/FastFourierTransforms/2d/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import matplotlib.pyplot as plt
-from palmerpenguins import load_penguins
-from shiny.express import input, render, ui
-
-ui.input_slider("n", "Number of bins", 1, 100, 20)
-
-@render.plot(alt="A histogram")
-def plot():
-    df = load_penguins()
-    mass = df["body_mass_g"]
-
-    fig, ax = plt.subplots()
-    ax.hist(mass, input.n(), density=True)
-    ax.set_title("Palmer Penguin Masses")
-    ax.set_xlabel("Mass (g)")
-    ax.set_ylabel("Density")
-
-    return fig
\ No newline at end of file
diff --git a/docs/apps/FastFourierTransforms/2d/requirements.txt b/docs/apps/FastFourierTransforms/2d/requirements.txt
deleted file mode 100644
index a46db3e..0000000
--- a/docs/apps/FastFourierTransforms/2d/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-numpy
-palmerpenguins
-matplotlib
\ No newline at end of file
diff --git a/docs/apps/FastFourierTransforms/app.md b/docs/apps/FastFourierTransforms/app.md
deleted file mode 100644
index ad72fde..0000000
--- a/docs/apps/FastFourierTransforms/app.md
+++ /dev/null
@@ -1,88 +0,0 @@
----
-authors:
-  - ptuemmler
-categories:
-  - Mathematics
-  - Numerics
-tags:
-  - Code
-  - Fourier Transform
-date: 2025-07-04
-hide:
-  - toc
-draft: true
----
-# Fast Fourier Transforms 
-
-The [Fast Fourier Transform (FFT)](https://en.wikipedia.org/wiki/Fast_Fourier_transform) is an efficient algorithm to compute the Discrete Fourier Transform (DFT) and its inverse. It is widely used in signal processing, image analysis, and solving partial differential equations. A typical issue is the physically correct scaling and calculation of respective axes.
-A common problem is the scaling of the axes, which can lead to confusion when interpreting the results. This app provides a simple interface to visualize the FFT and its inverse, allowing you to explore the effects of different parameters on the resulting spectra.
-<!-- more -->
-The simplest case of the FFT is the transformation of a 1D signal, which can be extended to 2D signals (e.g. images) and higher dimensions. The app allows you to visualize the FFT of a 1D signal and its inverse, as well as the scaling of the axes.
-For the 1D case, we can use the following python and matlab code snippets to compute the FFT and its inverse with the correct scaling and also return the respective axes if desired:
-
-=== "python"
-
-    ``` python title="fft.py" linenums="1"
-    from numpy import fft
-
-    def fft_1d(axis, signal, return_axes=False):
-        """
-        Compute the 1D FFT of a signal and return the transformed signal and axes.
-        
-        Parameters
-        ----------
-        axis : 1D array-like
-            The axis of the signal.
-        signal : 1D array-like
-            The signal to be transformed.
-        return_axes : bool, optional
-            If True, return the transformed axes and signal. Default is False.
-        
-        Returns
-        -------
-        tuple
-            If return_axes is True, return a tuple of the transformed axes and signal.
-            Otherwise, return only the transformed signal.
-        """
-        transformed_signal = fft.fft(signal)
-        if return_axes:
-            transformed_axis = fft.fftfreq(len(axis), axis[1] - axis[0])
-            return transformed_axis, transformed_signal
-        else:
-            return transformed_signal
-
-    def ifft_1d(axis, transformed_signal, return_axes=False):
-        """
-        Compute the inverse 1D FFT of a transformed signal and return the original signal and axes.
-        
-        Parameters
-        ----------
-        axis : 1D array-like
-            The axis of the transformed signal.
-        transformed_signal : 1D array-like
-            The transformed signal to be inverted.
-        return_axes : bool, optional
-            If True, return the original axes and signal. Default is False.
-        
-        Returns
-        -------
-        tuple
-            If return_axes is True, return a tuple of the original axes and signal.
-            Otherwise, return only the original signal.
-        """
-        original_signal = fft.ifft(transformed_signal)
-        if return_axes:
-            original_axis = fft.fftfreq(len(axis), axis[1] - axis[0])
-            return original_axis, original_signal
-        else:
-            return original_signal
-    ```
-
-=== "matlab"
-
-    ``` matlab
-    
-    ```
-
-{{embed_app("100%", "500px", "1d")}}
-{{embed_app("100%", "500px", "2d")}}