diff --git a/workshops/latex/beispieldokument/zeta_0.tex b/workshops/latex/beispieldokument/zeta_0.tex
index 5628fda06c61166d5596fe178a2dcd9390851090..129dea00ce632f3193750fabaef0c9bb7de67306 100644
--- a/workshops/latex/beispieldokument/zeta_0.tex
+++ b/workshops/latex/beispieldokument/zeta_0.tex
@@ -40,21 +40,30 @@ Na klar.
 Im vorigen Unterabschnitt haben wir bemerkt, dass wir die Summe auffassen 
 können als einen Grenzwert der Zetafunktion.
 
-Epilog
-
-Dieser Teil soll nachher nicht im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-· Strukturierte Dokumente sind einfach mit \LaTeX.
-
-· Makros machen einem das Leben leicht.
-
-· Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-
-· Referenzen auf alles mögliche, also (Unter)abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-
-· Eigentlich ist das alles gar nicht so schwierig.
+Epilog -- Ein paar Programmiertipps von Rob Pike
+
+Most programs are too complicated - that is, more complex than they
+need to be to solve their problems efficiently.  Why? Mostly
+it's because of bad design, but I will skip that issue here because
+it's a big one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+
+You can't tell where a program is going to spend its
+    time. Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+
+Measure. Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+
+Fancy algorithms are slow when n is small, and n
+    is usually small. Fancy algorithms have big constants. Until you know that
+    n is frequently going to be big, don't get fancy.  (Even if n does get
+    big, use 2. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+
+Fancy algorithms are buggier than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_1.tex b/workshops/latex/beispieldokument/zeta_1.tex
index fb7149ae7ba6838a7b5ea059de54b2c4f594d859..b6550a57a7112a04eac17085bec2d6520c934cc6 100644
--- a/workshops/latex/beispieldokument/zeta_1.tex
+++ b/workshops/latex/beispieldokument/zeta_1.tex
@@ -42,21 +42,30 @@ Na klar.
 Im vorigen Unterabschnitt haben wir bemerkt, dass wir die Summe auffassen 
 können als einen Grenzwert der Zetafunktion.
 
-Epilog
-
-Dieser Teil soll nachher \textcolor{red}{nicht} im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-· Strukturierte Dokumente sind einfach mit \LaTeX.
-
-· Makros machen einem das Leben leicht.
-
-· Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-
-· Referenzen auf alles mögliche, also (Unter)abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-
-· Eigentlich ist das alles gar nicht so schwierig.
+Epilog -- Ein paar Programmiertipps von Rob Pike
+
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+
+\textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+
+\textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+
+\textcolor{red}{Fancy algorithms are slow when n is small}, and n
+    is usually small. Fancy algorithms have big constants. Until you know that
+    n is frequently going to be big, don't get fancy.  (Even if n does get
+    big, use 2. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+
+\textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_2.tex b/workshops/latex/beispieldokument/zeta_2.tex
index f5d930cbb7f735e5f3b90d3ed660c09f9dff93a9..bf5f07f2e4d3e324910f8330c3e9185cedb9c0d7 100644
--- a/workshops/latex/beispieldokument/zeta_2.tex
+++ b/workshops/latex/beispieldokument/zeta_2.tex
@@ -44,21 +44,29 @@ Ergebnis führt.
 Im vorigen Unterabschnitt haben wir bemerkt, dass wir die Summe auffassen 
 können als einen Grenzwert der Zetafunktion.
 
-\section*{Epilog}
-
-Dieser Teil soll nachher \textcolor{red}{nicht} im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-· Strukturierte Dokumente sind einfach mit \LaTeX.
-
-· Makros machen einem das Leben leicht.
-
-· Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-
-· Referenzen auf alles mögliche, also (Unter)abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-
-· Eigentlich ist das alles gar nicht so schwierig.
+\section*{Epilog -- Ein paar Programmiertipps von Rob Pike}
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+
+\textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+
+\textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+
+\textcolor{red}{Fancy algorithms are slow when n is small}, and n
+    is usually small. Fancy algorithms have big constants. Until you know that
+    n is frequently going to be big, don't get fancy.  (Even if n does get
+    big, use 2. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+
+\textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_3.tex b/workshops/latex/beispieldokument/zeta_3.tex
index 0c12bbf92938dbc716c2a94deda64e25a96943b9..d059491c476614e5ffe8559172680640f4b943d4 100644
--- a/workshops/latex/beispieldokument/zeta_3.tex
+++ b/workshops/latex/beispieldokument/zeta_3.tex
@@ -46,19 +46,27 @@ Ergebnis führt.
 Im vorigen Unterabschnitt haben wir bemerkt, dass wir die Summe auffassen 
 können als einen Grenzwert der Zetafunktion.
 
-\section*{Epilog}
-
-Dieser Teil soll nachher \textcolor{red}{nicht} im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-\begin{itemize}
-  \item Strukturierte Dokumente sind einfach mit \LaTeX.
-  \item Makros machen einem das Leben leicht.
-  \item Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-  \item Referenzen auf alles mögliche, also (Unter)abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-  \item Eigentlich ist das alles gar nicht so schwierig.
-\end{itemize}
+\section*{Epilog -- Ein paar Programmiertipps von Rob Pike}
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+\begin{enumerate}
+  \item  \textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+  \item  \textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+  \item  \textcolor{red}{Fancy algorithms are slow when n is small}, and n
+    is usually small. Fancy algorithms have big constants. Until you know that
+    n is frequently going to be big, don't get fancy.  (Even if n does get
+    big, use 2. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+  \item  \textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
+\end{enumerate}
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_4.tex b/workshops/latex/beispieldokument/zeta_4.tex
index 0cf7829fc97baada2a31ce98c47d4d534f84dc23..349fea8529713bccea7c1a9cd101c0b8d4c77d49 100644
--- a/workshops/latex/beispieldokument/zeta_4.tex
+++ b/workshops/latex/beispieldokument/zeta_4.tex
@@ -49,19 +49,27 @@ Ergebnis führt.
 Im vorigen Unterabschnitt haben wir bemerkt, dass wir die Summe auffassen 
 können als einen Grenzwert der Zetafunktion, $\zeta(-1) = -1/12$.
 
-\section*{Epilog}
-
-Dieser Teil soll nachher \textcolor{red}{nicht} im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-\begin{itemize}
-  \item Strukturierte Dokumente sind einfach mit \LaTeX.
-  \item Makros machen einem das Leben leicht.
-  \item Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-  \item Referenzen auf alles mögliche, also (Unter)$^n$abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-  \item Eigentlich ist das alles gar nicht so schwierig.
-\end{itemize}
+\section*{Epilog -- Ein paar Programmiertipps von Rob Pike}
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+\begin{enumerate}
+  \item  \textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+  \item  \textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+  \item  \textcolor{red}{Fancy algorithms are slow when $n$ is small}, and $n$
+    is usually small. Fancy algorithms have big constants. Until you know that
+    $n$ is frequently going to be big, don't get fancy.  (Even if $n$ does get
+    big, use 2. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+  \item  \textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
+\end{enumerate}
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_5.tex b/workshops/latex/beispieldokument/zeta_5.tex
index f3ea6f52b6f4f5d87a120d9fa115b9106c4b1a00..c3756901ef7b007c861afdb0b89d551f6e0291fd 100644
--- a/workshops/latex/beispieldokument/zeta_5.tex
+++ b/workshops/latex/beispieldokument/zeta_5.tex
@@ -68,29 +68,30 @@ Tabelle 1: Wert der $n$-ten Partialsumme
   \end{tabular}
 \end{center}
 
-\section*{Epilog}
-
-Dieser Teil soll nachher \textcolor{red}{nicht} im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-\begin{itemize}
-  \item Strukturierte Dokumente sind einfach mit \LaTeX.
-  \item Makros machen einem das Leben leicht.
-  \item Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-  \item Referenzen auf alles mögliche, also (Unter)$^n$abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-  \item Eigentlich ist das alles gar nicht so schwierig.
-\end{itemize}
-
-\vfill
-% Der Befehl vfill fügt einen vertikalen Abstand ein, der den übrigen Platz
-% ausfüllt
+\section*{Epilog -- Ein paar Programmiertipps von Rob Pike}
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+\begin{enumerate}
+  \item  \textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+  \item  \textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+  \item  \textcolor{red}{Fancy algorithms are slow when $n$ is small}, and $n$
+    is usually small. Fancy algorithms have big constants. Until you know that
+    $n$ is frequently going to be big, don't get fancy.  (Even if $n$ does get
+    big, use 2. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+  \item  \textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
+\end{enumerate}
 \begin{center}
 \includegraphics[width=0.5\textwidth]{ctanlion.eps}
 \end{center}
-\vfill
-% die beiden Abstände werden gleichmäßig verteilt. Das Bild erscheint also in
-% der Mitte.
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_6.tex b/workshops/latex/beispieldokument/zeta_6.tex
index 03c78fc6b7ec9542801ac09146d52cbab0a196eb..bbf8953a5575dda7cc8bdc0e821ccff8d28e6101 100644
--- a/workshops/latex/beispieldokument/zeta_6.tex
+++ b/workshops/latex/beispieldokument/zeta_6.tex
@@ -69,29 +69,30 @@ können als einen Grenzwert der Zetafunktion, $\zeta(-1) = -1/12$.
   \end{center}
 \end{table}
 
-\section*{Epilog}
-
-Dieser Teil soll nachher \textcolor{red}{nicht} im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-\begin{itemize}
-  \item Strukturierte Dokumente sind einfach mit \LaTeX.
-  \item Makros machen einem das Leben leicht.
-  \item Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-  \item Referenzen auf alles mögliche, also (Unter)$^n$abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-  \item Eigentlich ist das alles gar nicht so schwierig.
-\end{itemize}
-
-\vfill
-% Der Befehl vfill fügt einen vertikalen Abstand ein, der den übrigen Platz
-% ausfüllt
+\section*{Epilog -- Ein paar Programmiertipps von Rob Pike}
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+\begin{enumerate}
+  \item  \textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+  \item  \textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+  \item  \textcolor{red}{Fancy algorithms are slow when $n$ is small}, and $n$
+    is usually small. Fancy algorithms have big constants. Until you know that
+    $n$ is frequently going to be big, don't get fancy.  (Even if $n$ does get
+    big, use 2. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+  \item  \textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
+\end{enumerate}
 \begin{center}
 \includegraphics[width=0.5\textwidth]{ctanlion.eps}
 \end{center}
-\vfill
-% die beiden Abstände werden gleichmäßig verteilt. Das Bild erscheint also in
-% der Mitte.
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_7.tex b/workshops/latex/beispieldokument/zeta_7.tex
index 22cfe4dbdca603c425c17a1451d04cec9cddaf1c..61850307792aa1b05dd80672354f435b33966c10 100644
--- a/workshops/latex/beispieldokument/zeta_7.tex
+++ b/workshops/latex/beispieldokument/zeta_7.tex
@@ -72,29 +72,30 @@ können als einen Grenzwert der Zetafunktion, $\zeta(-1) = -1/12$.
   \end{center}
 \end{table}
 
-\section*{Epilog}
-
-Dieser Teil soll nachher \textcolor{red}{nicht} im Inhaltsverzeichnis auftauchen, und 
-erhält damit auch keine Nummer. \\
-Außerdem:
-
-\begin{itemize}
-  \item Strukturierte Dokumente sind einfach mit \LaTeX.
-  \item Makros machen einem das Leben leicht.
-  \item Formeln -- und auch der ganze Rest -- sehen (noch nicht) toll aus.
-  \item Referenzen auf alles mögliche, also (Unter)$^n$abschnitte,
-    Gleichungen, Abbildungen, Tabellen, ... sind automatisch richtig.
-  \item Eigentlich ist das alles gar nicht so schwierig.
-\end{itemize}
-
-\vfill
-% Der Befehl vfill fügt einen vertikalen Abstand ein, der den übrigen Platz
-% ausfüllt
+\section*{Epilog -- Ein paar Programmiertipps von Rob Pike}
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
+\begin{enumerate}
+  \item  \textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+  \item  \label{measure}\textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+  \item  \textcolor{red}{Fancy algorithms are slow when $n$ is small}, and $n$
+    is usually small. Fancy algorithms have big constants. Until you know that
+    $n$ is frequently going to be big, don't get fancy.  (Even if $n$ does get
+    big, use \ref{measure}. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+  \item  \textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
+\end{enumerate}
 \begin{center}
 \includegraphics[width=0.5\textwidth]{ctanlion.eps}
 \end{center}
-\vfill
-% die beiden Abstände werden gleichmäßig verteilt. Das Bild erscheint also in
-% der Mitte.
 
 \end{document}
diff --git a/workshops/latex/beispieldokument/zeta_8.tex b/workshops/latex/beispieldokument/zeta_8.tex
index 21c601f1607c2ebf5ef9babc4a568943ee28c91c..87cde03e3a9cf87855087c2d14ca5bd61084d16a 100644
--- a/workshops/latex/beispieldokument/zeta_8.tex
+++ b/workshops/latex/beispieldokument/zeta_8.tex
@@ -71,24 +71,26 @@ können als einen Grenzwert der Zetafunktion, $\riemann$.
 \end{table}
 
 \section*{Epilog -- Ein paar Programmiertipps von Rob Pike}
-Most programs are too complicated - that is, more complex than they need to be
-to solve their problems efficiently.  Why? Mostly it's because of bad design,
-but I will skip that issue here because it's a big one.  But programs are often
-complicated at the microscopic level, and that is something I can address here.
+Most programs are too \textbf{complicated} - that is, more complex than they
+\textit{need} to be to solve their problems \textit{efficiently}.  Why? Mostly
+it's because of \textbf{bad design}, but I will skip that issue here because
+it's a \textbf{\textcolor{red}{big}} one.  But programs are often complicated at the
+microscopic level, and that is something I can address here.
 \begin{enumerate}
-  \item  You can't tell where a program is going to spend its
-      time.  Bottlenecks occur in surprising places, so don't try to second
-      guess and put in a speed hack until you've proven that's where the
-      bottleneck is.
-  \item  Measure.  Don't tune for speed until you've measured, and even then don't
-    unless one part of the code overwhelms the rest.
-  \item  Fancy algorithms are slow when $n$ is small, and $n$ is usually small.
-    Fancy algorithms have big constants. Until you know that $n$ is frequently
-    going to be big, don't get fancy.  (Even if $n$ does get big, use Rule 2
-    first.)   For example, binary trees are always faster than splay trees for
-    workaday problems.
-  \item  Fancy algorithms are buggier than simple ones, and they're much harder to
-    implement.  Use simple algorithms as well as simple data structures.
+  \item  \textcolor{red}{You can't tell where a program is going to spend its
+    time.}  Bottlenecks occur in surprising places, so don't try to second
+    guess and put in a speed hack until you've proven that's where the
+    bottleneck is.
+  \item  \label{measure}\textcolor{red}{Measure.} Don't tune for speed until you've measured,
+    and even then don't unless one part of the code overwhelms the rest.
+  \item  \textcolor{red}{Fancy algorithms are slow when $n$ is small}, and $n$
+    is usually small. Fancy algorithms have big constants. Until you know that
+    $n$ is frequently going to be big, don't get fancy.  (Even if $n$ does get
+    big, use \ref{measure}. first.) For example, binary trees are always faster
+    than splay trees for workaday problems.
+  \item  \textcolor{red}{Fancy algorithms are buggier} than simple ones, and
+    they're much harder to implement.  Use simple algorithms as well as simple
+    data structures.
 \end{enumerate}
 \begin{center}
 \includegraphics[width=0.5\textwidth]{ctanlion.eps}
diff --git a/workshops/latex/latex.tex b/workshops/latex/latex.tex
index 2b8eae2521dc3f2a3d0f7d626b63ef03f5acdd05..523df7dff80e5fc29be36f70299e2bc9e59b1c84 100644
--- a/workshops/latex/latex.tex
+++ b/workshops/latex/latex.tex
@@ -671,6 +671,7 @@
   \end{tikzpicture}
     \vspace{-6pt}
   \end{center}
+  \structure{URLs:} \highlightRed{\textbackslash url}\verb+{http://fsmpi.eu/latex}+ \\
   \structure{Besondere Zeichen}
   \begin{itemize}
     \item {\bfseries\color{MidnightBlue}\%, "{}}: \texttt{\textbackslash\%}, \verb+"{}+