diff --git a/doc/get_started/install_sorters.rst b/doc/get_started/install_sorters.rst
index 82bef42c14..fa3d883da9 100644
--- a/doc/get_started/install_sorters.rst
+++ b/doc/get_started/install_sorters.rst
@@ -284,7 +284,7 @@ working not only at peak times but at all times, recovering more spikes close to
 
         pip install hdbscan
         pip install spikeinterface
-        pip install numba  (or conda install numba as recommended by conda authors)
+        pip install numba (or conda install numba as recommended by conda authors)
 
 
 Tridesclous2
@@ -294,11 +294,25 @@ This is an upgraded version of Tridesclous, natively written in SpikeInterface.
 
 
 * Python
-* Requires: HDBSCAN and Numba
+* Requires: Numba
 * Authors: Samuel Garcia
 * Installation::
 
-      pip install hdbscan
+      pip install spikeinterface
+      pip install numba
+
+
+Lupin
+^^^^^
+
+This is a representative spike sorting pipeline, natively written in SpikeInterface.
+
+
+* Python
+* Requires: Numba
+* Authors: Samuel Garcia & Pierre Yger
+* Installation::
+
       pip install spikeinterface
       pip install numba
 
diff --git a/doc/how_to/analyze_neuropixels.rst b/doc/how_to/analyze_neuropixels.rst
index a1a1a6b8e2..fb37441ab3 100644
--- a/doc/how_to/analyze_neuropixels.rst
+++ b/doc/how_to/analyze_neuropixels.rst
@@ -516,7 +516,7 @@ pipeline, in SpikeInterface this is dead-simple: one function.
 -  most of sorters are wrapped from external tools (kilosort,
    kisolort2.5, spykingcircus, montainsort4 …) that often also need
    other requirements (e.g., MATLAB, CUDA)
--  some sorters are internally developed (spyekingcircus2)
+-  some sorters are internally developed (spykingcircus2, tridesclous2, lupin)
 -  external sorter can be run inside a container (docker, singularity)
    WITHOUT pre-installation
 
diff --git a/doc/modules/sorters.rst b/doc/modules/sorters.rst
index 78ac514b6d..972e77ad07 100644
--- a/doc/modules/sorters.rst
+++ b/doc/modules/sorters.rst
@@ -546,8 +546,9 @@ Internal sorters
 In 2022, we started the :py:mod:`spikeinterface.sortingcomponents` module to break into components a sorting pipeline.
 These components can be gathered to create a new sorter. We already have 2 sorters to showcase this new module:
 
-* :code:`spykingcircus2` (experimental, but ready to be tested)
-* :code:`tridesclous2` (experimental, not ready to be used)
+* :code:`spykingcircus2`
+* :code:`tridesclous2`
+* :code:`lupin`
 
 There are some benefits of using these sorters:
   * they directly handle SpikeInterface objects, so they do not need any data copy.
@@ -560,6 +561,37 @@ From the user's perspective, they behave exactly like the external sorters:
 
     sorting = run_sorter(sorter_name="spykingcircus2", recording=recording, folder="/tmp/folder")
 
+These sorters are based on the :py:mod:`spikeinterface.sortingcomponents`, allowing fast and modular implementations
+of various algorithms often encountered in spike-sorting.
+
+SpyKING-CIRCUS 2
+^^^^^^^^^^^^^^^^
+
+This is an updated version of SpyKING-CIRCUS \cite{yger2018spike} based on the modular
+components. In summary, this spike sorting pipeline uses (when motion is present) the DREDGE motion
+correction algorithm before filtering and whitening the data. On these whitened data, the chains of components
+that are used are: matched filtering for peak detection, iterative splits for clustering (Iter-HDBSCAN),
+and orthogonal matching pursuit for template reconstruction (Circus-OMP).
+
+
+TriDesClous 2
+^^^^^^^^^^^^^
+
+This is an updated version of TriDesClous based on the modular components. In summary,
+the code uses (when motion is present) the DREDGE motion correction algorithm before filtering the data
+. On these filtered data, the chains of components that are used are: locally exclusive for peak detection,
+iterative splits for clustering (Iter-ISOPLIT), and fast greedy partial deconvolution,
+only applied at peak times for template reconstruction (TDC-peeler).
+
+Lupin
+^^^^^
+
+In summary, the code uses (when motion is present) the DREDGE motion correction algorithm
+before filtering and whitening the data. On these whitened data, the chains of components that are
+used are: matched filtering for peak detection, iterative splits for clustering (Iter-ISOPLIT),
+and augmented matching pursuit for the spike deconvolution (Wobble).
+
+
 Read more in the :ref:`sorting-components-module` docs.
 
 Contributing
diff --git a/src/spikeinterface/sorters/internal/lupin.py b/src/spikeinterface/sorters/internal/lupin.py
index 40bf32e005..c35facaf11 100644
--- a/src/spikeinterface/sorters/internal/lupin.py
+++ b/src/spikeinterface/sorters/internal/lupin.py
@@ -125,6 +125,34 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
 
         apply_cmr = num_chans >= 32
 
+        if verbose:
+            version = cls.get_sorter_version()
+            lupin_ascii_art = f"""
+                            .::----::..
+                 ..:-+#%*+#%@@@@@@@+..:+#+.
+                .+@%%@@@@@@@@@@@@@@#:  .=@=
+                -@@@@@@@@@@@@@@@@@@%-. .=@*
+                .@@@@@@@@@@@@@@@@@@@*. .-%#
+                 %@@@@@@@@@@@@@@@@@@*.  :#%
+                 =%%@@@@@@@@@@@@@@%%+:  .+@.
+                 -%#@@@@@@@@@@@@@@%*==  .-@.
+                 :#%@@@@@@@@@@@@@@%#+#  .=@.
+                 .+%@@@@@@@@@@@@@@@%#@. .+@.
+                 .+*#@@@@@@@@@@@@@@@%@. .+@:
+                 .=%+@@@@@@@@@@@@@@@@*.  =%-.
+                  :#%##@@@@@@@@@@@@%%-.  :%+.
+               ..:=**%@@@@@@@@@@@@@@@:.  .%%......
+             .+#@%%%@**@%@@@@@@@@@@@*....=%@%@@@@@%=..
+            .*@@@@@@@@@@@=#@@@@@@@@@@*=:.:.#@@@@@@%%-.
+           .-%%@@@@@@@@@@@@@#+%@+-::-+=:=::+@@@@@@##-.
+           .:%@@@@@@@@@@@@@@@@@%+%##*%%%**%@@@@@@##+.
+            .+@@@@@@@@@@@@@@@@#***++***@@@@@@@@%%%=.
+             .:*@@@@@@@@%=-.....   ....:-*%%%%%+-.
+                 ..:-:..
+                        LUPIN version {version}
+            """
+            print(lupin_ascii_art)
+
         # preprocessing
         if params["apply_preprocessing"]:
             if params["apply_motion_correction"]: