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diff --git a/index.json b/index.json
diff --git a/index.xml b/index.xml
@@ -1647,6 +1647,8 @@ nilss_prob = NILSSProblem(prob_attractor, NILSS(nseg, nstep), g)
 &lt;a href=&#34;https://summerofcode.withgoogle.com/organizations/5765643267211264/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;NumFocus&lt;/a&gt;/
 &lt;a href=&#34;https://sciml.ai&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciML&lt;/a&gt; organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.&lt;/p&gt;
 &lt;p&gt;This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).&lt;/p&gt;
+&lt;p&gt;Note: Please check the 
+&lt;a href=&#34;https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciMLSensitivity.jl docs&lt;/a&gt; for a maintained neural hybrid DE tutorial!&lt;/p&gt;
 &lt;h2 id=&#34;hybrid-differential-equations&#34;&gt;Hybrid Differential Equations&lt;/h2&gt;
 &lt;p&gt;DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 &lt;a href=&#34;https://diffeq.sciml.ai/stable/features/callback_functions/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;callbacks&lt;/a&gt;. Evidently, the incorporation of discontinuities allows a user to specify changes (&lt;em&gt;events&lt;/em&gt;) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by 

diff --git a/post/hybridde/index.html b/post/hybridde/index.html
@@ -662,6 +662,8 @@ <h1>Neural Hybrid Differential Equations</h1>
 <a href="https://summerofcode.withgoogle.com/organizations/5765643267211264/" target="_blank" rel="noopener">NumFocus</a>/
 <a href="https://sciml.ai" target="_blank" rel="noopener">SciML</a> organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.</p>
 <p>This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).</p>
+<p>Note: Please check the 
+<a href="https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/" target="_blank" rel="noopener">SciMLSensitivity.jl docs</a> for a maintained neural hybrid DE tutorial!</p>
 <h2 id="hybrid-differential-equations">Hybrid Differential Equations</h2>
 <p>DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 <a href="https://diffeq.sciml.ai/stable/features/callback_functions/" target="_blank" rel="noopener">callbacks</a>. Evidently, the incorporation of discontinuities allows a user to specify changes (<em>events</em>) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by 

diff --git a/post/index.xml b/post/index.xml
@@ -1622,6 +1622,8 @@ nilss_prob = NILSSProblem(prob_attractor, NILSS(nseg, nstep), g)
 &lt;a href=&#34;https://summerofcode.withgoogle.com/organizations/5765643267211264/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;NumFocus&lt;/a&gt;/
 &lt;a href=&#34;https://sciml.ai&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciML&lt;/a&gt; organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.&lt;/p&gt;
 &lt;p&gt;This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).&lt;/p&gt;
+&lt;p&gt;Note: Please check the 
+&lt;a href=&#34;https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciMLSensitivity.jl docs&lt;/a&gt; for a maintained neural hybrid DE tutorial!&lt;/p&gt;
 &lt;h2 id=&#34;hybrid-differential-equations&#34;&gt;Hybrid Differential Equations&lt;/h2&gt;
 &lt;p&gt;DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 &lt;a href=&#34;https://diffeq.sciml.ai/stable/features/callback_functions/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;callbacks&lt;/a&gt;. Evidently, the incorporation of discontinuities allows a user to specify changes (&lt;em&gt;events&lt;/em&gt;) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by 

diff --git a/tag/adjoint-sensitivity-methods/index.xml b/tag/adjoint-sensitivity-methods/index.xml
@@ -1046,6 +1046,8 @@ nilss_prob = NILSSProblem(prob_attractor, NILSS(nseg, nstep), g)
 &lt;a href=&#34;https://summerofcode.withgoogle.com/organizations/5765643267211264/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;NumFocus&lt;/a&gt;/
 &lt;a href=&#34;https://sciml.ai&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciML&lt;/a&gt; organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.&lt;/p&gt;
 &lt;p&gt;This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).&lt;/p&gt;
+&lt;p&gt;Note: Please check the 
+&lt;a href=&#34;https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciMLSensitivity.jl docs&lt;/a&gt; for a maintained neural hybrid DE tutorial!&lt;/p&gt;
 &lt;h2 id=&#34;hybrid-differential-equations&#34;&gt;Hybrid Differential Equations&lt;/h2&gt;
 &lt;p&gt;DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 &lt;a href=&#34;https://diffeq.sciml.ai/stable/features/callback_functions/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;callbacks&lt;/a&gt;. Evidently, the incorporation of discontinuities allows a user to specify changes (&lt;em&gt;events&lt;/em&gt;) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by 

diff --git a/tag/event-handling/index.xml b/tag/event-handling/index.xml
@@ -627,6 +627,8 @@ $$ &lt;a href=&#34;#fnref:6&#34; class=&#34;footnote-backref&#34; role=&#34;doc-
 &lt;a href=&#34;https://summerofcode.withgoogle.com/organizations/5765643267211264/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;NumFocus&lt;/a&gt;/
 &lt;a href=&#34;https://sciml.ai&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciML&lt;/a&gt; organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.&lt;/p&gt;
 &lt;p&gt;This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).&lt;/p&gt;
+&lt;p&gt;Note: Please check the 
+&lt;a href=&#34;https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciMLSensitivity.jl docs&lt;/a&gt; for a maintained neural hybrid DE tutorial!&lt;/p&gt;
 &lt;h2 id=&#34;hybrid-differential-equations&#34;&gt;Hybrid Differential Equations&lt;/h2&gt;
 &lt;p&gt;DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 &lt;a href=&#34;https://diffeq.sciml.ai/stable/features/callback_functions/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;callbacks&lt;/a&gt;. Evidently, the incorporation of discontinuities allows a user to specify changes (&lt;em&gt;events&lt;/em&gt;) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by 

diff --git a/tag/gsoc-2021/index.xml b/tag/gsoc-2021/index.xml
@@ -1622,6 +1622,8 @@ nilss_prob = NILSSProblem(prob_attractor, NILSS(nseg, nstep), g)
 &lt;a href=&#34;https://summerofcode.withgoogle.com/organizations/5765643267211264/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;NumFocus&lt;/a&gt;/
 &lt;a href=&#34;https://sciml.ai&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciML&lt;/a&gt; organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.&lt;/p&gt;
 &lt;p&gt;This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).&lt;/p&gt;
+&lt;p&gt;Note: Please check the 
+&lt;a href=&#34;https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciMLSensitivity.jl docs&lt;/a&gt; for a maintained neural hybrid DE tutorial!&lt;/p&gt;
 &lt;h2 id=&#34;hybrid-differential-equations&#34;&gt;Hybrid Differential Equations&lt;/h2&gt;
 &lt;p&gt;DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 &lt;a href=&#34;https://diffeq.sciml.ai/stable/features/callback_functions/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;callbacks&lt;/a&gt;. Evidently, the incorporation of discontinuities allows a user to specify changes (&lt;em&gt;events&lt;/em&gt;) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by 

diff --git a/tag/hybrid-differential-equations/index.xml b/tag/hybrid-differential-equations/index.xml
@@ -627,6 +627,8 @@ $$ &lt;a href=&#34;#fnref:6&#34; class=&#34;footnote-backref&#34; role=&#34;doc-
 &lt;a href=&#34;https://summerofcode.withgoogle.com/organizations/5765643267211264/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;NumFocus&lt;/a&gt;/
 &lt;a href=&#34;https://sciml.ai&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciML&lt;/a&gt; organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.&lt;/p&gt;
 &lt;p&gt;This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).&lt;/p&gt;
+&lt;p&gt;Note: Please check the 
+&lt;a href=&#34;https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciMLSensitivity.jl docs&lt;/a&gt; for a maintained neural hybrid DE tutorial!&lt;/p&gt;
 &lt;h2 id=&#34;hybrid-differential-equations&#34;&gt;Hybrid Differential Equations&lt;/h2&gt;
 &lt;p&gt;DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 &lt;a href=&#34;https://diffeq.sciml.ai/stable/features/callback_functions/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;callbacks&lt;/a&gt;. Evidently, the incorporation of discontinuities allows a user to specify changes (&lt;em&gt;events&lt;/em&gt;) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by 

diff --git a/tag/julia/index.xml b/tag/julia/index.xml
@@ -867,6 +867,8 @@ nilss_prob = NILSSProblem(prob_attractor, NILSS(nseg, nstep), g)
 &lt;a href=&#34;https://summerofcode.withgoogle.com/organizations/5765643267211264/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;NumFocus&lt;/a&gt;/
 &lt;a href=&#34;https://sciml.ai&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciML&lt;/a&gt; organization and comprises adjoint sensitivity methods for discontinuities, shadowing methods for chaotic dynamics, symbolically generated adjoint methods, and further AD tooling within the Julia Language.&lt;/p&gt;
 &lt;p&gt;This first post aims to illustrate our new (adjoint) sensitivity analysis tools with respect to event handling in (ordinary) differential equations (DEs).&lt;/p&gt;
+&lt;p&gt;Note: Please check the 
+&lt;a href=&#34;https://docs.sciml.ai/SciMLSensitivity/dev/examples/hybrid_jump/hybrid_diffeq/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;SciMLSensitivity.jl docs&lt;/a&gt; for a maintained neural hybrid DE tutorial!&lt;/p&gt;
 &lt;h2 id=&#34;hybrid-differential-equations&#34;&gt;Hybrid Differential Equations&lt;/h2&gt;
 &lt;p&gt;DEs with additional explicit or implicit discontinuities are called hybrid DEs. Within the SciML software suite, such discontinuities may be incorporated into DE models by 
 &lt;a href=&#34;https://diffeq.sciml.ai/stable/features/callback_functions/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;callbacks&lt;/a&gt;. Evidently, the incorporation of discontinuities allows a user to specify changes (&lt;em&gt;events&lt;/em&gt;) in the system, i.e., changes of the state or the parameters of the DE, which cannot be modeled by a plain ordinary DE. While explicit events can be described by