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@@ -528,9 +528,9 @@ <h1 id="tip-of-the-week-python-memory-management-and-troubleshooting">Tip of the
   <i class="icon fa-solid fa-circle-info"></i>
   <div class="alert-content">
     
-<p>Each month we seek to provide a software tip of the month geared towards helping you achieve your software goals. Views
+<p>These blog posts are intended to provide software tips, concepts, and tools geared towards helping you achieve your goals. Views
 expressed in the content belong to the content creators and not the organization, its affiliates, or employees. If you
-have any software questions or suggestions for an upcoming tip of the month, please don’t hesitate to reach out!</p>
+have any questions or suggestions for blog posts, please don’t hesitate to reach out!</p>
 
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diff --git a/preview/pr-28/blog/index.html b/preview/pr-28/blog/index.html
index 47f21c43b3..71a1f074c0 100644
--- a/preview/pr-28/blog/index.html
+++ b/preview/pr-28/blog/index.html
@@ -575,7 +575,7 @@ <h3 id="2024">2024</h3>
 
   
   
-  <p data-search="Tip of the Week  Python Memory Management and Troubleshooting          Each month we seek to provide a software tip of the month geared towards helping you achieve your software goals  Viewsexpressed in the content belong to the content creators and not the organization  its affiliates  or employees  If youhave any software questions or suggestions for an upcoming tip of the month  please don  8217 t hesitate to reach out   IntroductionHave you ever run Python code only to find it taking forever to complete or sometime abruptly ending with an error like  123456 Killed or killed  program exited with code  137  You may have experienced memory resource or management challenges associated with these scenarios This post will cover some computer memory definitions  how Python makes use of computer memory  and share some tools which may help with these types of challenges What is Memory Computer MemoryComputer memory is a type of computer resource available for use by software on a computerComputer memory  also sometimes known as   8220 RAM  8221  or   8220 random access memory  8221   or   8220 dynamic memory  8221  is a type of resource used by computer software on a computer   8220 Computer memory stores information  such as data and programs for immediate use in the computer    8230  Main memory operates at a high speed compared to non memory storage which is slower but less expensive and oftentimes higher in capacity    8220   Wikipedia  Computer memory                      Fixed size memory blocks may be free or used at various times  They can be thought of like reusable buckets to hold things       One way to organize computer memory is through the use of   8220 fixed size blocks  8221   also called   8220 blocks  8221  Fixed size memory blocks are chunks of memory of a certain byte size  usually all the same size  Memory blocks may be in use or free at different times Memory heaps help organize available memory on a computer for specific procedures  Heaps may have one or many memory pools Computer memory blocks may be organized in hierarchical layers to manage memory efficiently or towards a specific purpose One top level organization model for computer memory is through the use of heaps which help describe chunks of the total memory available on a computer for specific processes These heaps may be private  only available to a specific software process  or shared  available to one or many software processes  Heaps are sometimes further segmented into pools which are areas of the heap which can be used for specific purposes Memory AllocatorMemory allocators help software reserve and free computer memory resources Memory management is a concept which helps enable the shared use of computer memory to avoid challenges such as memory overuse  where all memory is in use and never shared to other software  Computer memory management often occurs through the use of a memory allocator which controls how computer memory resources are used for software Computer software is written to interact with memory allocators to use computer memory Memory allocators may be used manually  with specific directions provided on when and how to use memory resources  or automatically  with an algorithmic approach of some kind  The memory allocator usually performs the following actions with memory  in addition to others      8220 Allocation  8221   computer memory resource reservation  taking memory   This is sometimes also known as   8220 alloc  8221   or   8220 allocate memory  8221      8220 Deallocation  8221   computer memory resource freeing  giving back memory for other uses   This is sometimes also known as   8220 free  8221   or   8220 freeing memory from allocation  8221  Garbage CollectionGarbage collectors help free computer memory which is no longer referenced by software   8220 Garbage collection  GC   8221  is used to describe a type of automated memory management GC is typically used to help reduce human error  avoid unintentional system failures  and decrease development time  through less memory specific code    8220 The garbage collector attempts to reclaim memory which was allocated by the program  but is no longer referenced  such memory is called garbage   8221   Wikipedia  Garbage collection  computer science   A garbage collector often works in tandem with a memory allocator to help control computer memory resource usage in software development How Does Python Interact with Computer Memory Python OverviewA Python interpreter executes Python code and manages memory for Python procedures Python is an interpreted   8220 high level  8221  programming language  Python  What is Python   Interpreted languages are those which include an   8220 interpreter  8221  which helps execute code written in a particular way  Wikipedia  Interpreter  computing   High level languages such as Python often remove the requirement for software developers to manually perform memory management  Wikipedia  High level programming language  Python code is executed by a commonly pre packaged and downloaded binary call the Python interpreter The Python interpreter reads Python code and performs memory management as the code is executed The CPython Python interpreter is the most commonly used interpreter for Python  and what  8217 s use as a reference for other content here There are also other interpreters such as PyPy  Jython  and IronPython which all handle memory differently than the CPython interpreter Python  8217 s Memory ManagerThe Python memory manager helps manage memory for Python code executed by the Python interpreter Memory is managed for Python software processes automatically  when unspecified  or manually  when specified  through the Python interpreter The Python memory manager is an abstraction which manages memory for Python software processes through the Python interpreter  Python  Memory Management  From a high level perspective  we assume variables and other operations written in Python will automatically allocate and deallocate memory through the Python interpreter when executed Python  8217 s memory manager performs work through various memory allocators and a garbage collector  or as configured with customizations  within a private Python memory heap Python  8217 s Memory AllocatorsThe Python memory manager by default will use pymalloc internally or malloc from the system to allocate computer memory resources The Python memory manager allocates memory for use through memory allocators Python may use one or many memory allocators depending on specifications in Python code and how the Python interpreter is configured  for example  see Python  Memory Management   Default Memory Allocators  One way to understand Python memory allocators is through the following distinctions     8220 Python Memory Allocator  8221   pymalloc The Python interpreter is packaged with a specialized memory allocator called pymalloc   8220 Python has a pymalloc allocator optimized for small objects  smaller or equal to 512 bytes  with a short lifetime   8221   Python  Memory Management   The pymalloc allocator  Ultimately  pymalloc uses C standard library dynamic memory allocation functions to implement memory work   C dynamic memory allocation functions  malloc  realloc  etc  When pymalloc is disabled or a memory requirements exceed pymalloc  8217 s constraints  the Python interpreter will directly use a function from the C standard library called C standard library dynamic memory allocation functions When C standard library dynamic memory allocation functions are used by the Python interpreter  it uses the system  8217 s existing implementation of the C standard library pymalloc makes use of arenas to further organize pools within a computer memory heap It  8217 s important to note that pymalloc adds additional abstractions to how memory is organized through the use of   8220 arenas  8221  These arenas are specific to pymalloc purposes pymalloc may be disabled through the use of a special environment variable called PYTHONMALLOC  for example  to use only C standard library dynamic memory allocation functions as seen below  This same environment variable may be used with debug settings in order to help troubleshoot in depth questions Additional Python Memory AllocatorsPython code and package dependencies may stipulate the use of additional memory allocators  such as mimalloc and jemalloc outside of the Python memory manager Python provides the capability of customizing memory allocation through the use of packages See below for some notable examples of additional memory allocation possibilities   NumPy Memory AllocationNumPy uses custom C API  8217 s which are backed by C dynamic memory allocation functions  alloc  free  realloc  to help address memory management These interfaces can be controlled directly through NumPy to help manage memory effectively when using the package   PyArrow Memory AllocatorsPyArrow provides the capability to use C standard library dynamic memory allocation functions  jemalloc  or mimalloc through the PyArrow Memory Pools group of functions A default memory allocator is selected for use when PyArrow based on the operating system and the availability of the memory allocator on the system The selection of a memory allocator for use with PyArrow can be influenced by how it performs on a particular system Python Reference Counting                    Python reference counting at a simple level works through the use of object reference increments and decrements       As computer memory is allocated to Python processes the Python memory manager keeps track of these through the use of a reference counter In Python  we could label this as an   8220 Object reference counter  8221  because all data in Python is represented by objects  Python  Data model    8220   8230  CPython counts how many different places there are that have a reference to an object  Such a place could be another object  or a global  or static  C variable  or a local variable in some C function   8221   Python Developer  8217 s Guide  Garbage collector design  Python  8217 s Garbage CollectionThe Python garbage collector works as part of the Python memory manager to free memory which is no longer needed  based on reference count  Python by default uses an optional garbage collector to automatically deallocate garbage memory through the Python interpreter in CPython   8220 When an object s reference count becomes zero  the object is deallocated   8221   Python Developer  8217 s Guide  Garbage collector design Python  8217 s garbage collector focuses on collecting garbage created by pymalloc  C memory functions  as well as other memory allocators like mimalloc and jemalloc Python Tools for Observing Memory BehaviorPython Built in Toolsimport gcimport sys  set gc in debug mode for detecting memory leaksgc set debug gc DEBUG LEAK   create an int objectan object   1  show the number of uncollectable references via COLLECTEDCOLLECTED   gc collect  print f Uncollectable garbage references   COLLECTED     show the reference count for an objectprint f Reference count of  an object    sys getrefcount an object    The gc module provides an interface to the Python garbage collector In addition  the sys module provides many functions which provide information about references and other details about Python objects as they are executed through the interpreter These functions and other packages can help software developers observe memory behaviors within Python procedures Python Package  Scalene                    Scalene provides a web interface to analyze memory  CPU  and GPU resource consumption in one spot alongside suggested areas of concern       Scalene is a Python package for analyzing memory  CPU  and GPU resource consumption It provides a web interface to help visualize and understand how resources are consumed Scalene provides suggestions on which portions of your code to troubleshoot through the web interface Scalene can also be configured to work with OpenAI LLM  8217 s by way of a an OpenAI API  provided by the user Python Package  Memray                    Memray provides the ability to create and view flamegraphs which show how memory was consumed as a procedure executed       Memray is a Python package to track memory allocation within Python and compiled extension modules Memray provides a high level way to investigate memory performance and adds visualizations such as flamegraphs which contextualization of stack traces and memory allocations in one spot  Memray seeks to provide a way to overcome challenges with tracking and understanding Python and other memory allocators  such as C  C    or Rust libraries used in tandem with a Python process  Concluding ThoughtsIt  8217 s worth mentioning that this article covers only a small fraction of how and what memory is as well as how Python might make use of it Hopefully it clarifies the process and provides a way to get started with investigating memory within the software you work with Wishing you the very best in your software journey with memory ">
+  <p data-search="Tip of the Week  Python Memory Management and Troubleshooting          These blog posts are intended to provide software tips  concepts  and tools geared towards helping you achieve your goals  Viewsexpressed in the content belong to the content creators and not the organization  its affiliates  or employees  If youhave any questions or suggestions for blog posts  please don  8217 t hesitate to reach out   IntroductionHave you ever run Python code only to find it taking forever to complete or sometime abruptly ending with an error like  123456 Killed or killed  program exited with code  137  You may have experienced memory resource or management challenges associated with these scenarios This post will cover some computer memory definitions  how Python makes use of computer memory  and share some tools which may help with these types of challenges What is Memory Computer MemoryComputer memory is a type of computer resource available for use by software on a computerComputer memory  also sometimes known as   8220 RAM  8221  or   8220 random access memory  8221   or   8220 dynamic memory  8221  is a type of resource used by computer software on a computer   8220 Computer memory stores information  such as data and programs for immediate use in the computer    8230  Main memory operates at a high speed compared to non memory storage which is slower but less expensive and oftentimes higher in capacity    8220   Wikipedia  Computer memory                      Fixed size memory blocks may be free or used at various times  They can be thought of like reusable buckets to hold things       One way to organize computer memory is through the use of   8220 fixed size blocks  8221   also called   8220 blocks  8221  Fixed size memory blocks are chunks of memory of a certain byte size  usually all the same size  Memory blocks may be in use or free at different times Memory heaps help organize available memory on a computer for specific procedures  Heaps may have one or many memory pools Computer memory blocks may be organized in hierarchical layers to manage memory efficiently or towards a specific purpose One top level organization model for computer memory is through the use of heaps which help describe chunks of the total memory available on a computer for specific processes These heaps may be private  only available to a specific software process  or shared  available to one or many software processes  Heaps are sometimes further segmented into pools which are areas of the heap which can be used for specific purposes Memory AllocatorMemory allocators help software reserve and free computer memory resources Memory management is a concept which helps enable the shared use of computer memory to avoid challenges such as memory overuse  where all memory is in use and never shared to other software  Computer memory management often occurs through the use of a memory allocator which controls how computer memory resources are used for software Computer software is written to interact with memory allocators to use computer memory Memory allocators may be used manually  with specific directions provided on when and how to use memory resources  or automatically  with an algorithmic approach of some kind  The memory allocator usually performs the following actions with memory  in addition to others      8220 Allocation  8221   computer memory resource reservation  taking memory   This is sometimes also known as   8220 alloc  8221   or   8220 allocate memory  8221      8220 Deallocation  8221   computer memory resource freeing  giving back memory for other uses   This is sometimes also known as   8220 free  8221   or   8220 freeing memory from allocation  8221  Garbage CollectionGarbage collectors help free computer memory which is no longer referenced by software   8220 Garbage collection  GC   8221  is used to describe a type of automated memory management GC is typically used to help reduce human error  avoid unintentional system failures  and decrease development time  through less memory specific code    8220 The garbage collector attempts to reclaim memory which was allocated by the program  but is no longer referenced  such memory is called garbage   8221   Wikipedia  Garbage collection  computer science   A garbage collector often works in tandem with a memory allocator to help control computer memory resource usage in software development How Does Python Interact with Computer Memory Python OverviewA Python interpreter executes Python code and manages memory for Python procedures Python is an interpreted   8220 high level  8221  programming language  Python  What is Python   Interpreted languages are those which include an   8220 interpreter  8221  which helps execute code written in a particular way  Wikipedia  Interpreter  computing   High level languages such as Python often remove the requirement for software developers to manually perform memory management  Wikipedia  High level programming language  Python code is executed by a commonly pre packaged and downloaded binary call the Python interpreter The Python interpreter reads Python code and performs memory management as the code is executed The CPython Python interpreter is the most commonly used interpreter for Python  and what  8217 s use as a reference for other content here There are also other interpreters such as PyPy  Jython  and IronPython which all handle memory differently than the CPython interpreter Python  8217 s Memory ManagerThe Python memory manager helps manage memory for Python code executed by the Python interpreter Memory is managed for Python software processes automatically  when unspecified  or manually  when specified  through the Python interpreter The Python memory manager is an abstraction which manages memory for Python software processes through the Python interpreter  Python  Memory Management  From a high level perspective  we assume variables and other operations written in Python will automatically allocate and deallocate memory through the Python interpreter when executed Python  8217 s memory manager performs work through various memory allocators and a garbage collector  or as configured with customizations  within a private Python memory heap Python  8217 s Memory AllocatorsThe Python memory manager by default will use pymalloc internally or malloc from the system to allocate computer memory resources The Python memory manager allocates memory for use through memory allocators Python may use one or many memory allocators depending on specifications in Python code and how the Python interpreter is configured  for example  see Python  Memory Management   Default Memory Allocators  One way to understand Python memory allocators is through the following distinctions     8220 Python Memory Allocator  8221   pymalloc The Python interpreter is packaged with a specialized memory allocator called pymalloc   8220 Python has a pymalloc allocator optimized for small objects  smaller or equal to 512 bytes  with a short lifetime   8221   Python  Memory Management   The pymalloc allocator  Ultimately  pymalloc uses C standard library dynamic memory allocation functions to implement memory work   C dynamic memory allocation functions  malloc  realloc  etc  When pymalloc is disabled or a memory requirements exceed pymalloc  8217 s constraints  the Python interpreter will directly use a function from the C standard library called C standard library dynamic memory allocation functions When C standard library dynamic memory allocation functions are used by the Python interpreter  it uses the system  8217 s existing implementation of the C standard library pymalloc makes use of arenas to further organize pools within a computer memory heap It  8217 s important to note that pymalloc adds additional abstractions to how memory is organized through the use of   8220 arenas  8221  These arenas are specific to pymalloc purposes pymalloc may be disabled through the use of a special environment variable called PYTHONMALLOC  for example  to use only C standard library dynamic memory allocation functions as seen below  This same environment variable may be used with debug settings in order to help troubleshoot in depth questions Additional Python Memory AllocatorsPython code and package dependencies may stipulate the use of additional memory allocators  such as mimalloc and jemalloc outside of the Python memory manager Python provides the capability of customizing memory allocation through the use of packages See below for some notable examples of additional memory allocation possibilities   NumPy Memory AllocationNumPy uses custom C API  8217 s which are backed by C dynamic memory allocation functions  alloc  free  realloc  to help address memory management These interfaces can be controlled directly through NumPy to help manage memory effectively when using the package   PyArrow Memory AllocatorsPyArrow provides the capability to use C standard library dynamic memory allocation functions  jemalloc  or mimalloc through the PyArrow Memory Pools group of functions A default memory allocator is selected for use when PyArrow based on the operating system and the availability of the memory allocator on the system The selection of a memory allocator for use with PyArrow can be influenced by how it performs on a particular system Python Reference Counting                    Python reference counting at a simple level works through the use of object reference increments and decrements       As computer memory is allocated to Python processes the Python memory manager keeps track of these through the use of a reference counter In Python  we could label this as an   8220 Object reference counter  8221  because all data in Python is represented by objects  Python  Data model    8220   8230  CPython counts how many different places there are that have a reference to an object  Such a place could be another object  or a global  or static  C variable  or a local variable in some C function   8221   Python Developer  8217 s Guide  Garbage collector design  Python  8217 s Garbage CollectionThe Python garbage collector works as part of the Python memory manager to free memory which is no longer needed  based on reference count  Python by default uses an optional garbage collector to automatically deallocate garbage memory through the Python interpreter in CPython   8220 When an object s reference count becomes zero  the object is deallocated   8221   Python Developer  8217 s Guide  Garbage collector design Python  8217 s garbage collector focuses on collecting garbage created by pymalloc  C memory functions  as well as other memory allocators like mimalloc and jemalloc Python Tools for Observing Memory BehaviorPython Built in Toolsimport gcimport sys  set gc in debug mode for detecting memory leaksgc set debug gc DEBUG LEAK   create an int objectan object   1  show the number of uncollectable references via COLLECTEDCOLLECTED   gc collect  print f Uncollectable garbage references   COLLECTED     show the reference count for an objectprint f Reference count of  an object    sys getrefcount an object    The gc module provides an interface to the Python garbage collector In addition  the sys module provides many functions which provide information about references and other details about Python objects as they are executed through the interpreter These functions and other packages can help software developers observe memory behaviors within Python procedures Python Package  Scalene                    Scalene provides a web interface to analyze memory  CPU  and GPU resource consumption in one spot alongside suggested areas of concern       Scalene is a Python package for analyzing memory  CPU  and GPU resource consumption It provides a web interface to help visualize and understand how resources are consumed Scalene provides suggestions on which portions of your code to troubleshoot through the web interface Scalene can also be configured to work with OpenAI LLM  8217 s by way of a an OpenAI API  provided by the user Python Package  Memray                    Memray provides the ability to create and view flamegraphs which show how memory was consumed as a procedure executed       Memray is a Python package to track memory allocation within Python and compiled extension modules Memray provides a high level way to investigate memory performance and adds visualizations such as flamegraphs which contextualization of stack traces and memory allocations in one spot  Memray seeks to provide a way to overcome challenges with tracking and understanding Python and other memory allocators  such as C  C    or Rust libraries used in tandem with a Python process  Concluding ThoughtsIt  8217 s worth mentioning that this article covers only a small fraction of how and what memory is as well as how Python might make use of it Hopefully it clarifies the process and provides a way to get started with investigating memory within the software you work with Wishing you the very best in your software journey with memory ">
     
 Have you ever run Python code only to find it taking forever to complete or sometime abruptly ending with an error like: 123456 Killed or killed (program exited with code: 137)?
 You may have experienced memory resource or management challenges associated with these scenarios.
diff --git a/preview/pr-28/feed.xml b/preview/pr-28/feed.xml
index f6c55097d9..845d92a834 100644
--- a/preview/pr-28/feed.xml
+++ b/preview/pr-28/feed.xml
@@ -1,13 +1,13 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="/set-website/preview/pr-28/feed.xml" rel="self" type="application/atom+xml" /><link href="/set-website/preview/pr-28/" rel="alternate" type="text/html" /><updated>2024-01-25T20:22:35+00:00</updated><id>/set-website/preview/pr-28/feed.xml</id><title type="html">Software Engineering Team</title><subtitle>The software engineering team of the Department of Biomedical Informatics at the University of Colorado Anschutz</subtitle><entry><title type="html">Tip of the Month: Python Memory Management and Troubleshooting</title><link href="/set-website/preview/pr-28/2024/01/22/Python-Memory-Management-and-Troubleshooting.html" rel="alternate" type="text/html" title="Tip of the Month: Python Memory Management and Troubleshooting" /><published>2024-01-22T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2024/01/22/Python-Memory-Management-and-Troubleshooting</id><content type="html" xml:base="/set-website/preview/pr-28/2024/01/22/Python-Memory-Management-and-Troubleshooting.html"><![CDATA[<h1 id="tip-of-the-week-python-memory-management-and-troubleshooting">Tip of the Week: Python Memory Management and Troubleshooting</h1>
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="/set-website/preview/pr-28/feed.xml" rel="self" type="application/atom+xml" /><link href="/set-website/preview/pr-28/" rel="alternate" type="text/html" /><updated>2024-01-25T20:40:58+00:00</updated><id>/set-website/preview/pr-28/feed.xml</id><title type="html">Software Engineering Team</title><subtitle>The software engineering team of the Department of Biomedical Informatics at the University of Colorado Anschutz</subtitle><entry><title type="html">Tip of the Month: Python Memory Management and Troubleshooting</title><link href="/set-website/preview/pr-28/2024/01/22/Python-Memory-Management-and-Troubleshooting.html" rel="alternate" type="text/html" title="Tip of the Month: Python Memory Management and Troubleshooting" /><published>2024-01-22T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2024/01/22/Python-Memory-Management-and-Troubleshooting</id><content type="html" xml:base="/set-website/preview/pr-28/2024/01/22/Python-Memory-Management-and-Troubleshooting.html"><![CDATA[<h1 id="tip-of-the-week-python-memory-management-and-troubleshooting">Tip of the Week: Python Memory Management and Troubleshooting</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
   <i class="icon fa-solid fa-circle-info"></i>
   <div class="alert-content">
     
-<p>Each month we seek to provide a software tip of the month geared towards helping you achieve your software goals. Views
+<p>These blog posts are intended to provide software tips, concepts, and tools geared towards helping you achieve your goals. Views
 expressed in the content belong to the content creators and not the organization, its affiliates, or employees. If you
-have any software questions or suggestions for an upcoming tip of the month, please don&#8217;t hesitate to reach out!</p>
+have any questions or suggestions for blog posts, please don&#8217;t hesitate to reach out!</p>
 
   </div>
 </div>
@@ -373,7 +373,7 @@ Memray seeks to provide a way to overcome challenges with tracking and understan
 
 <p>It&#8217;s worth mentioning that this article covers only a small fraction of how and what memory is as well as how Python might make use of it.
 Hopefully it clarifies the process and provides a way to get started with investigating memory within the software you work with.
-Wishing you the very best in your software journey with memory!</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-month" /><category term="python" /><category term="memory" /><category term="memory-management" /><category term="memory-allocators" /><summary type="html"><![CDATA[Tip of the Week: Python Memory Management and Troubleshooting]]></summary></entry><entry><title type="html">Tip of the Week: Codesgiving - Open-source Contribution Walkthrough</title><link href="/set-website/preview/pr-28/2023/11/15/Codesgiving-Open-source-Contribution-Walkthrough.html" rel="alternate" type="text/html" title="Tip of the Week: Codesgiving - Open-source Contribution Walkthrough" /><published>2023-11-15T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/11/15/Codesgiving-Open-source-Contribution-Walkthrough</id><content type="html" xml:base="/set-website/preview/pr-28/2023/11/15/Codesgiving-Open-source-Contribution-Walkthrough.html"><![CDATA[<h1 id="tip-of-the-week-codesgiving---open-source-contribution-walkthrough">Tip of the Week: Codesgiving - Open-source Contribution Walkthrough</h1>
+Wishing you the very best in your software journey with memory!</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-month" /><category term="python" /><category term="memory" /><category term="memory-management" /><category term="memory-allocators" /><summary type="html"><![CDATA[Tip of the Week: Python Memory Management and Troubleshooting]]></summary></entry><entry><title type="html">Tip of the Week: Codesgiving - Open-source Contribution Walkthrough</title><link href="/set-website/preview/pr-28/2023/11/15/Codesgiving-Open-source-Contribution-Walkthrough.html" rel="alternate" type="text/html" title="Tip of the Week: Codesgiving - Open-source Contribution Walkthrough" /><published>2023-11-15T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/11/15/Codesgiving-Open-source-Contribution-Walkthrough</id><content type="html" xml:base="/set-website/preview/pr-28/2023/11/15/Codesgiving-Open-source-Contribution-Walkthrough.html"><![CDATA[<h1 id="tip-of-the-week-codesgiving---open-source-contribution-walkthrough">Tip of the Week: Codesgiving - Open-source Contribution Walkthrough</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -808,7 +808,7 @@ Best wishes on your open-source journey and happy Codesgiving!</p>
 
 <ul>
   <li>Top Image: Französischer Obstgarten zur Erntezeit (Le verger) by Charles-François Daubigny (cropped). (Source: <a href="https://commons.wikimedia.org/wiki/File:French_Orchard_at_Harvest_Time_(Le_verger)_(SM_1444).png">Wikimedia Commons</a>)</li>
-</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="codesgiving" /><category term="open-source" /><category term="contributions" /><category term="development-strategy" /><category term="process" /><summary type="html"><![CDATA[Tip of the Week: Codesgiving - Open-source Contribution Walkthrough]]></summary></entry><entry><title type="html">Tip of the Week: Data Quality Validation through Software Testing Techniques</title><link href="/set-website/preview/pr-28/2023/10/04/Data-Quality-Validation.html" rel="alternate" type="text/html" title="Tip of the Week: Data Quality Validation through Software Testing Techniques" /><published>2023-10-04T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/10/04/Data-Quality-Validation</id><content type="html" xml:base="/set-website/preview/pr-28/2023/10/04/Data-Quality-Validation.html"><![CDATA[<h1 id="tip-of-the-week-data-quality-validation-through-software-testing-techniques">Tip of the Week: Data Quality Validation through Software Testing Techniques</h1>
+</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="codesgiving" /><category term="open-source" /><category term="contributions" /><category term="development-strategy" /><category term="process" /><summary type="html"><![CDATA[Tip of the Week: Codesgiving - Open-source Contribution Walkthrough]]></summary></entry><entry><title type="html">Tip of the Week: Data Quality Validation through Software Testing Techniques</title><link href="/set-website/preview/pr-28/2023/10/04/Data-Quality-Validation.html" rel="alternate" type="text/html" title="Tip of the Week: Data Quality Validation through Software Testing Techniques" /><published>2023-10-04T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/10/04/Data-Quality-Validation</id><content type="html" xml:base="/set-website/preview/pr-28/2023/10/04/Data-Quality-Validation.html"><![CDATA[<h1 id="tip-of-the-week-data-quality-validation-through-software-testing-techniques">Tip of the Week: Data Quality Validation through Software Testing Techniques</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -1081,7 +1081,7 @@ Data managed by DVC allows software to be built in such a way that version preco
 These ideas apply to both databases and also more broadly through DVC mentioned above (among other tools) via the concept <a href="https://en.wikipedia.org/wiki/Code_as_data">&#8220;data as code&#8221;</a>.
 Implementing this idea has several advantages from source versioning, visibility, and replicability.
 One tool which implements these ideas is <a href="https://github.com/flyway/flyway">Flyway</a> which can manage and implement SQL-based files as part of software data precondition validation.
-A lightweight alternative to using Flyway is sometimes to include a SQL file which creates related database objects and becomes data documentation.</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="data-quality" /><category term="data-testing" /><category term="testing" /><category term="design-by-contract" /><category term="component-based-design" /><category term="hoare-logic" /><category term="data-as-code" /><summary type="html"><![CDATA[Tip of the Week: Data Quality Validation through Software Testing Techniques]]></summary></entry><entry><title type="html">Tip of the Week: Python Packaging as Publishing</title><link href="/set-website/preview/pr-28/2023/09/05/Python-Packaging-as-Publishing.html" rel="alternate" type="text/html" title="Tip of the Week: Python Packaging as Publishing" /><published>2023-09-05T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/09/05/Python-Packaging-as-Publishing</id><content type="html" xml:base="/set-website/preview/pr-28/2023/09/05/Python-Packaging-as-Publishing.html"><![CDATA[<h1 id="tip-of-the-week-python-packaging-as-publishing">Tip of the Week: Python Packaging as Publishing</h1>
+A lightweight alternative to using Flyway is sometimes to include a SQL file which creates related database objects and becomes data documentation.</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="data-quality" /><category term="data-testing" /><category term="testing" /><category term="design-by-contract" /><category term="component-based-design" /><category term="hoare-logic" /><category term="data-as-code" /><summary type="html"><![CDATA[Tip of the Week: Data Quality Validation through Software Testing Techniques]]></summary></entry><entry><title type="html">Tip of the Week: Python Packaging as Publishing</title><link href="/set-website/preview/pr-28/2023/09/05/Python-Packaging-as-Publishing.html" rel="alternate" type="text/html" title="Tip of the Week: Python Packaging as Publishing" /><published>2023-09-05T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/09/05/Python-Packaging-as-Publishing</id><content type="html" xml:base="/set-website/preview/pr-28/2023/09/05/Python-Packaging-as-Publishing.html"><![CDATA[<h1 id="tip-of-the-week-python-packaging-as-publishing">Tip of the Week: Python Packaging as Publishing</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -1557,7 +1557,7 @@ Building package-name <span class="o">(</span>0.1.0<span class="o">)</span>
   - Built package_name-0.1.0-py3-none-any.whl
 </code></pre></div></div>
 
-<p>Poetry readies source-code and pre-compiled versions of our code for distribution platforms like PyPI by using the <a href="https://python-poetry.org/docs/cli/#build"><code class="language-plaintext highlighter-rouge">poetry build ...</code> command</a>. We&#8217;ll cover more on these files and distribution steps with a later post!</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="Python" /><category term="packaging" /><category term="publishing" /><category term="software-design" /><category term="understandability" /><category term="environment-management" /><summary type="html"><![CDATA[Tip of the Week: Python Packaging as Publishing]]></summary></entry><entry><title type="html">Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster</title><link href="/set-website/preview/pr-28/2023/07/07/Using-Python-and-Anaconda-with-the-Alpine-HPC-Cluster.html" rel="alternate" type="text/html" title="Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster" /><published>2023-07-07T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/07/07/Using-Python-and-Anaconda-with-the-Alpine-HPC-Cluster</id><content type="html" xml:base="/set-website/preview/pr-28/2023/07/07/Using-Python-and-Anaconda-with-the-Alpine-HPC-Cluster.html"><![CDATA[<h1 id="tip-of-the-week-using-python-and-anaconda-with-the-alpine-hpc-cluster">Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster</h1>
+<p>Poetry readies source-code and pre-compiled versions of our code for distribution platforms like PyPI by using the <a href="https://python-poetry.org/docs/cli/#build"><code class="language-plaintext highlighter-rouge">poetry build ...</code> command</a>. We&#8217;ll cover more on these files and distribution steps with a later post!</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="Python" /><category term="packaging" /><category term="publishing" /><category term="software-design" /><category term="understandability" /><category term="environment-management" /><summary type="html"><![CDATA[Tip of the Week: Python Packaging as Publishing]]></summary></entry><entry><title type="html">Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster</title><link href="/set-website/preview/pr-28/2023/07/07/Using-Python-and-Anaconda-with-the-Alpine-HPC-Cluster.html" rel="alternate" type="text/html" title="Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster" /><published>2023-07-07T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/07/07/Using-Python-and-Anaconda-with-the-Alpine-HPC-Cluster</id><content type="html" xml:base="/set-website/preview/pr-28/2023/07/07/Using-Python-and-Anaconda-with-the-Alpine-HPC-Cluster.html"><![CDATA[<h1 id="tip-of-the-week-using-python-and-anaconda-with-the-alpine-hpc-cluster">Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -1847,7 +1847,7 @@ In this example we&#8217;ll use <a href="https://www.globus.org/">Globus</a> as
   <li><a href="https://docs.github.com/en/get-started/getting-started-with-git">GitHub: Getting started with Git</a></li>
   <li><a href="https://slurm.schedmd.com/">Slurm Documentation</a></li>
   <li><a href="https://docs.globus.org/">Globus Documentation</a></li>
-</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="Python" /><category term="Anaconda" /><category term="high-performance-compute" /><category term="slurm" /><category term="globus" /><summary type="html"><![CDATA[Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster]]></summary></entry><entry><title type="html">Tip of the Week: Automate Software Workflows with GitHub Actions</title><link href="/set-website/preview/pr-28/2023/03/15/Automate-Software-Workflows-with-Github-Actions.html" rel="alternate" type="text/html" title="Tip of the Week: Automate Software Workflows with GitHub Actions" /><published>2023-03-15T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/03/15/Automate-Software-Workflows-with-Github-Actions</id><content type="html" xml:base="/set-website/preview/pr-28/2023/03/15/Automate-Software-Workflows-with-Github-Actions.html"><![CDATA[<h1 id="tip-of-the-week-automate-software-workflows-with-github-actions">Tip of the Week: Automate Software Workflows with GitHub Actions</h1>
+</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="Python" /><category term="Anaconda" /><category term="high-performance-compute" /><category term="slurm" /><category term="globus" /><summary type="html"><![CDATA[Tip of the Week: Using Python and Anaconda with the Alpine HPC Cluster]]></summary></entry><entry><title type="html">Tip of the Week: Automate Software Workflows with GitHub Actions</title><link href="/set-website/preview/pr-28/2023/03/15/Automate-Software-Workflows-with-Github-Actions.html" rel="alternate" type="text/html" title="Tip of the Week: Automate Software Workflows with GitHub Actions" /><published>2023-03-15T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/03/15/Automate-Software-Workflows-with-Github-Actions</id><content type="html" xml:base="/set-website/preview/pr-28/2023/03/15/Automate-Software-Workflows-with-Github-Actions.html"><![CDATA[<h1 id="tip-of-the-week-automate-software-workflows-with-github-actions">Tip of the Week: Automate Software Workflows with GitHub Actions</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -2013,7 +2013,7 @@ Reference this example for a brief demonstration of how GitHub Actions and Dagge
 
 <h2 id="closing-remarks">Closing Remarks</h2>
 
-<p>Using GitHub Actions through the above methods can help automate your technical work and increase the quality of your code with sometimes very little additional effort. Saving time through this form of automation can provide additional flexibility accomplish more complex work which requires your attention (perhaps <a href="https://cu-dbmi.github.io/set-website/2023/01/17/Timebox-Your-Software-Work.html">using timeboxing techniques</a>). Even small amounts of time saved can turn into large opportunities for other work. On this note, be sure to explore how GitHub Actions can improve things for your software endeavors.</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="github" /><category term="workflow" /><category term="github-actions" /><category term="continuous-integration" /><summary type="html"><![CDATA[Tip of the Week: Automate Software Workflows with GitHub Actions]]></summary></entry><entry><title type="html">Tip of the Week: Branch, Review, and Learn</title><link href="/set-website/preview/pr-28/2023/02/13/Branch-Review-and-Learn.html" rel="alternate" type="text/html" title="Tip of the Week: Branch, Review, and Learn" /><published>2023-02-13T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/02/13/Branch-Review-and-Learn</id><content type="html" xml:base="/set-website/preview/pr-28/2023/02/13/Branch-Review-and-Learn.html"><![CDATA[<h1 id="tip-of-the-week-branch-review-and-learn">Tip of the Week: Branch, Review, and Learn</h1>
+<p>Using GitHub Actions through the above methods can help automate your technical work and increase the quality of your code with sometimes very little additional effort. Saving time through this form of automation can provide additional flexibility accomplish more complex work which requires your attention (perhaps <a href="https://cu-dbmi.github.io/set-website/2023/01/17/Timebox-Your-Software-Work.html">using timeboxing techniques</a>). Even small amounts of time saved can turn into large opportunities for other work. On this note, be sure to explore how GitHub Actions can improve things for your software endeavors.</p>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="github" /><category term="workflow" /><category term="github-actions" /><category term="continuous-integration" /><summary type="html"><![CDATA[Tip of the Week: Automate Software Workflows with GitHub Actions]]></summary></entry><entry><title type="html">Tip of the Week: Branch, Review, and Learn</title><link href="/set-website/preview/pr-28/2023/02/13/Branch-Review-and-Learn.html" rel="alternate" type="text/html" title="Tip of the Week: Branch, Review, and Learn" /><published>2023-02-13T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/02/13/Branch-Review-and-Learn</id><content type="html" xml:base="/set-website/preview/pr-28/2023/02/13/Branch-Review-and-Learn.html"><![CDATA[<h1 id="tip-of-the-week-branch-review-and-learn">Tip of the Week: Branch, Review, and Learn</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -2170,7 +2170,7 @@ flowchart LR
   <li><a href="https://github.com/git-guides">Github: Git Guide</a></li>
   <li><a href="https://www.atlassian.com/git/tutorials/what-is-version-control">Atlassian: What is version control?</a></li>
   <li><a href="https://docs.gitlab.com/ee/topics/git/">Git Lab: Git</a></li>
-</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="git" /><category term="branching" /><category term="pull-requests" /><category term="merging" /><summary type="html"><![CDATA[Tip of the Week: Branch, Review, and Learn]]></summary></entry><entry><title type="html">Tip of the Week: Software Linting with R</title><link href="/set-website/preview/pr-28/2023/01/30/Software-Linting-with-R.html" rel="alternate" type="text/html" title="Tip of the Week: Software Linting with R" /><published>2023-01-30T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/01/30/Software-Linting-with-R</id><content type="html" xml:base="/set-website/preview/pr-28/2023/01/30/Software-Linting-with-R.html"><![CDATA[<h1 id="tip-of-the-week-software-linting-with-r">Tip of the Week: Software Linting with R</h1>
+</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="git" /><category term="branching" /><category term="pull-requests" /><category term="merging" /><summary type="html"><![CDATA[Tip of the Week: Branch, Review, and Learn]]></summary></entry><entry><title type="html">Tip of the Week: Software Linting with R</title><link href="/set-website/preview/pr-28/2023/01/30/Software-Linting-with-R.html" rel="alternate" type="text/html" title="Tip of the Week: Software Linting with R" /><published>2023-01-30T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/01/30/Software-Linting-with-R</id><content type="html" xml:base="/set-website/preview/pr-28/2023/01/30/Software-Linting-with-R.html"><![CDATA[<h1 id="tip-of-the-week-software-linting-with-r">Tip of the Week: Software Linting with R</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -2287,7 +2287,7 @@ flowchart LR
   <li><a href="https://en.wikipedia.org/wiki/Continuous_testing">continuous testing</a></li>
   <li><a href="https://docs.github.com/en/actions">Github Actions</a></li>
   <li><a href="https://github.com/pre-commit/action">an example Github Action</a></li>
-</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="r" /><category term="static-analysis" /><category term="linting" /><category term="continuous-testing" /><summary type="html"><![CDATA[Tip of the Week: Software Linting with R]]></summary></entry><entry><title type="html">Tip of the Week: Timebox Your Software Work</title><link href="/set-website/preview/pr-28/2023/01/17/Timebox-Your-Software-Work.html" rel="alternate" type="text/html" title="Tip of the Week: Timebox Your Software Work" /><published>2023-01-17T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/01/17/Timebox-Your-Software-Work</id><content type="html" xml:base="/set-website/preview/pr-28/2023/01/17/Timebox-Your-Software-Work.html"><![CDATA[<h1 id="tip-of-the-week-timebox-your-software-work">Tip of the Week: Timebox Your Software Work</h1>
+</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="r" /><category term="static-analysis" /><category term="linting" /><category term="continuous-testing" /><summary type="html"><![CDATA[Tip of the Week: Software Linting with R]]></summary></entry><entry><title type="html">Tip of the Week: Timebox Your Software Work</title><link href="/set-website/preview/pr-28/2023/01/17/Timebox-Your-Software-Work.html" rel="alternate" type="text/html" title="Tip of the Week: Timebox Your Software Work" /><published>2023-01-17T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/01/17/Timebox-Your-Software-Work</id><content type="html" xml:base="/set-website/preview/pr-28/2023/01/17/Timebox-Your-Software-Work.html"><![CDATA[<h1 id="tip-of-the-week-timebox-your-software-work">Tip of the Week: Timebox Your Software Work</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
@@ -2410,7 +2410,7 @@ Use timeboxing techniques such as <a href="https://en.wikipedia.org/wiki/Pomodor
   <li><a href="https://en.wikipedia.org/wiki/52/17_rule">52/17 Rule</a></li>
   <li><a href="https://en.wikipedia.org/wiki/Modularity">Modularity</a></li>
   <li><a href="https://github.com/features/issues">Github Issues</a></li>
-</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="timeboxing" /><category term="timeblocking" /><category term="productivity" /><category term="modularization" /><category term="projectmanagement" /><summary type="html"><![CDATA[Tip of the Week: Timebox Your Software Work]]></summary></entry><entry><title type="html">Tip of the Week: Linting Documentation as Code</title><link href="/set-website/preview/pr-28/2023/01/03/Linting-Documentation-as-Code.html" rel="alternate" type="text/html" title="Tip of the Week: Linting Documentation as Code" /><published>2023-01-03T00:00:00+00:00</published><updated>2024-01-25T20:21:53+00:00</updated><id>/set-website/preview/pr-28/2023/01/03/Linting-Documentation-as-Code</id><content type="html" xml:base="/set-website/preview/pr-28/2023/01/03/Linting-Documentation-as-Code.html"><![CDATA[<h1 id="tip-of-the-week-linting-documentation-as-code">Tip of the Week: Linting Documentation as Code</h1>
+</ul>]]></content><author><name>dave-bunten</name></author><category term="tip-of-the-week" /><category term="software" /><category term="timeboxing" /><category term="timeblocking" /><category term="productivity" /><category term="modularization" /><category term="projectmanagement" /><summary type="html"><![CDATA[Tip of the Week: Timebox Your Software Work]]></summary></entry><entry><title type="html">Tip of the Week: Linting Documentation as Code</title><link href="/set-website/preview/pr-28/2023/01/03/Linting-Documentation-as-Code.html" rel="alternate" type="text/html" title="Tip of the Week: Linting Documentation as Code" /><published>2023-01-03T00:00:00+00:00</published><updated>2024-01-25T20:40:19+00:00</updated><id>/set-website/preview/pr-28/2023/01/03/Linting-Documentation-as-Code</id><content type="html" xml:base="/set-website/preview/pr-28/2023/01/03/Linting-Documentation-as-Code.html"><![CDATA[<h1 id="tip-of-the-week-linting-documentation-as-code">Tip of the Week: Linting Documentation as Code</h1>
 
 <div class="alert" style="--color: #0ea5e9">
   
diff --git a/preview/pr-28/sitemap.xml b/preview/pr-28/sitemap.xml
index 34e8a668c1..a555d6a6a7 100644
--- a/preview/pr-28/sitemap.xml
+++ b/preview/pr-28/sitemap.xml
@@ -2,86 +2,86 @@
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 <url>
 <loc>/set-website/preview/pr-28/members/dave-bunten.html</loc>
-<lastmod>2024-01-25T20:21:53+00:00</lastmod>
+<lastmod>2024-01-25T20:40:19+00:00</lastmod>
 </url>
 <url>
 <loc>/set-website/preview/pr-28/members/faisal-alquaddoomi.html</loc>
-<lastmod>2024-01-25T20:21:53+00:00</lastmod>
+<lastmod>2024-01-25T20:40:19+00:00</lastmod>
 </url>
 <url>
 <loc>/set-website/preview/pr-28/members/vincent-rubinetti.html</loc>
-<lastmod>2024-01-25T20:21:53+00:00</lastmod>
+<lastmod>2024-01-25T20:40:19+00:00</lastmod>
 </url>
 <url>
 <loc>/set-website/preview/pr-28/2022/10/17/Use-Linting-Tools-to-Save-Time.html</loc>
-<lastmod>2024-01-25T20:21:53+00:00</lastmod>
+<lastmod>2024-01-25T20:40:19+00:00</lastmod>
 </url>
 <url>
 <loc>/set-website/preview/pr-28/2022/11/27/Diagrams-as-Code.html</loc>
-<lastmod>2024-01-25T20:21:53+00:00</lastmod>
+<lastmod>2024-01-25T20:40:19+00:00</lastmod>
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 <loc>/set-website/preview/pr-28/2022/12/05/Data-Engineering-with-SQL-Arrow-and-DuckDB.html</loc>
-<lastmod>2024-01-25T20:21:53+00:00</lastmod>
+<lastmod>2024-01-25T20:40:19+00:00</lastmod>
 </url>
 <url>
 <loc>/set-website/preview/pr-28/2022/12/12/Remove-Unused-Code-to-Avoid-Decay.html</loc>
-<lastmod>2024-01-25T20:21:53+00:00</lastmod>
+<lastmod>2024-01-25T20:40:19+00:00</lastmod>
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 </url>
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