0.134 summaries (#237)

* Improve summary delineations

See:

- https://discourse.gohugo.io/t/potential-regression-between-0-132-and-0-134/51574/12?u=stefanv
- https://github.com/gohugoio/hugo/releases/tag/v0.134.0

* Fix rendering of images in feeds by resolving to root

https://discourse.gohugo.io/t/potential-regression-between-0-132-and-0-134/51574/8

Author: stefanv

config.yaml

@@ -11,6 +11,11 @@ markup:
   goldmark:
     renderer:
       unsafe: true
+    renderHooks:
+      image:
+        enableDefault: true
+      link:
+        enableDefault: true
 
 outputFormats:
   Atom:

content/posts/community-stories/will_tirone/index.md

@@ -8,7 +8,7 @@ displayInList: true
 author: ["Will Tirone"]
 ---
 
-In mid-2018 I started learning Python by reading textbooks and watching online tutorials. I had absolutely zero background in computer science, but it seemed interesting so I continued to try. At some point, I decided I wanted to do a master’s degree in statistics, so I began to work on more statistics-based programming. That’s when I found SciPy. I became (and still am) fascinated by the idea of open-source software that is completely free to use and supported by a community of diligent programmers. With plenty of extra time on my hands during the pandemic, I made it my goal to contribute to a Python library. My first contribution was actually to a project called [first contributions](https://github.com/firstcontributions/first-contributions) which walks you through a very basic commit and push to GitHub. That built up my confidence a bit, so I decided to tackle a SciPy issue.
+In mid-2018 I started learning Python by reading textbooks and watching online tutorials. I had absolutely zero background in computer science, but it seemed interesting so I continued to try. At some point, I decided I wanted to do a master’s degree in statistics, so I began to work on more statistics-based programming. That’s when I found SciPy. I became (and still am) fascinated by the idea of open-source software that is completely free to use and supported by a community of diligent programmers. <!--more-->With plenty of extra time on my hands during the pandemic, I made it my goal to contribute to a Python library. My first contribution was actually to a project called [first contributions](https://github.com/firstcontributions/first-contributions) which walks you through a very basic commit and push to GitHub. That built up my confidence a bit, so I decided to tackle a SciPy issue.
 It was not easy. I watched several videos and guides on how to contribute to an open-source library but got stuck many times along the way! I have to admit I felt incompetent trying to make changes to this huge library, but the maintainers and community could not have been nicer or more supportive. That’s really the magic of open source. I was confused and lost, but the (largely volunteer) community was amazing.
 
 Eventually, I managed to get a very small commit merged into the main branch of SciPy (which you can see [here](https://github.com/scipy/scipy/pull/12962)). Despite being, at most, a few lines of code, this was a huge landmark for me as a programmer. To my surprise though, in early 2021, a little badge pop up on my GitHub profile that said “Mars 2020 Helicopter Contributor”. I was confused. I didn’t recall working on helicopters, much less helicopters that flew on Mars. I still remember getting chills when I read that I contributed to a library that was used on NASA’s Mars 2020 mission, which involved the robotic helicopter Ingenuity. GitHub posted an [article](https://github.blog/2021-04-19-open-source-goes-to-mars/) explaining how about 12,000 people received a badge indicating that they had contributed to an open-source library that was used on the mission! Keep in mind that I made an absolutely tiny contribution, but I was extremely proud to be recognized in that way.

content/posts/numpy/numpy-rng/index.md

@@ -12,6 +12,8 @@ author: ["Albert Thomas <albertcthomas>"]
 
 Given the practical challenges of achieving true randomness, deterministic algorithms, known as Pseudo Random Number Generators (RNGs), are employed in science to create sequences that mimic randomness. These generators are used for simulations, experiments, and analysis where it is essential to have numbers that appear unpredictable. I want to share here what I have learned about best practices with pseudo RNGs and especially the ones available in [NumPy](https://numpy.org/).
 
+<!--more-->
+
 A pseudo RNG works by updating an internal state through a deterministic algorithm. This internal state is initialized with a value known as a seed and each update produces a number that appears randomly generated. The key here is that the process is deterministic, meaning that if you start with the same seed and apply the same algorithm, you will get the same sequence of internal states (and numbers). Despite this determinism, the resulting numbers exhibit properties of randomness, appearing unpredictable and evenly distributed. Users can either specify the seed manually, providing a degree of control over the generated sequence, or they can opt to let the RNG object automatically derive the seed from system entropy. The latter approach enhances unpredictability by incorporating external factors into the seed.
 
 I assume a certain knowledge of NumPy and that NumPy 1.17 or greater is used. The reason for this is that great new features were introduced in the [random](https://numpy.org/doc/stable/reference/random/index.html) module of version 1.17. As `numpy` is usually imported as `np`, I will sometimes use `np` instead of `numpy`. Finally, RNG will always mean pseudo RNG in the rest of this blog post.

content/posts/numpy/numpy2/index.md

@@ -19,6 +19,8 @@ launch of NumPy 2.0! This major release marks a significant milestone in the
 evolution of NumPy, bringing a wealth of enhancements and improvements to users,
 and setting the stage for future feature development.
 
+<!--more-->
+
 NumPy has improved and evolved over the past 18 years, with many old releases bringing
 significant performance, usability, and consistency improvements.
 That said, our approach for a long time has been to make only incremental changes while

content/posts/scientific-python/dev-summit-1-sparse/index.md

@@ -9,9 +9,18 @@ Sparse Arrays at the May Developer Summit in Seattle
 tags: ["Summit", "Scientific-Python", "scipy.sparse", "Sparse"]
 displayInList: true
 author: ["Dan Schult", "Stéfan van der Walt"]
----
+summary: |
+    The first [Scientific Python Developer Summit](https://blog.scientific-python.org/scientific-python/dev-summit-1/) provided an opportunity
+    for core developers from the scientific Python ecosystem to come together to:
+
+    1. improve joint infrastructure
+    2. better coordinate core projects
+    3. work on a shared strategic plan
 
-## Scientific-Python Developer Summit
+    One of the focuses of the summit was Sparse Arrays, and specifically their implementation in SciPy.
+    This post attempts to recap what happened with "sparse" at the summit
+    and a glimpse of plans for our continuing work.
+---
 
 (May 22-26, 2023, Seattle WA) --
 The first [Scientific Python Developer Summit](https://blog.scientific-python.org/scientific-python/dev-summit-1/) provided an opportunity

content/posts/scientific-python/dev-summit-1/index.md

@@ -17,6 +17,8 @@ resources:
 
 The first [Scientific Python Developer Summit](https://scientific-python.org/summits/developer/2023/) (May 22-26, 2023) brought together 34 developers at the eScience Institute at the University of Washington to develop shared infrastructure, documentation, tools, and recommendations for libraries in the Scientific Python ecosystem.
 
+<!--more-->
+
 ## Pre-summit planning
 
 Prior to the summit we held several hour-long planning meetings:

content/posts/scientific-python/translations/index.md

@@ -6,6 +6,7 @@ tags: ["Scientific-Python", "translations", "inclusion", "CZI"]
 description: "Setting up and managing translations for Scientific Python projects."
 displayInList: true
 author: ["Melissa Mendonça", "Albert Steppi"]
+summary: We are happy to announce that we have organized the necessary infrastructure and processes to allow volunteers to start translating multiple project websites. In this blog post, we will discuss how we set up manage translations for Scientific Python projects, and how you can participate in the translation and localization effort.
 ---
 
 In November 2022, [the Chan Zuckerberg Initiative (CZI) awarded the Scientific Python project with a grant to improve communications infrastructure and accessibility](https://blog.scientific-python.org/scientific-python/2022-czi-grant/). This proposal involves several key areas to improve sustainability, inclusivity and accessibility of the Scientific Python ecosystem. One of these areas is making software documentation and user interfaces available in multiple languages. We are happy to announce that we have organized the necessary infrastructure and processes to allow volunteers to start translating multiple project websites.

content/posts/scikit-learn/gpu-ci/index.md

@@ -12,6 +12,8 @@ canonicalURL: https://betatim.github.io/posts/github-action-with-gpu/
 TL;DR: If you have GPU code in your project, setup a GitHub hosted GPU runner today.
 It is fairly quick to do and will free you from having to run tests manually.
 
+<!--more-->
+
 Writing automated tests for your code base and certainly for the more complex parts
 of it has become as normal as brushing your teeth in the morning. Having a system
 that automatically runs a project's tests for every Pull Request