Restore giscus comments on all posts (#38)

* Restore giscus comments site-wide

Wire giscus into the theme's native comments provider so every post
gets a comment box automatically. Remove the hand-pasted
github-comments include from 15 posts to avoid duplicate widgets.

* Pin url and baseurl to fix broken links

Setting `repository` activates jekyll-github-metadata, which infers a
wrong baseurl without API access. Pinning both makes local and Pages
builds behave the same.

Author: aburnim

_config.yml

@@ -21,6 +21,26 @@ twitter_username: username
 github_username: diff-use
 minimal_mistakes_skin: default
 search: true
+repository: diff-use/diff-use.github.io
+# Pin these explicitly. Setting `repository` activates jekyll-github-metadata,
+# which otherwise infers a wrong baseurl (/pages/diff-use) and breaks all links.
+url: "https://diffuse.science"
+baseurl: ""
+
+# Comments (GitHub Discussions via giscus)
+comments:
+  provider: "giscus"
+  giscus:
+    repo_id: "R_kgDOPO07gg"
+    category_name: "General"
+    category_id: "DIC_kwDOPO07gs4CtV5I"
+    discussion_term: "title"
+    reactions_enabled: "1"
+    emit_metadata: "0"
+    input_position: "bottom"
+    theme: "light"
+    strict: "0"
+    lang: "en"
 
 # Build settings
 markdown: kramdown
@@ -84,7 +104,7 @@ defaults:
       layout: single
       author_profile: true
       read_time: true
-      comments: false
+      comments: true
       share: true
       related: true
   # _pages

_posts/2025-07-22-new-website.md

@@ -11,5 +11,3 @@ comments: true
 ---
 
 Our new website is hosted by Github Pages, and its source code can be viewed [here](https://github.com/diff-use/diff-use.github.io/).
-
-{% include github-comments.html %}

_posts/2025-07-29-davinci.md

@@ -19,6 +19,3 @@ The pattern on the left side corresponds to a particular 2-state molecular motio
 The two diffuse patterns are similar, but clearly different. A major goal of the diffUSE project is to get the weak signal of diffuse scatter measured clearly enough to be analyzed unambiguously in this way.
 
 [Script](https://github.com/jmholton/UnTangle/blob/main/confpdb2diffusemtz.csh) for converting a two-conformer pdb file into diffuse scatter data.
-
-
-{% include github-comments.html %}

_posts/2025-07-29-ligands.md

@@ -18,5 +18,3 @@ The authors distill these findings into a set of very conservative lessons: prio
 3. **Input to machine learning to predict protein ligand complexes**: Curating only “gold-standard” data is going to be incredibly limiting. Disagreements between models and experimental data are opportunities to improve algorithms and better understand real-world uncertainty. I doubt that many of the structures that will be deposited by the [OpenBind consortium](https://openbind.uk/) will pass these filters.
 
 Real experimental data is messy, full of alternate conformations, unexpected chemistries, and crystallization “oddities”. Filtering exclusively for perfection may feel safe, but it also limits discovery. Even though using coordinates of the partial occupancy ligands and static alternative conformations will improve things, I'm hoping that the ML for structural biology field will increasingly embrace the mess of experimental data more directly. I’ve written about this before from [conceptual](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10947451), [practical](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12052810), and [policy](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220883) perspectives. While this trio of papers represents a tremendous teaching text that guides the reader through many of the complexities of protein-ligand data sets, I disagree with the jeremiads at the end of these papers about the potential for misuse. I truly wish there were more careful papers like this out there.   
-
-{% include github-comments.html %}

_posts/2025-07-30-wetfeet.md

@@ -32,5 +32,3 @@ Team diffUSE convened at Astera HQ in Emeryville, CA on June 23-24, 2025. The wh
 We were aided in our quest by Galen Correy (Fraser Lab at UCSF) who donated a tray full of beautiful SARS-CoV-2 Nsp3 Macrodomain crystals. Galen and collaborators have done some amazing crystallography with this system: check out their [ligand-screening campaign](https://fraserlab.com/macrodomain/) and [neutron diffraction experiments](https://pmc.ncbi.nlm.nih.gov/articles/PMC9140965/). As we discovered at ALS that afternoon, it also has beautiful diffuse scattering!
 
 The diffUSE collaboration has an ambitious open science policy. In the logbook, you can find our [beamtime notes](https://diffuse.science/logbook/beamtime/20250624-als/) and read a [preliminary analysis of the diffuse scattering](https://diffuse.science/logbook/20250624-als831-macrodomain-analysis/).
-
-{% include github-comments.html %}

_posts/2025-08-05-jobs.md

@@ -21,5 +21,3 @@ Description: The Diffuse Project is seeking a Machine Learning Infrastructure En
 
 3) Software Engineer
 Description: The Diffuse Project is seeking a Software Engineer to join a multidisciplinary team working to expand the frontier of structural biology by developing methods to capture protein motion. We are assembling a team to develop the process for collecting and interpreting this data from data collection to the final interpretation and scientific impact. You will develop open-source software products to process experimental structural biology data and to manipulate protein structural models. We are particularly interested in product minded applicants who have worked to build products for scientists or other disciplines where a close interface with your users was critical. [Apply here](https://jobs.ashbyhq.com/astera/18327a4b-acfd-46ac-8059-aa06304b0cb5)
-
-{% include github-comments.html %}

_posts/2025-08-05-lets-dance.md

@@ -42,5 +42,3 @@ The simulated electron density in the center panel shows the average picture. It
 *Methods*
 
 The [lunus](http://github.com.lanl/lunus) repository has some examples of how to [prepare](https://github.com/lanl/lunus/tree/master/examples/tutorials/crystalline_MD_prep) crystalline MD simulations and use them to analyze [Bragg](https://github.com/lanl/lunus/tree/master/examples/tutorials/crystalline_MD_analysis_bragg) and [diffuse](https://github.com/lanl/lunus/tree/master/examples/tutorials/crystalline_MD_analysis_diffuse) data. 
-
-{% include github-comments.html %}

_posts/2025-08-12-encoding.md

@@ -20,7 +20,3 @@ Exciting, much of this information on dynamics is already encoded in the raw exp
 [As we have written about before](https://pmc.ncbi.nlm.nih.gov/articles/PMC11220883/), we envision a hierarchical, ensemble-aware encoding framework designed to capture the full complexity of macromolecular dynamics. This includes distinguishing between different sources of heterogeneity, such as conformational changes versus compositional variation, and representing them in a nested structure that reflects the true physical states. Beyond encoding a single model, this would enable searches based on dynamic properties—for example, identifying all proteins where a particular loop adopts multiple conformations or where ligand binding alters flexibility in a neighboring site. To enable AI co-driven discovery, these representations must serve both human reasoning and machine learning, which means encoding at the individual model level and making this data practical for researchers to access, adapt, and integrate into their pipelines. Such a system would not only help scientists interpret complex structures but also establish community benchmarks, uncover systematic errors, and accelerate method development.
 
 Our goal is to re-engineer the encoding and infrastructure of structural biology to embrace dynamics and set the stage for the next revolution, one where machine learning models not only predict what a protein looks like but also how it moves and functions.
-
-
-
-{% include github-comments.html %}

_posts/2025-08-12-modeling.md

@@ -16,6 +16,3 @@ At present, we often model ensembles separately from Bragg peaks, the sharp, wel
 Our approach begins by improving how experimental data is integrated into modeling, building tools that incorporate both Bragg and diffuse data into optimization and machine learning loss functions and validation metrics, and improving algorithms for ensemble modeling directly from Bragg data. We are also moving towards developing machine learning algorithms that train directly on experimental data rather than using it only in the loss function.
 
 Ultimately, we envision a representation learning framework that dissolves the boundaries between experimental modalities, bringing Bragg, diffuse, and other structural data types into a single, shared space. Within this unified representation, molecular dynamics simulations informed by diffuse data will flow seamlessly into Bragg-based training and inference, allowing the strengths of each approach to amplify the other. By enabling AI models to learn jointly from heterogeneous datasets, we can unlock new levels of predictive accuracy, reveal hidden relationships between data types, and open the door to true cross-modality discovery in structural biology.
-
-
-{% include github-comments.html %}

_posts/2025-08-13-diffuse-blog-post.md

@@ -18,6 +18,3 @@ We just launched The Diffuse Initiative, a new project to experimentally study p
 As a scientist and funder, this is near and dear to my heart. I wrote up some of our thinking here: [https://seemay.substack.com/p/from-systems-operators-to-systems](https://seemay.substack.com/p/from-systems-operators-to-systems)
 
 ![Protein in motion](/assets/images/posts/20250813_seemay_substack.png)
-
-
-{% include github-comments.html %}