diff --git a/.gitignore b/.gitignore
index 8db7a3f..0a0be6b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@ env/
 .DS_Store
 node_modules/
 package-lock.json
+.ipynb_checkpoints/
diff --git a/README.md b/README.md
index a8c85d0..6e363c6 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ You probably want to run ruby from a version manager like `chruby`, see [here](h
   - add humans.txt https://humanstxt.org/
   - fix the OG tags so that https://cards-dev.twitter.com/validator works
   - consider switching to using pandoc as a markdown renderer
-  - switch so that the blog is served from the root instead of doing a redirect
+  - setup webmentions https://aarongustafson.github.io/jekyll-webmention_io/
 
 ## Notes
 [Installation](https://jekyllrb.com/docs/installation/macos/)
diff --git a/_config.yml b/_config.yml
index 46ea1d2..147c2e7 100644
--- a/_config.yml
+++ b/_config.yml
@@ -27,4 +27,4 @@ whitelist:
   - flexible_include
 
 feed:
-  posts_limit: 20
\ No newline at end of file
+  posts_limit: 100
\ No newline at end of file
diff --git a/_cv_entries/2_msc.md b/_cv_entries/2_msc.md
index d1b985b..c8b189f 100644
--- a/_cv_entries/2_msc.md
+++ b/_cv_entries/2_msc.md
@@ -6,16 +6,103 @@ location: Trinity College, Cambridge
 subtitle: "Imaging Magnetic Phenomena with Scanning Diamond Magnetometry"
 
 image: /assets/images/vector_magnet_angle_view.png
-alt: "A vector magnet that I designed."
+alt: "A render of vector magnet that I designed in a CAD program."
+
+image_markup: <model-viewer style="height:250px;" src="/assets/blog/vector_magnet/vector_magnet.glb" ar ar-modes="webxr scene-viewer quick-look" camera-controls poster="/assets/blog/vector_magnet/vector_magnet.png" shadow-intensity="1" environment-image="/assets/blog/vector_magnet/aircraft_workshop_01_1k.hdr" camera-orbit="-169.8deg 78.57deg 0.8881m" field-of-view="32.55deg" interaction-prompt="none" auto-rotate> </model-viewer>
 
 layout: cv_entry
 read_more: true
+assets: /assets/blog/vector_magnet
 ---
 
 Supervisor: Professor Mete Atatüre<br>
 <br>
 The project centered around the use of a Nitrogen-Vancancy defect in a nanoscale diamond to detect magnetic fields with ultra high resolution. We experimented with mounting such a nano-diamond to the tip of an atomic force microscope in order to produce field images. I built a 3d vector magnetometer in order to determine the axis of a defect in a nano-diamond.
 
-Check out a little interactive model of the magnetometer below.
+Check out a little interactive model of the magnetometer below. The device has three pairs of copper Helmholtz coils that generate controlled, linear, magnetic fields in all three directions.
 
-<iframe src="https://myhub.autodesk360.com/ue2a56e59/shares/public/SH7f1edQT22b515c761efc56dd943429df14?mode=embed" width="640" height="480" allowfullscreen="true" webkitallowfullscreen="true" mozallowfullscreen="true"  frameborder="0"></iframe>
+<figure>
+<model-viewer style="height:500px;" src="{{ page.assets }}/vector_magnet.glb" ar ar-modes="webxr scene-viewer quick-look" camera-controls poster="{{ page.assets }}/vector_magnet.png" shadow-intensity="1" environment-image="{{ page.assets }}/aircraft_workshop_01_1k.hdr" camera-orbit="-169.8deg 78.57deg 0.8881m" field-of-view="32.55deg" interaction-prompt="none" auto-rotate> </model-viewer>
+
+<!-- <model-viewer id="vector-magnet-whole-2" style="height:500px;" src="{{ page.assets }}/vector_magnet.glb" ar ar-modes="webxr scene-viewer quick-look" camera-controls poster="{{ page.assets }}/vector_magnet.png" shadow-intensity="1" environment-image="{{ page.assets }}/aircraft_workshop_01_1k.hdr" interaction-prompt="none" interpolation-decay="300" auto-rotate> 
+
+<button class="Hotspot" style="disaplay:none;" slot="hotspot-1" data-position="0.3140708429455987m 0.2809109652738389m -0.32504316945989636m" data-normal="-3.85185900533595e-30m -1.343588384327496e-7m 0.9999999999999911m" data-visibility-attribute="visible"><div class="left HotspotAnnotation">AFM Tip</div></button>
+
+<button class="Hotspot" style="disaplay:none;" slot="hotspot-3" data-position="0.32587557211500007m 0.2711873555719334m -0.32504317169367186m" data-normal=" -4.575935640098142e-8m -2.0164410743149838e-7m 0.9999999999999787m" data-visibility-attribute="visible"><div class="HotspotAnnotation">Microscope Objective</div></button>
+
+<button class="Hotspot" style="disaplay:none;" slot="hotspot-4" data-position="0.3186977335100803m 0.2788147714959883m -0.3250432252613787m" data-normal=" 1.5099584039393812e-7m -1.34358838432748e-7m 0.9999999999999796m" data-visibility-attribute="visible"><div class="HotspotAnnotation">PCB Excitation Coil</div></button>
+<button class="section" onclick="toggleSection(this)">Show Section View</button>
+
+</model-viewer> -->
+<figcaption>
+</figcaption>
+</figure>
+
+Here's a cutaway view, try zooming out to get your bearing with respect to the above diagram. You can see that in the center of these three pairs of coils there is:
+
+<figure>
+<model-viewer style="height:400px;" src="{{ page.assets }}/vector_magnet_section.glb" ar ar-modes="webxr scene-viewer quick-look" camera-controls poster="{{ page.assets }}/section_view.png" shadow-intensity="1" environment-image="{{ page.assets }}/aircraft_workshop_01_1k.hdr" camera-orbit="37.19deg 75.38deg 0.3104m" field-of-view="12deg">
+<button class="Hotspot" slot="hotspot-1" data-position="0.0002550490643940138m 0.12905932644259982m 0.0003319628199390896m" data-normal="-3.85185900533595e-30m -1.343588384327496e-7m 0.9999999999999911m" data-visibility-attribute="visible">
+        <div class="left HotspotAnnotation">AFM Tip</div>
+    </button><button class="Hotspot" slot="hotspot-3" data-position="0.015349223451080356m 0.11522588429565153m 0.0037307930577810513m" data-normal="0.9822871951592153m -1.7349759832900923e-7m 0.18738160589079159m" data-visibility-attribute="visible">
+        <div class="HotspotAnnotation">Microscope Objective</div>
+    </button><button class="Hotspot" slot="hotspot-4" data-position="0.004252449047777678m 0.12449395035064194m -0.0015034997269766357m" data-normal="3.85185900533595e-30m 0.9999999999999911m 1.343588384327496e-7m" data-visibility-attribute="visible">
+        <div class="HotspotAnnotation">PCB Excitation Coil</div>
+    </button></model-viewer>
+<figcaption>
+</figcaption>
+</figure>
+
+**AFM Tip**: The atomic force microscope tip in blue with a nano-diamond attached to the very tip. We want to figure out which was the axis the NV defect in this nano-diamond is pointing. To do that we need to expose it to different directions of magnetic field while also blasting it with light and radio waves.
+
+**PCB coil** For the radio wave blasting we have a single turn coil made on a PCB. I haven't cut the coil away so that you can see it's whole shape. We'll pump RF power into this tuned to the electronic transitions in the NV defect that we want to probe.
+
+**Microscope Objective** The microscope objective allows us to optically pump the transitions in the NV defect (much like a laser) in order to keep electrons in excited quantum states that they wouldn't normally sit in.
+
+By putting in varying currents through the three coils pairs we can create a very well controlled magnetic field in any direction and of varying strength. We can then run a sweep through all the possible field directions while blasting the NV center with light and RF in order to determine it axis with respect to the plastic housing of the tip. 
+
+This is how you would calibrate one of these magnetism sensing AFM tips after first sticking a diamond to the tip.
+
+Once we know the axis direction this AFM tip could then be transferred back to the AFM to measure magnetic fields at the nanoscale!
+
+TODO: Explain this in a bit more detail.
+
+<!-- <script type="module">
+  const modelViewer = document.querySelector("model-viewer#vector-magnet-whole-2");
+  modelViewer.in_section_view = false;
+
+  modelViewer.addEventListener('load', (event) => { 
+    
+    modelViewer.cameraOrbit = modelViewer.in_section_view ? "37.19deg 75.38deg 0.01m" : "auto auto auto";
+    modelViewer.cameraTarget = modelViewer.in_section_view ? "0.3142m 0.2804m -0.3250m" : "auto auto auto";
+    modelViewer.fieldOfView = modelViewer.in_section_view ? "5deg" : "auto";
+    modelViewer.autoRotate = !modelViewer.in_section_view;
+
+    if(modelViewer.in_section_view){
+        window.setTimeout(() => [...modelViewer.getElementsByClassName("Hotspot")].forEach(e => e.style.display = "unset"), 1000);
+    } else {
+        [...modelViewer.getElementsByClassName("Hotspot")].forEach(e => e.style.display = "none")
+    }
+
+    // const logPoint = (event) => {
+    //     const p = modelViewer.positionAndNormalFromPoint(event.clientX, event.clientY)
+    // console.log(p.position.toString(), p.normal.toString());
+    // };
+    
+    // modelViewer.addEventListener("click", logPoint);
+    
+});
+
+  window.toggleSection = (element) => {
+    modelViewer.in_section_view = !modelViewer.in_section_view;
+
+    console.log(modelViewer.cameraOrbit, modelViewer.cameraTarget, modelViewer.fieldOfView);
+    if(modelViewer.in_section_view) {
+        modelViewer.src = "{{ page.assets }}/test_section.glb";
+    } else {
+        modelViewer.src = "{{ page.assets }}/test_whole.glb";
+    }
+    
+  };
+
+</script> -->
\ No newline at end of file
diff --git a/_data/navigation.yml b/_data/navigation.yml
index e540749..b44fb10 100644
--- a/_data/navigation.yml
+++ b/_data/navigation.yml
@@ -1,5 +1,5 @@
 - name: Blog
-  link: /blog/
+  link: /
 - name: CV
   link: /cv/
 - name: Thesis
diff --git a/_drafts/auto_screenshot.md b/_drafts/auto_screenshot.md
new file mode 100644
index 0000000..5e441ae
--- /dev/null
+++ b/_drafts/auto_screenshot.md
@@ -0,0 +1 @@
+https://gist.github.com/leodutra/d880580f86620915b28a3eadccb81527
\ No newline at end of file
diff --git a/_drafts/rpi_selfhosting.md b/_drafts/rpi_selfhosting.md
new file mode 100644
index 0000000..4a88dec
--- /dev/null
+++ b/_drafts/rpi_selfhosting.md
@@ -0,0 +1 @@
+https://www.raspberryconnect.com/projects/65-raspberrypi-hotspot-accesspoints/183-raspberry-pi-automatic-hotspot-and-static-hotspot-installer
\ No newline at end of file
diff --git a/_drafts/running.md b/_drafts/running.md
index 8d311a8..64de823 100644
--- a/_drafts/running.md
+++ b/_drafts/running.md
@@ -1,14 +1,23 @@
 ---
-title: Building Micropython from source
+title: My First Half Marathon
 layout: post
-image:
-alt: 
+image: /assets/blog/running/time_vs_distance.svg
+social_image: /assets/blog/running/time_vs_distance.png
+alt: A scatter graph of run time vs run distance for all my runs on strava. It shows that I mainly run between 5 and 6 min per kilometer, regardless of distance 
 ---
 
+I just ran my first half marathon. To celebrate the occasion I'm going to have a look at my historical run data.
+
+<figure>
+<img src="/assets/blog/running/time_vs_distance_plus_hist.svg"/>
+<figcaption>
+</figcaption>
+</figure>
+
 - download all my runs from strava
 - scatter them on a (distance, time) plot
 - plot the (distance, time) curves predicted by the V02 max tables in the running book
-- interpolate the table to get a smooth function parametrised by V02max
+- interpolate the table to get a smooth function parametrized by V02max
 - fit that to my data
 
 - potentially will need to take only the top 20% of runs or something
@@ -16,4 +25,4 @@ alt:
 
 Extensions:
 - download heart rate data and make a histogram per hour of the day
-- could map radius to heart rate and angle to hour of day to make a nice figure
\ No newline at end of file
+- could map radius to heart rate and angle to hour of day to make a nice figure
diff --git a/_drafts/test.md b/_drafts/test.md
index ef44748..9729e53 100644
--- a/_drafts/test.md
+++ b/_drafts/test.md
@@ -8,13 +8,18 @@ alt:
 
 <!-- <div class="strava-embed-placeholder" data-embed-type="activity" data-embed-id="9552293688"></div><script src="https://strava-embeds.com/embed.js"></script> -->
 
-<!-- Import the component -->
-<script type="module" src="https://ajax.googleapis.com/ajax/libs/model-viewer/3.1.1/model-viewer.min.js"></script>
 
 <!-- Use it like any other HTML element -->
 <!-- <model-viewer alt="test" src="/assets/blog/toothbrush_shelf/toothbrush_shelf.glb" ar environment-image="" poster="/assets/blog/toothbrush_shelf/spin.gif" shadow-intensity="1" camera-controls touch-action="pan-y"></model-viewer> -->
 
 <!-- <model-viewer> HTML element -->
 <model-viewer src="/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb" ar ar-modes="webxr scene-viewer quick-look" camera-controls poster="/assets/blog/toothbrush_shelf/model/poster.webp" shadow-intensity="1" auto-rotate camera-orbit="-212.4deg 77.5deg 411.2m" field-of-view="30deg"> </model-viewer>
-<!-- Loads <model-viewer> for browsers: -->
-<script type="module" src="https://ajax.googleapis.com/ajax/libs/model-viewer/3.0.1/model-viewer.min.js"></script>
\ No newline at end of file
+
+
+<model-viewer src="/assets/blog/terrain/untitled.glb" ar ar-modes="webxr scene-viewer quick-look" camera-controls shadow-intensity="1" environment-image="/assets/blog/terrain/pillars_1k.hdr" exposure="1.34" camera-orbit="-198.3deg 49.94deg 143.8m" field-of-view="38.8deg">
+    <button class="Hotspot" slot="hotspot-1" data-position="6.6575782971657045m 1.2675063664559048m -11.69313039177284m" data-normal="0.03993612823978079m 0.9984038328257562m 0.0399361022141038m" data-visibility-attribute="visible">
+        <div class="HotspotAnnotation">My House</div>
+    </button><button class="Hotspot" slot="hotspot-2" data-position="-12.219647443249531m 0.2444805745162455m -0.03384905050821585m" data-normal="-0.3045547244913506m 0.9517337624555773m -0.03806921580684815m" data-visibility-attribute="visible">
+        <div class="HotspotAnnotation">RARA</div>
+    </button>
+</model-viewer>
diff --git a/_includes/default_head_tags.html b/_includes/default_head_tags.html
index e50a42e..bdaf1c9 100644
--- a/_includes/default_head_tags.html
+++ b/_includes/default_head_tags.html
@@ -40,10 +40,14 @@ See: https://developers.google.com/search/docs/advanced/mobile/google-discover?h
 {% endif %}
 {% endif %}
 
+<!-- indieauth.com authentication -->
+<link rel="authorization_endpoint" href="https://indieauth.com/auth">
+
 <link rel="stylesheet" href="/assets/css/styles.css">
-<script type="module" src="/assets/js/model-viewer.js"></script>
 <script src="/assets/js/index.js"></script>
 
+<script type="module" src="/assets/js/model-viewer.js"></script>
+
 
 <script>
   MathJax = {
diff --git a/_includes/header.html b/_includes/header.html
index 29eea6b..d5c008e 100644
--- a/_includes/header.html
+++ b/_includes/header.html
@@ -6,10 +6,9 @@
 </div>
 <p class="p-note">Physicist, Programmer, Maker and Baker
 <p class="professional-links">
-  <a class="u-url" rel="me" href="https://github.com/TomHodson"><img class="icon" src="/assets/icons/github.svg">GitHub</a>
-  <a class="u-url" rel="me" href="https://twitter.com/T_Hodson"><img class="icon" src="/assets/icons/twitter.svg"><s>Twitter</s></a>
+  <a href="https://github.com/TomHodson" rel="me authn" class="u-url"><img class="icon" src="/assets/icons/github.svg">GitHub</a>
+  <a href="https://tech.lgbt/@Tomhodson" rel="me" class="u-url"><img class="icon" src="/assets/icons/mastodon.svg">Mastodon</a>
   <a href="/feed.xml"><img class="icon" src="/assets/icons/rss.svg">RSS</a>
-  <a class="u-url" rel="me" href="https://tech.lgbt/@Tomhodson"><img class="icon" src="/assets/icons/mastodon.svg">Mastodon</a>
 </p>
 {% include sidebar.html%}
 
diff --git a/_includes/sidebar.html b/_includes/sidebar.html
index 4ba306b..5ac750c 100644
--- a/_includes/sidebar.html
+++ b/_includes/sidebar.html
@@ -1,4 +1,4 @@
-<nav aria-label="Site Map" class="site-map"></nav>
+<nav aria-label="Site Map" class="site-map">
     {% for item in site.data.navigation %}
       <a href="{{ item.link }}" {% if page.url == item.link %}class="current"{% endif %}>{{ item.name }}</a>
     {% endfor %}
diff --git a/_posts/2023-02-10-shelves.md b/_posts/2023-02-10-shelves.md
index 01c2075..d416a48 100644
--- a/_posts/2023-02-10-shelves.md
+++ b/_posts/2023-02-10-shelves.md
@@ -5,12 +5,21 @@ layout: post
 image: /assets/blog/shelves/thumb.svg
 social_image: /assets/social/shelves.png
 alt: A 3D render of some shelves fitting into a triangular space under a ladder.
+assets: /assets/blog/shelves
 ---
+
+<figure>
+<model-viewer src="{{page.assets}}/model/shelves.glb" ar ar-modes="webxr scene-viewer quick-look" camera-controls poster="{{page.assets}}/model/shelves.png" shadow-intensity="2" shadow-softness="1" camera-orbit="13.36deg 78.29deg 8.638m" field-of-view="37.48deg" interaction-prompt="none" auto-rotate environment-image="{{page.assets}}/model/env.jpg" alt="An interactive 3D model of the shelves, this is pretty unnecessary but I find it very cool that you can just view 3D models in browsers now like they're any other media."> </model-viewer>
+<figcaption>
+I'm a bit  addicted these little interactive 3D models. Give me time, I'll get over it.
+</figcaption>
+</figure>
+
 In our new flat we have this mezzanine bed with a yellow ladder leading up to it. Between the ladder and the wardrobe we had this kind of triangular space that we wanted to use for more storage. After doing a quick design on paper I started mocking something up. 
 
 
 <figure>
-<img src="/assets/blog/shelves/skeleton.jpg"/>
+<img style="height:500px;" src="{{page.assets}}/skeleton.jpg"/>
 <figcaption>
 The first try.
 </figcaption>
@@ -19,7 +28,7 @@ The first try.
 I got quite far with this version before realised I hade made a terrible error, somewhere on my scratch pad of calculations I had written something like "1700 - 36 = 1404"! After taking a few days to mourn the lost effort I decided to make a better plan to avoid making similar mistakes. I used the CAD model to generate a set of cutting plans that I could print out and take to the workshop.
 
 <figure>
-<img src="/assets/blog/shelves/Shelves v11.png"/>
+<img src="{{page.assets}}/Shelves v11.png"/>
 <figcaption>
 After messing up the measurments on the first iteration, I went back and myself a nice CAD model to take measurements from while cutting.
 </figcaption>
@@ -28,21 +37,21 @@ After messing up the measurments on the first iteration, I went back and myself
 In this new version I opted to make the sided panels out of solid sheets of 18mm pine plywood, it made the final object heavier but they were much easier to cut on the table saw. The extra weight was probably a good thing, in place it feels pleasantly heavy and sturdy. 
 
 <figure>
-<img src="/assets/blog/shelves/plans.svg"/>
+<img src="{{page.assets}}/plans.svg"/>
 <figcaption>
 From the CAD model, it's quite easy to make nice plans for each piece.
 </figcaption>
 </figure>
 
 <figure>
-<img src="/assets/blog/shelves/thumb.svg"/>
+<img src="{{page.assets}}/thumb.svg"/>
 <figcaption>
 I really like the line drawn aesthetic, reminds me a lot of Ikea construction plans.
 </figcaption>
 </figure>
 
 <figure>
-<img src="/assets/blog/shelves/real_2.jpg"/>
+<img src="{{page.assets}}/real_2.jpg"/>
 <figcaption>
 The final shelves in their new home!
 </figcaption>
diff --git a/_posts/2023-06-21-my_first_PCB.md b/_posts/2023-06-21-my_first_PCB.md
index 9ad01a7..7a82b0a 100644
--- a/_posts/2023-06-21-my_first_PCB.md
+++ b/_posts/2023-06-21-my_first_PCB.md
@@ -1,7 +1,7 @@
 ---
 title: My first PCB!
 excerpt: |
-    I've had a longstanding ambition to get a PCB manufactured but I've always put it off. Lately I had a need for a little adapter board to break out these 1.27mm spaced pins to 2.54mm pins that would fit into a breadboard. Feeling like it was a simple enough board I finally decided to fire up KiCad and give it a go.   <model-viewer alt="An interactive 3D render of a PCB with 2.54mm headers on one side to fit a breadboard and 1.27 inch headers on the other." src="/assets/blog/PCB/model/pcb.glb" ar camera-controls poster="/assets/blog/PCB/model/poster.webp" interaction-prompt="none" shadow-intensity="1" shadow-softness="1" exposure="0.5" camera-orbit="196.6deg 59.73deg 0.1m" field-of-view="30deg" auto-rotate> </model-viewer>
+    I've had a longstanding ambition to get a PCB manufactured but I've always put it off. Lately I had a need for a little adapter board to break out these 1.27mm spaced pins to 2.54mm pins that would fit into a breadboard. Feeling like it was a simple enough board I finally decided to fire up KiCad and give it a go.   <model-viewer alt="An interactive 3D render of a PCB with 2.54mm headers on one side to fit a breadboard and 1.27 inch headers on the other." src="/assets/blog/PCB/model/pcb.glb" ar camera-controls poster="/assets/social/pcb_1.png" interaction-prompt="none" shadow-intensity="1" shadow-softness="1" exposure="0.5" camera-orbit="196.6deg 59.73deg 0.1m" field-of-view="30deg" auto-rotate> </model-viewer>
 layout: post
 commentid: 110810437631337327
 hide_image: true # Only use this image for static previews
diff --git a/_posts/2023-07-31-toothbrush-holder.md b/_posts/2023-07-31-toothbrush-holder.md
index 5b7d879..dddfe90 100644
--- a/_posts/2023-07-31-toothbrush-holder.md
+++ b/_posts/2023-07-31-toothbrush-holder.md
@@ -1,7 +1,7 @@
 ---
 title: Toothbrush Shelf
 excerpt: |
-    It can be hard to find genuine everyday uses of 3D printing, but after a while you do find some. <model-viewer alt="An interactive 3D render of a small 3D printed shelf to hold a toothbrush near the bathroom shaver socket without the cables being messy" src="/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb" ar poster="/assets/blog/toothbrush_shelf/model/poster.webp" camera-controls shadow-intensity="1.38" shadow-softness="2" exposure="0.8" auto-rotate camera-orbit="-35deg 78.72deg 411.2m" field-of-view="30deg" interaction-prompt="none"> </model-viewer>
+    It can be hard to find genuine everyday uses of 3D printing, but after a while you do find some. <model-viewer alt="An interactive 3D render of a small 3D printed shelf to hold a toothbrush near the bathroom shaver socket without the cables being messy" src="/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb" ar poster="/assets/blog/toothbrush_shelf/model/toothbrush_shelf.webp" camera-controls shadow-intensity="1.38" shadow-softness="2" exposure="0.8" auto-rotate camera-orbit="-35deg 78.72deg 411.2m" field-of-view="30deg" interaction-prompt="none" ar-placement="wall"> </model-viewer>
 layout: post
 hide_image: true # Only use this image for static previews
 image: /assets/blog/toothbrush_shelf/spin.gif
@@ -11,12 +11,13 @@ alt: A render of a 3D printed shelf sitting above a shaver outlet, it spins slow
 It can be hard to find genuine everyday uses of 3D printing, but after a while you do find some! I made this little shelf to tidy up the wires to our devices in the bathroom. The wires are now hidden, coiled up, inside the shelf.
 
 <figure>
-<model-viewer alt="An interactive 3D render of a small 3D printed shelf to hold a toothbrush near the bathroom shaver socket without the cables being messy" src="/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb" ar poster="/assets/blog/toothbrush_shelf/model/poster.webp" camera-controls shadow-intensity="1.38" shadow-softness="2" exposure="0.8" auto-rotate camera-orbit="-35deg 78.72deg 411.2m" field-of-view="30deg" interaction-prompt="none"> </model-viewer>
+<model-viewer alt="An interactive 3D render of a small 3D printed shelf to hold a toothbrush near the bathroom shaver socket without the cables being messy" src="/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb" ar poster="/assets/blog/toothbrush_shelf/model/toothbrush_shelf.webp" camera-controls shadow-intensity="1.38" shadow-softness="2" exposure="0.8" auto-rotate camera-orbit="-35deg 78.72deg 411.2m" field-of-view="30deg" interaction-prompt="none" ar-placement="wall"> </model-viewer>
 <figcaption>
 A little 3D model that you can pan and zoom on. Or even use AR on your phone.
 </figcaption>
 </figure>
 
+
 <figure>
 <img style="height:500px;" src="/assets/blog/toothbrush_shelf/crop.jpeg"/>
 <figcaption>
diff --git a/_posts/2023-08-10-miniflux-and-rsshub.md b/_posts/2023-08-10-miniflux-and-rsshub.md
new file mode 100644
index 0000000..1cac640
--- /dev/null
+++ b/_posts/2023-08-10-miniflux-and-rsshub.md
@@ -0,0 +1,108 @@
+---
+title: "Selfhosting: Miniflux and RSSHub"
+layout: post
+excerpt: Some notes on selfhosting an RSS reader.
+---
+
+Like many nerdy, computery types, I like to subscribe to blogs and other content through RSS. RSS is crazy simple, you host a url on a website with a list of posts with titles/URLs/content encoded in XML (I know I know but it only have like 5 tags and is only nested one level deep.) An RSS reader just checks a big list of those URLs every now and then and presents you the latest thing to show up. 
+
+Incidentally this is also how podcasts work, at least for a while, Spotify is clearly trying to capture it.
+
+Anyway, I usually use [theoldreader](https://theoldreader.com/) to read RSS feeds but lately they've implemented a premium version that you have to pay $3 a month for if you have more than 100 feeds (I have 99...).
+
+Honestly, I use their service a lot so somehow $3 doesn't seem so bad, but it spurred me to look into selfhosting. 
+
+Selfhosting seems to be all the rage these days. Probably in response to feeling locked in to corporate mega structures, the aforementioned computery nerdy types have gone looking for ways to maintain their own anarchic web infrastructure. See i.e [the indieweb movement](https://indieweb.org/), [mastodon](https://joinmastodon.org/) etc etc etc
+
+So I want to try out some self hosting. Let's start with an RSS reader. [Miniflux](https://miniflux.app/) seems well regarded. So I popped over their, grabbed a `docker-compose.yml`, ran `docker compose up -d` and we seem to be off to the races.
+
+Ok, a nice thing about Miniflux when compared to theoldreader is the former seems to be better at telling you when there's something wrong with your feeds. It told me about a few blogs it couldn't reach, notably [Derek Lowe's excellent blog about chemical drug discovery](https://www.science.org/blogs/pipeline).
+
+That blog has an [rss feed](https://www.science.org/blogs/pipeline/feed), which loads perfectly find in my browser but doesn't seem to work when outside of that context, i.e in python:
+
+```python
+>>> import requests
+>>> requests.get("https://blogs.sciencemag.org/pipeline/feed")
+<Response [403]>
+```
+
+Playing around a bit more, adding in useragents, accepting cookies and following redirects, I eventually get back a page with a challenge that requires JS to run. This is the antithesis of how RSS should work!
+
+Ok so to fix this I came upon [RSSHub](https://docs.rsshub.app/en/) which is a kind of RSS proxy, it parses sites that don't have RSS feeds and generates them for you. I saw that this has pupeteer support so I'm hopping that I can use it to bypass the anti-crawler tactics science.org is using.
+
+Anyway, for how here is a docker-compose.yml for both miniflux and RSSHub. What took me a while to figure out is that docker containers live in their own special network. So to subscribe to a selfhosted RSSHub feed you need to put something like "http://rsshub:1200/" where rsshub is the key to the image in the yaml file below.
+
+EDIT: I got it to work using puppeteer! For now the code is in a [branch](https://github.com/TomHodson/RSSHub/tree/science_blogs) for which I'll do a proper PR soon.
+
+```
+version: '3'
+
+services:
+  miniflux:
+    image: miniflux/miniflux:latest
+    # build:
+    #   context: .
+    #   dockerfile: packaging/docker/alpine/Dockerfile 
+    container_name: miniflux
+    restart: always
+    healthcheck:
+      test: ["CMD", "/usr/bin/miniflux", "-healthcheck", "auto"]
+    ports:
+      - "8889:8080"
+    depends_on:
+      - rsshub
+      - db
+
+    environment:
+      - DATABASE_URL=postgres://miniflux:secret@db/miniflux?sslmode=disable
+      - RUN_MIGRATIONS=1
+      - CREATE_ADMIN=1
+      - ADMIN_USERNAME=admin
+      - ADMIN_PASSWORD=test123
+  db:
+    image: postgres:15
+    environment:
+      - POSTGRES_USER=miniflux
+      - POSTGRES_PASSWORD=secret
+    volumes:
+      - miniflux-db:/var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD", "pg_isready", "-U", "miniflux"]
+      interval: 10s
+      start_period: 30s
+
+  rsshub:
+      # two ways to enable puppeteer:
+      # * comment out marked lines, then use this image instead: diygod/rsshub:chromium-bundled
+      # * (consumes more disk space and memory) leave everything unchanged
+      image: diygod/rsshub
+      restart: always
+      ports:
+          - '1200:1200'
+      environment:
+          NODE_ENV: production
+          CACHE_TYPE: redis
+          REDIS_URL: 'redis://redis:6379/'
+          PUPPETEER_WS_ENDPOINT: 'ws://browserless:3000'  # marked
+      depends_on:
+          - redis
+          - browserless  # marked
+
+  browserless:  # marked
+      image: browserless/chrome  # marked
+      restart: always  # marked
+      ulimits:  # marked
+        core:  # marked
+          hard: 0  # marked
+          soft: 0  # marked
+
+  redis:
+      image: redis:alpine
+      restart: always
+      volumes:
+          - redis-data:/data
+
+volumes:
+  miniflux-db:
+  redis-data:
+```
\ No newline at end of file
diff --git a/_posts/2023-08-13-half-marathon.md b/_posts/2023-08-13-half-marathon.md
new file mode 100644
index 0000000..b43606f
--- /dev/null
+++ b/_posts/2023-08-13-half-marathon.md
@@ -0,0 +1,58 @@
+---
+title: My First Half Marathon!
+layout: post
+image: /assets/blog/running/time_vs_distance.svg
+social_image: /assets/blog/running/time_vs_distance.png
+alt: A scatter graph of run time vs run distance for all my runs on strava. It shows that I mainly run between 5 and 6 min per kilometer, regardless of distance 
+---
+
+I recently ran my first half marathon! I'm also signed up for a full marathon in October. 😬
+
+Anyway, to celebrate the occasion and because I have now have a reason to think about how fast I might run a particular distance. I had a look at my historical run data. There's a great website called [statshunter](https://www.statshunters.com/) that you can authorise to Strava and from which you can download a little csv of all your runs. The first logical thing I could think to do is to see how fast I tend to run different distances.
+
+<figure>
+<img src="/assets/blog/running/time_vs_distance_plus_hist.svg"/>
+<figcaption>
+So it's pretty much a straight line. This is telling me what I kinda already knew: I'm comfortable running between 5 and 7 mins/km and that's basically how fast I always run regardless of distance. 
+</figcaption>
+</figure>
+
+A friend lent me a huge running book which I'm going to dig through more but I suspect one of the conclusions will be a bit obvious: I could run those shorter distances a lot faster.
+
+That same friend also lent me a heart rate watch which I've been playing with. So the next thing I want to learn about is what type of heart rates you should target when you train for a particular event. 
+
+
+Code:
+
+```python
+from matplotlib import pyplot as plt
+import numpy as np
+from datetime import datetime
+import pandas as pd
+
+runs = pd.read_csv("runs.csv", parse_dates = ["Date"]) # Get this from statshunter.com
+
+f, (ax2, ax) = plt.subplots(nrows=2, figsize = (5,5), sharex = True,
+                           gridspec_kw = dict(height_ratios = (1,2)))
+
+ax.set(ylabel = "Moving Time (mins)", xlabel = "Distance (km)")
+
+x = runs["Distance (m)"].values/1e3
+y = runs["Moving time"].values/60
+
+dists = np.linspace(1, 25, 2)
+for i in [5,6,7]:
+    mins_per_km = i * dists
+    ax.plot(dists, mins_per_km, color = "black", linestyle = "dotted", label = f"{i} min/km")
+    ax.text(25.5, 25*i, f"{i} min/km", va = "center")
+
+ax.annotate("Half Marathon!", (x[0], y[0]-1), (20, 50), arrowprops = dict(arrowstyle = "->"))
+
+ax.scatter(x, y, s=20, alpha = 0.6*fade_out_by_date(runs["Date"]))
+for a in [ax, ax2]: a.spines[['right', 'top']].set_visible(False)
+
+ax2.hist(x, bins = 30, alpha = 0.5)
+ax2.set(yticks=[], ylabel = "Frequency Density")
+
+f.savefig("time_vs_distance_plus_hist.svg", transparent=True)
+```
\ No newline at end of file
diff --git a/_sass/header.scss b/_sass/header.scss
index f9f6156..a61ccbb 100644
--- a/_sass/header.scss
+++ b/_sass/header.scss
@@ -79,8 +79,11 @@ header p.professional-links a {
       padding-right: 20px;
     }
 
+    // To squish the text together a bit in the mobile view.
+    header p {
+        margin-block: 0.25em;
+    }
 
-  
     header .avatar {
         margin: auto;
     } 
@@ -90,7 +93,6 @@ header p.professional-links a {
         flex-direction: row;
         justify-content: center;
         flex-wrap: wrap;
-        justify-content: center;
     }
     nav a {
         margin-left: 1em;
@@ -98,6 +100,8 @@ header p.professional-links a {
 
     header p.professional-links {
         flex-direction: row;
+        flex-wrap: wrap;
+        justify-content: center;
     }
 
     header hr {
@@ -107,7 +111,7 @@ header p.professional-links a {
     header p.professional-links a {
         margin-left: 1em;
         margin-right: 1em;
-        flex-wrap: wrap;
+        flex-wrap: nowrap;
         justify-content: center;
     }
 }
\ No newline at end of file
diff --git a/_sass/main.scss b/_sass/main.scss
index d546a8a..ee526a1 100644
--- a/_sass/main.scss
+++ b/_sass/main.scss
@@ -8,6 +8,7 @@
 @import "thesis"; // the thesis content
 @import "blogroll"; // the summaries of the blogposts
 @import "comments";  //the mastodon comments
+@import "model_viewer"; //Styles for the 3D model viewer
 
 // The syntax highlighting css
 // generated with rougify style bw > code_style_bw.scss
diff --git a/_sass/model_viewer.scss b/_sass/model_viewer.scss
new file mode 100644
index 0000000..484f48b
--- /dev/null
+++ b/_sass/model_viewer.scss
@@ -0,0 +1,65 @@
+.Hotspot {
+    background: #fff;
+    border-radius: 32px;
+    border: 0;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.25);
+    box-sizing: border-box;
+    cursor: pointer;
+    height: 15px;
+    padding: 8px;
+    position: relative;
+    transition: opacity 0.3s;
+    width: 15px;
+  }
+  
+  .Hotspot:not([data-visible]) {
+    background: transparent;
+    border: 4px solid #fff;
+    box-shadow: none;
+    height: 32px;
+    pointer-events: none;
+    width: 32px;
+  }
+  
+  .Hotspot:focus {
+    border: 4px solid rgb(0, 128, 200);
+    height: 32px;
+    outline: none;
+    width: 32px;
+  }
+  
+  .Hotspot > * {
+    opacity: 1;
+    transform: translateY(-50%);
+  }
+  
+  .HotspotAnnotation{
+    background: #fff;
+    border-radius: 4px;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.25);
+    color: rgba(0, 0, 0, 0.8);
+    display: block;
+    font-family: Futura, Helvetica Neue, sans-serif;
+    font-size: 12px;
+    font-weight: 700;
+    left: calc(100% + 2em);
+    max-width: 128px;
+    overflow-wrap: break-word;
+    padding: 0.5em 1em;
+    position: absolute;
+    top: 50%;
+    width: max-content;
+  }
+
+  .left.HotspotAnnotation {
+    right: calc(100% + 2em);
+    left: unset;
+  }
+  
+  .Hotspot:not([data-visible]) > * {
+    opacity: 0;
+    pointer-events: none;
+    transform: translateY(calc(-50% + 4px));
+    transition: transform 0.3s, opacity 0.3s;
+  }
+  
\ No newline at end of file
diff --git a/assets/blog/running/running.ipynb b/assets/blog/running/running.ipynb
new file mode 100644
index 0000000..4f88c3c
--- /dev/null
+++ b/assets/blog/running/running.ipynb
@@ -0,0 +1,776 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "4b3f6dc4-69d3-4f8a-98b7-38e9745483c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting tabulate\n",
+      "  Downloading tabulate-0.9.0-py3-none-any.whl (35 kB)\n",
+      "Installing collected packages: tabulate\n",
+      "Successfully installed tabulate-0.9.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tabulate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "0eda6302-263d-4cb4-bb63-a3757839d95c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Tiles</th>\n",
+       "      <th>New Tiles</th>\n",
+       "      <th>Moving time</th>\n",
+       "      <th>Elapsed time</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Distance (m)</th>\n",
+       "      <th>Elevation (m)</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Commute</th>\n",
+       "      <th>Trainer</th>\n",
+       "      <th>Max heartrate</th>\n",
+       "      <th>Avg heartrate</th>\n",
+       "      <th>Start Lat</th>\n",
+       "      <th>Start Long</th>\n",
+       "      <th>Gear</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-07-30 18:34:42</td>\n",
+       "      <td>Heavy rain, grazing on blackberries</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7455</td>\n",
+       "      <td>8206</td>\n",
+       "      <td>10.1484</td>\n",
+       "      <td>21012.5</td>\n",
+       "      <td>47.1</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.5555</td>\n",
+       "      <td>-0.080539</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-07-27 19:06:27</td>\n",
+       "      <td>Evening Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1046</td>\n",
+       "      <td>1309</td>\n",
+       "      <td>11.9772</td>\n",
+       "      <td>3479.5</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.5556</td>\n",
+       "      <td>-0.080564</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-07-26 07:20:46</td>\n",
+       "      <td>Morning Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3368</td>\n",
+       "      <td>4697</td>\n",
+       "      <td>9.6840</td>\n",
+       "      <td>9058.4</td>\n",
+       "      <td>40.5</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.5556</td>\n",
+       "      <td>-0.080450</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-07-23 19:44:19</td>\n",
+       "      <td>Evening Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1304</td>\n",
+       "      <td>2055</td>\n",
+       "      <td>10.3716</td>\n",
+       "      <td>3757.1</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.5553</td>\n",
+       "      <td>-0.080291</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-07-22 20:09:37</td>\n",
+       "      <td>Night Run</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4073</td>\n",
+       "      <td>5099</td>\n",
+       "      <td>10.1772</td>\n",
+       "      <td>11514.3</td>\n",
+       "      <td>43.8</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.5555</td>\n",
+       "      <td>-0.080549</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>2017-04-23 19:01:35</td>\n",
+       "      <td>Evening Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2899</td>\n",
+       "      <td>2948</td>\n",
+       "      <td>10.6128</td>\n",
+       "      <td>8547.3</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.2080</td>\n",
+       "      <td>0.119659</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>2017-03-06 18:35:40</td>\n",
+       "      <td>Evening Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2950</td>\n",
+       "      <td>2972</td>\n",
+       "      <td>11.3328</td>\n",
+       "      <td>9287.6</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.2080</td>\n",
+       "      <td>0.119585</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>2017-02-14 19:15:36</td>\n",
+       "      <td>Evening Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3166</td>\n",
+       "      <td>3286</td>\n",
+       "      <td>9.9144</td>\n",
+       "      <td>8717.6</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.2079</td>\n",
+       "      <td>0.119709</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>2017-01-30 19:10:27</td>\n",
+       "      <td>Evening Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3059</td>\n",
+       "      <td>3172</td>\n",
+       "      <td>10.1196</td>\n",
+       "      <td>8598.0</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.2081</td>\n",
+       "      <td>0.119615</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>2017-01-16 18:19:07</td>\n",
+       "      <td>Evening Run</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3216</td>\n",
+       "      <td>3478</td>\n",
+       "      <td>9.6588</td>\n",
+       "      <td>8627.4</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>Run</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.2078</td>\n",
+       "      <td>0.119573</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>196 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Date                                  Name  Tiles   \n",
+       "0   2023-07-30 18:34:42  Heavy rain, grazing on blackberries       7  \\\n",
+       "1   2023-07-27 19:06:27                           Evening Run      4   \n",
+       "2   2023-07-26 07:20:46                           Morning Run      4   \n",
+       "3   2023-07-23 19:44:19                           Evening Run      4   \n",
+       "4   2023-07-22 20:09:37                             Night Run      5   \n",
+       "..                  ...                                   ...    ...   \n",
+       "191 2017-04-23 19:01:35                           Evening Run      4   \n",
+       "192 2017-03-06 18:35:40                           Evening Run      4   \n",
+       "193 2017-02-14 19:15:36                           Evening Run      4   \n",
+       "194 2017-01-30 19:10:27                           Evening Run      4   \n",
+       "195 2017-01-16 18:19:07                           Evening Run      4   \n",
+       "\n",
+       "     New Tiles  Moving time  Elapsed time  Average  Distance (m)   \n",
+       "0            4         7455          8206  10.1484       21012.5  \\\n",
+       "1            0         1046          1309  11.9772        3479.5   \n",
+       "2            0         3368          4697   9.6840        9058.4   \n",
+       "3            0         1304          2055  10.3716        3757.1   \n",
+       "4            1         4073          5099  10.1772       11514.3   \n",
+       "..         ...          ...           ...      ...           ...   \n",
+       "191          0         2899          2948  10.6128        8547.3   \n",
+       "192          3         2950          2972  11.3328        9287.6   \n",
+       "193          0         3166          3286   9.9144        8717.6   \n",
+       "194          0         3059          3172  10.1196        8598.0   \n",
+       "195          4         3216          3478   9.6588        8627.4   \n",
+       "\n",
+       "     Elevation (m) Type  Commute  Trainer  Max heartrate  Avg heartrate   \n",
+       "0             47.1  Run        0        0              0              0  \\\n",
+       "1             15.3  Run        0        0              0              0   \n",
+       "2             40.5  Run        0        0              0              0   \n",
+       "3             14.9  Run        0        0              0              0   \n",
+       "4             43.8  Run        0        0              0              0   \n",
+       "..             ...  ...      ...      ...            ...            ...   \n",
+       "191           17.8  Run        0        0              0              0   \n",
+       "192           12.0  Run        0        0              0              0   \n",
+       "193           17.7  Run        0        0              0              0   \n",
+       "194           17.7  Run        0        0              0              0   \n",
+       "195           17.7  Run        0        0              0              0   \n",
+       "\n",
+       "     Start Lat  Start Long  Gear  \n",
+       "0      51.5555   -0.080539   NaN  \n",
+       "1      51.5556   -0.080564   NaN  \n",
+       "2      51.5556   -0.080450   NaN  \n",
+       "3      51.5553   -0.080291   NaN  \n",
+       "4      51.5555   -0.080549   NaN  \n",
+       "..         ...         ...   ...  \n",
+       "191    52.2080    0.119659   NaN  \n",
+       "192    52.2080    0.119585   NaN  \n",
+       "193    52.2079    0.119709   NaN  \n",
+       "194    52.2081    0.119615   NaN  \n",
+       "195    52.2078    0.119573   NaN  \n",
+       "\n",
+       "[196 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "runs = pd.read_csv(\"runs.csv\", parse_dates = [\"Date\"])\n",
+    "runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "a64a0e41-8227-4145-a539-b7c448d422e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "def fade_out_by_date(dates, low = None, high = None):\n",
+    "    low, high = low or min(dates), high or max(dates)\n",
+    "    range = (high - low)\n",
+    "    return (dates - low) / range\n",
+    "\n",
+    "f, ax = plt.subplots(figsize = (7,3))\n",
+    "ax.set(ylabel = \"Moving Time (mins)\", xlabel = \"Distance (km)\")\n",
+    "\n",
+    "x = runs[\"Distance (m)\"].values/1e3\n",
+    "y = runs[\"Moving time\"].values/60\n",
+    "\n",
+    "dists = np.linspace(1, 25, 2)\n",
+    "for i in [5,6,7]:\n",
+    "    mins_per_km = i * dists\n",
+    "    ax.plot(dists, mins_per_km, color = \"black\", linestyle = \"dotted\", label = f\"{i} min/km\")\n",
+    "    ax.text(25.5, 25*i, f\"{i} min/km\", va = \"center\")\n",
+    "\n",
+    "ax.annotate(\"Half Marathon!\", (x[0], y[0]-1), (20, 50), arrowprops = dict(arrowstyle = \"->\"))\n",
+    "\n",
+    "ax.scatter(x, y, s=20, alpha = 0.6*fade_out_by_date(runs[\"Date\"]))\n",
+    "ax.spines[['right', 'top']].set_visible(False)\n",
+    "f.savefig(\"time_vs_distance.svg\", transparent=True)\n",
+    "f.savefig(\"time_vs_distance.png\", transparent=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "51846c1c-a67e-4da4-997c-5699e9e85ecf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax2, ax) = plt.subplots(nrows=2, figsize = (7,6), sharex = True,\n",
+    "                           gridspec_kw = dict(height_ratios = (1,2)))\n",
+    "\n",
+    "ax.set(ylabel = \"Moving Time (mins)\", xlabel = \"Distance (km)\")\n",
+    "\n",
+    "x = runs[\"Distance (m)\"].values/1e3\n",
+    "y = runs[\"Moving time\"].values/60\n",
+    "\n",
+    "dists = np.linspace(1, 25, 2)\n",
+    "for i in [5,6,7]:\n",
+    "    mins_per_km = i * dists\n",
+    "    ax.plot(dists, mins_per_km, color = \"black\", linestyle = \"dotted\", label = f\"{i} min/km\")\n",
+    "    ax.text(25.5, 25*i, f\"{i} min/km\", va = \"center\")\n",
+    "\n",
+    "ax.annotate(\"Half Marathon!\", (x[0], y[0]-1), (20, 50), arrowprops = dict(arrowstyle = \"->\"))\n",
+    "\n",
+    "ax.scatter(x, y, s=20, alpha = 0.6*fade_out_by_date(runs[\"Date\"]))\n",
+    "for a in [ax, ax2]: a.spines[['right', 'top']].set_visible(False)\n",
+    "\n",
+    "ax2.hist(x, bins = 30, alpha = 0.5)\n",
+    "ax2.set(yticks=[], ylabel = \"Frequency Density\")\n",
+    "\n",
+    "f.savefig(\"time_vs_distance_plus_hist.svg\", transparent=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "32000476-0a5a-4059-bbcf-234d3e0a8deb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x15d9c7050>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots()\n",
+    "ax.set(ylabel = \"Elapsed Time (mins)\", xlabel = \"Distance (km)\", ylim = (0, 175))\n",
+    "\n",
+    "dists = np.linspace(1, 25, 2)\n",
+    "for i in [5,6,7]:\n",
+    "    mins_per_km = i * dists\n",
+    "    ax.plot(dists, mins_per_km, color = \"black\", linestyle = \"dotted\", label = f\"{i} min/km\")\n",
+    "\n",
+    "ax.scatter(y = runs[\"Elapsed time\"]/60, x = runs[\"Distance (m)\"]/1e3, s=20, alpha = 0.6*fade_out_by_date(runs[\"Date\"]))\n",
+    "\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "ab8a145c-f38d-4645-ae8c-e8228a7e9a8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<script src=\"https://d3js.org/d3.v4.js\"></script>\n",
+       "<script>\n",
+       "\n",
+       "// set the dimensions and margins of the graph\n",
+       "var margin = {top: 10, right: 30, bottom: 30, left: 60},\n",
+       "    width = 460 - margin.left - margin.right,\n",
+       "    height = 400 - margin.top - margin.bottom;\n",
+       "\n",
+       "// append the svg object to the body of the page\n",
+       "var svg = d3.select(\"#my_dataviz\")\n",
+       "  .append(\"svg\")\n",
+       "    .attr(\"width\", width + margin.left + margin.right)\n",
+       "    .attr(\"height\", height + margin.top + margin.bottom)\n",
+       "  .append(\"g\")\n",
+       "    .attr(\"transform\",\n",
+       "          \"translate(\" + margin.left + \",\" + margin.top + \")\");\n",
+       "\n",
+       "//Read the data\n",
+       "d3.csv(\"http://localhost:8888/files/tomhodson.github.com/assets/blog/running/runs.csv?_xsrf=2%7C2490f004%7Cdbdffe7a8b66a073c9c5821633171301%7C1688987029\", function(data) {\n",
+       "\n",
+       "  // Add X axis\n",
+       "  var x = d3.scaleLinear()\n",
+       "    .domain([0, 25])\n",
+       "    .range([ 0, width ]);\n",
+       "  svg.append(\"g\")\n",
+       "    .attr(\"transform\", \"translate(0,\" + height + \")\")\n",
+       "    .call(d3.axisBottom(x));\n",
+       "\n",
+       "  // Add Y axis\n",
+       "  var y = d3.scaleLinear()\n",
+       "    .domain([0, 180])\n",
+       "    .range([ height, 0]);\n",
+       "  svg.append(\"g\")\n",
+       "    .call(d3.axisLeft(y))\n",
+       "    .attr(\"class\", \"YAxis\");\n",
+       "\n",
+       "  // Add dots\n",
+       "  svg.append('g')\n",
+       "    .selectAll(\"dot\")\n",
+       "    .data(data)\n",
+       "    .enter()\n",
+       "    .append(\"circle\")\n",
+       "      .attr(\"cx\", function (d) { return x(d[\"Distance (m)\"] / 1000); } )\n",
+       "      .attr(\"cy\", function (d) { return y(d[\"Moving time\"] / 60); } )\n",
+       "      .attr(\"r\", 3)\n",
+       "      .style(\"fill\", \"#69b3a2\")\n",
+       "\n",
+       "\n",
+       "  triggerTransition = () => {\n",
+       "\n",
+       "    y.domain([0, 300])\n",
+       "  \n",
+       "    svg.selectAll(\"circle\")\n",
+       "    .data(data)\n",
+       "    .transition()\n",
+       "    .delay(100)\n",
+       "    .duration(2000)\n",
+       "    .attr(\"transform\", function(d) { return \"translate(0,\" + y(-(d[\"Elapsed time\"]-d[\"Moving time\"]) / 60) + \")\"; })\n",
+       "    \n",
+       "    // .attr(\"cy\", function (d) { return y(d[\"Elapsed time\"] / 60); } )\n",
+       "\n",
+       "    svg.select(\".YAxis\")\n",
+       "    .transition()\n",
+       "    .duration(3000)\n",
+       "    .call(d3.axisLeft(y));\n",
+       "    }\n",
+       "\n",
+       "})\n",
+       "\n",
+       "\n",
+       "</script>\n",
+       "\n",
+       "<button onclick=\"triggerTransition()\">Trigger transition</button>\n",
+       "<!-- Create a div where the graph will take place -->\n",
+       "<div id=\"my_dataviz\"></div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "script = \"\"\"\n",
+    "<script src=\"https://d3js.org/d3.v4.js\"></script>\n",
+    "<script>\n",
+    "\n",
+    "// set the dimensions and margins of the graph\n",
+    "var margin = {top: 10, right: 30, bottom: 30, left: 60},\n",
+    "    width = 460 - margin.left - margin.right,\n",
+    "    height = 400 - margin.top - margin.bottom;\n",
+    "\n",
+    "// append the svg object to the body of the page\n",
+    "var svg = d3.select(\"#my_dataviz\")\n",
+    "  .append(\"svg\")\n",
+    "    .attr(\"width\", width + margin.left + margin.right)\n",
+    "    .attr(\"height\", height + margin.top + margin.bottom)\n",
+    "  .append(\"g\")\n",
+    "    .attr(\"transform\",\n",
+    "          \"translate(\" + margin.left + \",\" + margin.top + \")\");\n",
+    "\n",
+    "//Read the data\n",
+    "d3.csv(\"http://localhost:8888/files/tomhodson.github.com/assets/blog/running/runs.csv?_xsrf=2%7C2490f004%7Cdbdffe7a8b66a073c9c5821633171301%7C1688987029\", function(data) {\n",
+    "\n",
+    "  // Add X axis\n",
+    "  var x = d3.scaleLinear()\n",
+    "    .domain([0, 25])\n",
+    "    .range([ 0, width ]);\n",
+    "  svg.append(\"g\")\n",
+    "    .attr(\"transform\", \"translate(0,\" + height + \")\")\n",
+    "    .call(d3.axisBottom(x));\n",
+    "\n",
+    "  // Add Y axis\n",
+    "  var y = d3.scaleLinear()\n",
+    "    .domain([0, 180])\n",
+    "    .range([ height, 0]);\n",
+    "  svg.append(\"g\")\n",
+    "    .call(d3.axisLeft(y))\n",
+    "    .attr(\"class\", \"YAxis\");\n",
+    "\n",
+    "  // Add dots\n",
+    "  svg.append('g')\n",
+    "    .selectAll(\"dot\")\n",
+    "    .data(data)\n",
+    "    .enter()\n",
+    "    .append(\"circle\")\n",
+    "      .attr(\"cx\", function (d) { return x(d[\"Distance (m)\"] / 1000); } )\n",
+    "      .attr(\"cy\", function (d) { return y(d[\"Moving time\"] / 60); } )\n",
+    "      .attr(\"r\", 3)\n",
+    "      .style(\"fill\", \"#69b3a2\")\n",
+    "\n",
+    "\n",
+    "  triggerTransition = () => {\n",
+    "\n",
+    "    y.domain([0, 300])\n",
+    "  \n",
+    "    svg.selectAll(\"circle\")\n",
+    "    .data(data)\n",
+    "    .transition()\n",
+    "    .delay(100)\n",
+    "    .duration(2000)\n",
+    "    .attr(\"transform\", function(d) { return \"translate(0,\" + y(-(d[\"Elapsed time\"]-d[\"Moving time\"]) / 60) + \")\"; })\n",
+    "    \n",
+    "    // .attr(\"cy\", function (d) { return y(d[\"Elapsed time\"] / 60); } )\n",
+    "\n",
+    "    svg.select(\".YAxis\")\n",
+    "    .transition()\n",
+    "    .duration(3000)\n",
+    "    .call(d3.axisLeft(y));\n",
+    "    }\n",
+    "\n",
+    "})\n",
+    "\n",
+    "\n",
+    "</script>\n",
+    "\n",
+    "<button onclick=\"triggerTransition()\">Trigger transition</button>\n",
+    "<!-- Create a div where the graph will take place -->\n",
+    "<div id=\"my_dataviz\"></div>\n",
+    "\"\"\"\n",
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "HTML(script)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "bd19b6de-bdde-40bd-a344-0cd3cbd76f80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.0 [8.5, 9.183333333333334, 17.933333333333334, 19.316666666666666, 30.666666666666668, 63.766666666666666, 98.23333333333333, 141.06666666666666, 289.28333333333336]\n",
+      "31.0 [8.25, 8.916666666666666, 17.45, 18.8, 29.85, 62.06666666666667, 95.6, 137.35, 281.95]\n",
+      "32.0 [8.033333333333333, 8.683333333333334, 16.983333333333334, 18.3, 29.083333333333332, 60.43333333333333, 93.11666666666666, 133.81666666666666, 274.98333333333335]\n",
+      "33.0 [7.816666666666666, 8.45, 16.55, 17.833333333333332, 28.35, 58.9, 90.75, 130.45, 268.3666666666667]\n",
+      "34.0 [7.616666666666666, 8.233333333333333, 16.15, 17.4, 27.65, 57.43333333333333, 88.5, 127.26666666666667, 262.05]\n",
+      "35.0 [7.416666666666667, 8.016666666666667, 15.75, 16.966666666666665, 27.0, 56.05, 86.36666666666666, 124.21666666666667, 256.05]\n",
+      "36.0 [7.233333333333333, 7.816666666666666, 15.383333333333333, 16.566666666666666, 26.366666666666667, 54.733333333333334, 84.33333333333333, 121.31666666666666, 250.31666666666666]\n",
+      "37.0 [7.066666666666666, 7.633333333333334, 15.016666666666667, 16.183333333333334, 25.766666666666666, 53.483333333333334, 82.4, 118.56666666666666, 244.83333333333334]\n",
+      "38.0 [6.9, 7.45, 14.683333333333334, 15.816666666666666, 25.2, 52.28333333333333, 80.55, 115.91666666666667, 239.58333333333334]\n"
+     ]
+    }
+   ],
+   "source": [
+    "vdot_table = pd.read_csv(\"vdot.csv\")\n",
+    "table_dists = [1.5, 1.5609, 3, 2*1.609, 5, 10, 15, 42.195/2, 42.195]\n",
+    "\n",
+    "for col in vdot_table.columns[1:]:\n",
+    "    seconds = vdot_table[col].str.split(\":\").apply(lambda s: sum(int(x)*60**i for i,x in enumerate(s[::-1])))\n",
+    "    vdot_table[col] = seconds / 60 #pd.to_timedelta(seconds, unit='s')\n",
+    "\n",
+    "for i, row in vdot_table.iterrows():\n",
+    "    vdot, *times = row\n",
+    "    print(vdot, times)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "887f7db8-d9b7-47ad-9bb2-538bf1c475e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(figsize = (7,3))\n",
+    "ax.set(ylabel = \"Moving Time (mins)\", xlabel = \"Distance (km)\", xlim = (0,25))\n",
+    "\n",
+    "x = runs[\"Distance (m)\"].values/1e3\n",
+    "y = runs[\"Moving time\"].values/60\n",
+    "\n",
+    "\n",
+    "for i, row in list(vdot_table.iterrows())[:-1:3]:\n",
+    "    vdot, *times = row[:-1]\n",
+    "    ax.plot(table_dists[:-1], times, color = \"black\", linestyle = \"dotted\", label = f\"{i} min/km\")\n",
+    "    ax.text(22.5, times[-1], f\"vdot= {vdot}\", va = \"center\")\n",
+    "\n",
+    "ax.annotate(\"Half Marathon!\", (x[0], y[0]-1), (20, 50), arrowprops = dict(arrowstyle = \"->\"))\n",
+    "\n",
+    "ax.scatter(x, y, s=20, alpha = 0.6*fade_out_by_date(runs[\"Date\"]))\n",
+    "ax.spines[['right', 'top']].set_visible(False)\n",
+    "f.savefig(\"time_vs_distance.svg\", transparent=True)\n",
+    "f.savefig(\"time_vs_distance.png\", transparent=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e29ea263-ecc8-4109-af51-2498ea65573d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:micromamba-iot_ingester]",
+   "language": "python",
+   "name": "conda-env-micromamba-iot_ingester-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/assets/blog/running/runs.csv b/assets/blog/running/runs.csv
new file mode 100644
index 0000000..e3b35f5
--- /dev/null
+++ b/assets/blog/running/runs.csv
@@ -0,0 +1,197 @@
+Date,Name,Tiles,New Tiles,Moving time,Elapsed time,Average,Distance (m),Elevation (m),Type,Commute,Trainer,Max heartrate,Avg heartrate,Start Lat,Start Long,Gear
+2023-07-30 18:34:42,"Heavy rain, grazing on blackberries ",7,4,7455,8206,10.1484,21012.5,47.1,Run,0,0,0,0,51.5555,-0.0805394,
+2023-07-27 19:06:27,Evening Run,4,0,1046,1309,11.9772,3479.5,15.3,Run,0,0,0,0,51.5556,-0.080564,
+2023-07-26 07:20:46,Morning Run,4,0,3368,4697,9.684,9058.4,40.5,Run,0,0,0,0,51.5556,-0.0804497,
+2023-07-23 19:44:19,Evening Run,4,0,1304,2055,10.3716,3757.1,14.9,Run,0,0,0,0,51.5553,-0.0802908,
+2023-07-22 20:09:37,Night Run,5,1,4073,5099,10.1772,11514.3,43.8,Run,0,0,0,0,51.5555,-0.0805495,
+2023-07-13 17:20:25,Afternoon Run,1,0,1979,2197,11.3832,6258.2,32.2,Run,0,0,0,0,51.5653,-0.0524104,
+2023-07-10 17:11:06,Afternoon Run,4,0,1877,3011,11.4912,5990.7,23.1,Run,0,0,0,0,51.5554,-0.0805919,
+2023-07-08 19:22:28,Evening Run,4,0,3180,3801,10.3572,9150.4,43.4,Run,0,0,0,0,51.5554,-0.0806102,
+2023-07-05 07:56:30,Morning Run,5,4,2563,2994,10.548,7508.7,37.1,Run,0,0,0,0,51.4197,-0.947986,
+2023-07-04 08:12:25,Morning Run,2,2,653,1640,10.5192,1908.2,29.4,Run,0,0,0,0,51.4198,-0.949513,
+2023-06-30 07:03:15,Morning Run,3,0,1842,3900,10.386,5314.3,10.3,Run,0,0,0,0,51.3425,12.3776,
+2023-06-28 07:13:02,Morning Run,3,0,1782,4064,10.7424,5317.2,6.7,Run,0,0,0,0,51.3427,12.3781,
+2023-06-27 06:36:40,Morning Run,3,3,1743,2376,11.3148,5478.6,4.8,Run,0,0,0,0,51.3422,12.3781,
+2023-06-12 19:26:39,Evening Run,1,0,1240,1630,10.2456,3529,12.7,Run,0,0,0,0,51.5647,-0.046122,
+2023-06-06 18:27:57,Evening Run,4,0,1944,2233,11.592,6258.7,23,Run,0,0,0,0,51.5554,-0.0805764,
+2023-05-31 18:40:29,Night Run,2,0,1469,12276,8.4384,3443.1,8.9,Run,0,0,0,0,51.5554,-0.0805397,
+2023-05-13 17:11:27,Night Run,3,0,2117,19978,10.4796,6162.2,22.9,Run,0,0,0,0,51.5553,-0.0805932,
+2023-05-11 17:26:01,Evening Run,3,0,1508,2683,10.2888,4310.5,14.9,Run,0,0,0,0,51.5555,-0.0804862,
+2023-05-07 20:04:15,Night Run,3,0,1417,4465,9.4212,3708.5,12.8,Run,0,0,0,0,51.5556,-0.0805553,
+2023-04-29 10:18:55,Lunch Run,4,0,1453,4535,8.6256,3482.1,14.1,Run,0,0,0,0,51.5556,-0.0805465,
+2023-04-24 19:22:59,Evening Run,4,0,2232,2889,10.1088,6266.9,22.9,Run,0,0,0,0,51.5557,-0.0805353,
+2023-04-17 19:09:39,Evening Run,4,0,1975,3051,11.0448,6058.6,23,Run,0,0,0,0,51.5555,-0.0806151,
+2023-04-14 18:49:08,Evening Run,4,0,2006,2246,11.0268,6144.3,22.9,Run,0,0,0,0,51.5555,-0.080489,
+2023-04-05 17:38:00,Evening Run,3,0,2815,3858,9.4824,7415,32.3,Run,0,0,0,0,51.5556,-0.0804512,
+2023-03-30 18:20:11,Evening Run,4,0,1773,2115,10.17,5009.2,22.3,Run,0,0,0,0,51.5554,-0.0805344,
+2022-12-22 09:49:00,Morning Run,1,0,1215,2044,10.7748,3636.3,4.8,Run,0,0,0,0,51.5584,-0.0622994,
+2022-11-30 17:23:40,Afternoon Run,1,0,1437,2440,9.8496,3931.7,9.1,Run,0,0,0,0,51.5581,-0.0623459,
+2022-11-14 17:41:33,Evening Run,2,0,1877,2549,10.6128,5532.8,15.6,Run,0,0,0,0,51.5583,-0.0613758,
+2022-10-11 18:07:25,Evening Run,2,1,1397,2949,10.8648,4216.5,14.3,Run,0,0,0,0,51.5584,-0.0626121,
+2022-09-29 18:59:54,Evening Run,2,0,2144,2616,10.0548,5988.1,23.2,Run,0,0,0,0,51.5583,-0.0622645,
+2022-09-28 18:11:48,Evening Run,1,0,1226,1560,10.854,3697,9.3,Run,0,0,0,0,51.5583,-0.0619869,
+2022-09-20 19:29:34,Evening Run,4,1,1824,2133,10.8792,5512.3,24.2,Run,0,0,0,0,51.5584,-0.062278,
+2022-09-15 18:43:11,Evening Run,2,1,2189,2613,10.0368,6102.8,16.3,Run,0,0,0,0,51.5584,-0.0620325,
+2022-09-13 17:57:06,Evening Run,2,2,1402,1800,10.2204,3979.7,14.1,Run,0,0,0,0,51.5582,-0.0622277,
+2022-08-27 20:17:22,Evening Run,2,0,820,1026,10.53,2398.7,48.3,Run,0,0,0,0,43.7697,4.12133,
+2022-08-22 19:20:29,Hooooooot,2,0,946,1088,9.5004,2496.2,47.4,Run,0,0,0,0,43.7696,4.12102,
+2022-08-03 17:02:43,Afternoon Run,3,0,2090,2494,9.5364,5537.4,62.7,Run,0,0,0,0,51.4709,-0.0849791,
+2022-08-02 17:49:17,Evening Run,3,0,1635,1805,9.5508,4338.3,47.5,Run,0,0,0,0,51.4705,-0.0852663,
+2022-07-22 18:33:59,Evening Run,3,1,1804,2400,10.8,5412.7,53,Run,0,0,0,0,51.4706,-0.0848732,
+2022-07-11 18:02:45,Morning Run,3,0,2496,60579,9.2844,6436.4,13,Run,0,0,0,0,52.7238,-1.79276,
+2022-06-27 18:48:14,Evening Run,2,0,1053,3829,11.0448,3230.5,7.2,Run,0,0,0,0,52.7237,-1.79294,
+2022-06-16 17:42:05,Afternoon Run,1,0,1016,1140,11.3076,3191.4,11,Run,0,0,0,0,48.1249,11.5498,
+2022-06-13 19:03:34,Evening Run,5,0,1516,1814,11.2968,4757.1,47.7,Run,0,0,0,0,48.1248,11.5498,
+2022-06-06 19:03:21,Evening Run,5,0,3325,4683,9.7164,8975.4,107.2,Run,0,0,0,0,48.1249,11.5498,
+2022-05-30 19:17:53,Evening Run,5,0,2103,2611,10.8252,6324.1,56.7,Run,0,0,0,0,48.1232,11.5259,
+2022-05-23 19:02:10,Evening Run,5,2,3248,5919,10.278,9271.4,68.8,Run,0,0,0,0,48.1187,11.5818,
+2022-05-18 20:48:40,Night Run,6,0,2301,8775,12.0816,7722.4,39.5,Run,0,0,0,0,48.1186,11.5817,
+2022-05-16 20:05:56,Evening Run,4,0,2432,2973,10.5012,7093.8,48,Run,0,0,0,0,48.1185,11.5817,
+2022-05-11 07:21:10,Morning Run,3,0,1587,2008,10.2636,4524.3,41.1,Run,0,0,0,0,48.1185,11.5817,
+2022-05-02 21:34:33,Night Run,3,0,1631,1689,11.0844,5021.8,8.3,Run,0,0,0,0,48.1524,11.5796,
+2022-04-21 08:53:11,Morning Run,4,4,2267,3068,9.36,5895,164.8,Run,0,0,0,0,23.3653,-110.189,
+2022-03-22 07:48:23,Morning Run,3,0,1439,4620,10.4616,4182.4,14.2,Run,0,0,0,0,48.1206,11.5581,
+2022-03-20 09:25:35,Morning Run,3,3,1435,1737,10.3032,4106.4,13,Run,0,0,0,0,48.1208,11.5587,
+2022-03-01 17:16:31,Afternoon Run,3,0,1959,2991,10.5624,5748.3,36.1,Run,0,0,0,0,51.5485,-0.110401,
+2022-02-24 18:34:14,Evening Run,5,3,3003,48607,8.6796,7239.9,48.8,Run,0,0,0,0,51.5485,-0.110477,
+2022-02-15 19:16:08,Evening Run,3,2,2636,3590,10.6344,7787.4,46.6,Run,0,0,0,0,51.5486,-0.110636,
+2022-02-11 18:41:39,Evening Run,4,0,1291,1707,10.098,3620.7,10,Run,0,0,0,0,52.7375,-1.74716,
+2022-02-09 18:18:54,Evening Run,5,0,2028,2325,10.6272,5985.7,6.6,Run,0,0,0,0,52.7236,-1.79297,
+2022-01-21 17:28:20,Afternoon Run,2,0,1833,3549,10.3968,5294.3,53.5,Run,0,0,0,0,50.8604,4.37734,
+2022-01-16 16:51:03,Afternoon Run,2,2,2073,2488,8.55,4922.7,42.4,Run,0,0,0,0,50.8602,4.37721,
+2022-01-03 18:58:52,Evening Run,4,0,3056,3308,10.566,8968.3,57.1,Run,0,0,0,0,43.7694,4.12095,
+2021-12-29 12:37:57,My first half marathon! (Phone died at 16k),8,4,5391,9744,11.0412,16535.8,71.4,Run,0,0,0,0,43.7703,4.1216,
+2021-12-22 18:43:56,Evening Run,4,2,1673,2030,9.4608,4396.7,17.6,Run,0,0,0,0,43.7758,4.16233,
+2021-12-10 18:16:18,Evening Run,4,0,2643,2814,10.8504,7965.4,36.2,Run,0,0,0,0,43.7694,4.12112,
+2021-12-04 19:05:27,Evening Run,4,0,2954,3577,10.242,8404.4,8.6,Run,0,0,0,0,51.4756,-0.108807,
+2021-11-29 18:06:28,Evening Run,4,0,3050,3517,9.8856,8375.7,15.4,Run,0,0,0,0,51.4754,-0.10877,
+2021-10-31 19:32:30,Evening Run,4,1,1936,2197,10.476,5633.7,2.6,Run,0,0,0,0,51.4756,-0.108841,
+2021-10-25 19:02:51,Evening Run,2,0,2024,2204,10.44,5869.8,12.8,Run,0,0,0,0,51.4755,-0.108554,
+2021-10-06 18:35:27,Evening Run,4,1,1817,2187,10.9908,5546.4,12,Run,0,0,0,0,51.4756,-0.108592,
+2021-09-16 08:44:34,Morning Run,5,0,2621,2782,9.6192,7004,6.6,Run,0,0,0,0,52.7238,-1.79313,
+2021-09-02 10:35:29,Morning Run,3,3,2395,3308,9.3204,6200.4,83.1,Run,0,0,0,0,55.7,14.1047,
+2021-08-23 19:38:24,Evening Run,3,3,2646,2832,9.972,7329.9,20.1,Run,0,0,0,0,52.5514,13.3504,
+2021-08-17 13:51:30,Afternoon Run,5,0,2868,3753,10.1124,8055.1,15.9,Run,0,0,0,0,52.5058,13.309,
+2021-08-10 18:52:03,"Running, walking, talking ",5,5,3043,5036,9.3456,7899.1,9.6,Run,0,0,0,0,52.506,13.309,
+2021-07-14 18:54:24,Evening Run,3,0,2281,2573,10.4724,6634.6,53.4,Run,0,0,0,0,48.14,11.5387,
+2021-07-08 18:35:03,Evening Run,4,0,2411,2832,10.638,7125.4,45.6,Run,0,0,0,0,48.1398,11.5388,
+2021-07-02 18:28:41,Evening Run,6,0,2757,3279,8.6976,6661,64.4,Run,0,0,0,0,48.1392,11.539,
+2021-06-29 17:51:37,Afternoon Run,6,0,3225,6333,10.7496,9629.1,89.5,Run,0,0,0,0,48.14,11.5382,
+2021-06-14 19:08:31,Tooooo hot,5,0,2287,2527,9.936,6313,61.5,Run,0,0,0,0,48.1271,11.5382,
+2021-06-08 18:55:54,Nice phone call and a run ,6,0,3666,3952,10.2708,10458.4,73,Run,0,0,0,0,48.1372,11.5402,
+2021-06-02 21:27:52,Night Run,2,0,1720,1883,12.2616,5858.2,16,Run,0,0,0,0,48.1398,11.5387,
+2021-05-31 18:47:27,Evening Run,6,0,3971,4434,9.9288,10951.5,87.6,Run,0,0,0,0,48.1399,11.539,
+2021-05-24 18:48:45,Evening Run,6,0,3425,3641,10.134,9640.2,58.5,Run,0,0,0,0,48.1398,11.5387,
+2021-05-17 19:30:07,Wiggly and hilly ,4,0,2450,3202,10.5876,7204.6,124.3,Run,0,0,0,0,43.7695,4.12127,
+2021-05-15 15:08:40,Afternoon Run,4,0,3818,4164,10.458,11089.6,68.2,Run,0,0,0,0,43.7699,4.12157,
+2021-05-13 13:03:53,Afternoon Run,3,3,1957,2059,11.1312,6051.1,26.1,Run,0,0,0,0,49.0193,2.08805,
+2021-05-03 17:09:06,Afternoon Run,6,0,3077,3142,10.5588,9024.9,71.5,Run,0,0,0,0,48.1398,11.5391,
+2021-04-16 17:49:34,Afternoon Run,6,0,2978,3676,10.1988,8437.3,57.8,Run,0,0,0,0,48.1388,11.5391,
+2021-04-08 09:27:07,Morning Run,6,0,2869,4730,9.9288,7913.1,53.7,Run,0,0,0,0,48.1338,11.5401,
+2021-03-23 19:10:18,Evening Run,4,2,3858,4168,10.6632,11428.8,64.8,Run,0,0,0,0,43.7694,4.12132,
+2021-03-22 19:10:15,Evening Run,2,0,1403,1435,10.8576,4232,42.6,Run,0,0,0,0,43.7693,4.12126,
+2021-03-15 19:31:28,Evening Run,4,0,2752,2849,10.0908,7714.4,30.5,Run,0,0,0,0,48.1394,11.5392,
+2021-03-08 19:39:43,Evening Run,3,0,2440,2528,10.4796,7103.7,43.5,Run,0,0,0,0,48.1401,11.5386,
+2021-03-05 08:45:33,Morning Run,3,0,2963,3441,10.1592,8361.1,45.5,Run,0,0,0,0,48.1395,11.5393,
+2021-02-03 20:06:33,Evening Run,6,0,2933,3042,11.0664,9016.7,53,Run,0,0,0,0,48.14,11.539,
+2021-01-26 16:47:44,Afternoon Run,3,3,2842,3068,11.1996,8842,26.2,Run,0,0,0,0,51.5051,-0.308281,
+2021-01-14 08:45:00,"Icy, windy and press-ups :(",7,1,3734,4112,10.8072,11210.8,73.5,Run,0,0,0,0,48.1393,11.539,
+2021-01-11 17:05:53,Gentle run on icy paths ,3,0,1829,1998,11.2644,5722.9,22.4,Run,0,0,0,0,48.1399,11.5381,
+2021-01-04 17:12:56,Afternoon Run,3,0,1367,1422,13.1976,5011.5,21.8,Run,0,0,0,0,48.1398,11.5391,
+2020-12-26 08:59:21,Blowing out the cobwebs...,6,6,3998,4534,10.2204,11350.6,203.8,Run,0,0,0,0,49.1418,9.10928,
+2020-12-11 09:19:03,"Gentle Run no pace feedback, 10 press-ups or 5 pull-ups every song change ",6,0,3502,4194,10.836,10541.3,80.1,Run,0,0,0,0,48.1375,11.5411,
+2020-12-03 09:56:32,Morning Run,6,0,3759,3876,10.4832,10946.5,68.8,Run,0,0,0,0,48.1374,11.5408,
+2020-11-28 18:12:26,Evening Run,6,0,3314,3541,11.4588,10547.8,82.6,Run,0,0,0,0,48.1372,11.5405,
+2020-11-15 15:42:04,Afternoon Run,6,0,2432,2488,12.3516,8344.1,57.5,Run,0,0,0,0,48.1372,11.5403,
+2020-11-10 19:25:49,Evening Run,3,0,1826,1890,12.2364,6206.5,41.8,Run,0,0,0,0,48.1373,11.5406,
+2020-11-06 17:55:46,Afternoon Run,6,6,2521,350731,9.7488,6828.1,70.7,Run,0,0,0,0,48.1373,11.5405,
+2020-10-31 15:02:00,Afternoon Run,5,0,4276,6000,11.322,13446.1,72.5,Run,0,0,0,0,48.1228,11.5883,
+2020-10-22 18:05:38,Evening Run,5,1,3694,4508,9.7776,10033.5,70.1,Run,0,0,0,0,48.1224,11.5883,
+2020-10-20 18:29:02,Evening Run,3,0,3520,3520,10.7604,10520.3,72.2,Run,0,0,0,0,48.1226,11.5886,
+2020-10-03 09:04:51,Morning Run,5,2,3815,4015,9.4284,9990.4,70.3,Run,0,0,0,0,48.1227,11.5882,
+2020-09-13 09:19:36,Morning Run,4,0,3778,4489,10.4796,10996.9,20.8,Run,0,0,0,0,48.1643,11.5838,
+2020-09-05 10:01:27,Morning Run,6,1,3983,4147,9.666,10695.9,22.3,Run,0,0,0,0,48.1643,11.5835,
+2020-09-02 10:29:53,Morning Run,2,0,1902,2055,10.3428,5465.1,14.8,Run,0,0,0,0,48.1642,11.5836,
+2020-08-14 20:09:25,Evening Run,2,2,1297,1394,11.7396,4229.6,39.2,Run,0,0,0,0,45.941,6.89233,
+2020-07-29 19:24:12,Evening Run,4,0,3546,4308,10.1628,10008.9,17.6,Run,0,0,0,0,52.7236,-1.79298,
+2020-07-27 18:00:22,Evening Run,4,0,3459,3936,10.8072,10382.3,27.5,Run,0,0,0,0,52.7236,-1.79319,
+2020-07-19 19:17:54,Evening Run,3,0,1771,1933,12.2364,6019.4,15.5,Run,0,0,0,0,52.7238,-1.79294,
+2020-07-16 20:53:24,6 press-ups per km,3,1,3368,3756,10.6992,10008.6,11.6,Run,0,0,0,0,52.7236,-1.79295,
+2020-07-14 19:31:23,6 press-ups per km,4,0,3397,3761,10.6236,10024,26,Run,0,0,0,0,52.7239,-1.79276,
+2020-07-13 18:03:30,Evening Run,4,0,4066,5571,10.4112,11759.5,17.4,Run,0,0,0,0,52.7236,-1.79261,
+2020-07-02 21:06:56,Night Run,6,2,4584,5831,10.4184,13267.8,30.6,Run,0,0,0,0,52.7235,-1.79278,
+2020-06-29 19:58:01,So windy ,5,0,3618,4135,10.512,10566.2,27.3,Run,0,0,0,0,52.7237,-1.79295,
+2020-06-25 21:49:16,Night Run,4,0,3601,4086,10.5336,10537.5,27.7,Run,0,0,0,0,52.7235,-1.79245,
+2020-06-20 19:35:38,Evening Run,3,1,2660,3502,10.0872,7453.3,15.4,Run,0,0,0,0,52.7234,-1.79207,
+2020-06-18 10:08:04,Easing back in ,4,0,1307,1783,11.1312,4040.8,10,Run,0,0,0,0,52.7234,-1.79257,
+2020-06-02 18:25:43,Evening Run,5,0,1876,1952,11.6388,6064.7,6.9,Run,0,0,0,0,52.7237,-1.79311,
+2020-05-25 19:32:18,Evening Run,4,0,3299,3573,10.944,10027.5,23.2,Run,0,0,0,0,52.7238,-1.79244,
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+" clip-path="url(#pfe34a7b2e2)" style="fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #000000; stroke-width: 1.5"/>
+   </g>
+   <g id="line2d_23">
+    <path d="M 57.681818 310.966845 
+L 311.318182 160.036364 
+" clip-path="url(#pfe34a7b2e2)" style="fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #000000; stroke-width: 1.5"/>
+   </g>
+   <g id="patch_36">
+    <path d="M 45 320.4 
+L 45 152.4 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_37">
+    <path d="M 45 320.4 
+L 324 320.4 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="text_18">
+    <!-- 5 min/km -->
+    <g transform="translate(316.602273 207.715525) scale(0.1 -0.1)">
+     <defs>
+      <path id="DejaVuSans-2f" d="M 1625 4666 
+L 2156 4666 
+L 531 -594 
+L 0 -594 
+L 1625 4666 
+z
+" transform="scale(0.015625)"/>
+     </defs>
+     <use xlink:href="#DejaVuSans-35"/>
+     <use xlink:href="#DejaVuSans-20" x="63.623047"/>
+     <use xlink:href="#DejaVuSans-6d" x="95.410156"/>
+     <use xlink:href="#DejaVuSans-69" x="192.822266"/>
+     <use xlink:href="#DejaVuSans-6e" x="220.605469"/>
+     <use xlink:href="#DejaVuSans-2f" x="283.984375"/>
+     <use xlink:href="#DejaVuSans-6b" x="317.675781"/>
+     <use xlink:href="#DejaVuSans-6d" x="375.585938"/>
+    </g>
+   </g>
+   <g id="text_19">
+    <!-- 6 min/km -->
+    <g transform="translate(316.602273 185.255632) scale(0.1 -0.1)">
+     <defs>
+      <path id="DejaVuSans-36" d="M 2113 2584 
+Q 1688 2584 1439 2293 
+Q 1191 2003 1191 1497 
+Q 1191 994 1439 701 
+Q 1688 409 2113 409 
+Q 2538 409 2786 701 
+Q 3034 994 3034 1497 
+Q 3034 2003 2786 2293 
+Q 2538 2584 2113 2584 
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+M 3366 4563 
+L 3366 3988 
+Q 3128 4100 2886 4159 
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+Q 1781 4219 1451 3797 
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+Q 447 1150 447 2328 
+Q 447 3434 972 4092 
+Q 1497 4750 2381 4750 
+Q 2619 4750 2861 4703 
+Q 3103 4656 3366 4563 
+z
+" transform="scale(0.015625)"/>
+     </defs>
+     <use xlink:href="#DejaVuSans-36"/>
+     <use xlink:href="#DejaVuSans-20" x="63.623047"/>
+     <use xlink:href="#DejaVuSans-6d" x="95.410156"/>
+     <use xlink:href="#DejaVuSans-69" x="192.822266"/>
+     <use xlink:href="#DejaVuSans-6e" x="220.605469"/>
+     <use xlink:href="#DejaVuSans-2f" x="283.984375"/>
+     <use xlink:href="#DejaVuSans-6b" x="317.675781"/>
+     <use xlink:href="#DejaVuSans-6d" x="375.585938"/>
+    </g>
+   </g>
+   <g id="text_20">
+    <!-- 7 min/km -->
+    <g transform="translate(316.602273 162.795739) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-37"/>
+     <use xlink:href="#DejaVuSans-20" x="63.623047"/>
+     <use xlink:href="#DejaVuSans-6d" x="95.410156"/>
+     <use xlink:href="#DejaVuSans-69" x="192.822266"/>
+     <use xlink:href="#DejaVuSans-6e" x="220.605469"/>
+     <use xlink:href="#DejaVuSans-2f" x="283.984375"/>
+     <use xlink:href="#DejaVuSans-6b" x="317.675781"/>
+     <use xlink:href="#DejaVuSans-6d" x="375.585938"/>
+    </g>
+   </g>
+   <g id="patch_38">
+    <path d="M 292.220106 260.895959 
+Q 281.089109 234.632989 270.394399 209.399414 
+" style="fill: none; stroke: #000000; stroke-linecap: round"/>
+    <path d="M 270.113869 213.862742 
+L 270.394399 209.399414 
+L 273.796744 212.301835 
+" style="fill: none; stroke: #000000; stroke-linecap: round"/>
+   </g>
+   <g id="text_21">
+    <!-- Half Marathon! -->
+    <g transform="translate(258.477273 272.335829) scale(0.1 -0.1)">
+     <defs>
+      <path id="DejaVuSans-48" d="M 628 4666 
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+z
+" transform="scale(0.015625)"/>
+      <path id="DejaVuSans-68" d="M 3513 2113 
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+      <path id="DejaVuSans-21" d="M 966 794 
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+     </defs>
+     <use xlink:href="#DejaVuSans-48"/>
+     <use xlink:href="#DejaVuSans-61" x="75.195312"/>
+     <use xlink:href="#DejaVuSans-6c" x="136.474609"/>
+     <use xlink:href="#DejaVuSans-66" x="164.257812"/>
+     <use xlink:href="#DejaVuSans-20" x="199.462891"/>
+     <use xlink:href="#DejaVuSans-4d" x="231.25"/>
+     <use xlink:href="#DejaVuSans-61" x="317.529297"/>
+     <use xlink:href="#DejaVuSans-72" x="378.808594"/>
+     <use xlink:href="#DejaVuSans-61" x="419.921875"/>
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+     <use xlink:href="#DejaVuSans-21" x="708.349609"/>
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+   </g>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="pd8b7aec6a1">
+   <rect x="45" y="43.2" width="279" height="84"/>
+  </clipPath>
+  <clipPath id="pfe34a7b2e2">
+   <rect x="45" y="152.4" width="279" height="168"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/assets/blog/running/vdot.csv b/assets/blog/running/vdot.csv
new file mode 100644
index 0000000..e40c7bf
--- /dev/null
+++ b/assets/blog/running/vdot.csv
@@ -0,0 +1,10 @@
+VDOT, 1500m, mile, 3000, 2_mile, 5000, 10K, 15K, half_marathon, marathon
+30, 8:30, 9:11, 17:56, 19:19, 30:40, 63:46, 98:14, 2:21:04, 4:49:17
+31, 8:15, 8:55, 17:27, 18:48, 29:51, 62:04, 95:36, 2:17:21, 4:41:57
+32, 8:02, 8:41, 16:59, 18:18, 29:05, 60:26, 93:07, 2:13:49, 4:34:59
+33, 7:49, 8:27, 16:33, 17:50, 28:21, 58:54, 90:45, 2:10:27, 4:28:22
+34, 7:37, 8:14, 16:09, 17:24, 27:39, 57:26, 88:30, 2:07:16, 4:22:03
+35, 7:25, 8:01, 15:45, 16:58, 27:00, 56:03, 86:22, 2:04:13, 4:16:03
+36, 7:14, 7:49, 15:23, 16:34, 26:22, 54:44, 84:20, 2:01:19, 4:10:19
+37, 7:04, 7:38, 15:01, 16:11, 25:46, 53:29, 82:24, 1:58:34, 4:04:50
+38, 6:54, 7:27, 14:41, 15:49, 25:12, 52:17, 80:33, 1:55:55, 3:59:35
\ No newline at end of file
diff --git a/assets/blog/shelves/model/env.jpg b/assets/blog/shelves/model/env.jpg
new file mode 100644
index 0000000..c6ec13c
Binary files /dev/null and b/assets/blog/shelves/model/env.jpg differ
diff --git a/assets/blog/shelves/model/shelves.glb b/assets/blog/shelves/model/shelves.glb
new file mode 100644
index 0000000..5b37639
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diff --git a/assets/blog/shelves/model/shelves.png b/assets/blog/shelves/model/shelves.png
new file mode 100644
index 0000000..0348f67
Binary files /dev/null and b/assets/blog/shelves/model/shelves.png differ
diff --git a/assets/blog/toothbrush_shelf/crop.jpeg b/assets/blog/toothbrush_shelf/crop.jpeg
index bc2708e..6742d1f 100644
Binary files a/assets/blog/toothbrush_shelf/crop.jpeg and b/assets/blog/toothbrush_shelf/crop.jpeg differ
diff --git a/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb b/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb
index 4e246d1..72ecb92 100644
Binary files a/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb and b/assets/blog/toothbrush_shelf/model/toothbrush_shelf.glb differ
diff --git a/assets/blog/toothbrush_shelf/model/poster.webp b/assets/blog/toothbrush_shelf/model/toothbrush_shelf.webp
similarity index 100%
rename from assets/blog/toothbrush_shelf/model/poster.webp
rename to assets/blog/toothbrush_shelf/model/toothbrush_shelf.webp
diff --git a/assets/blog/vector_magnet/aircraft_workshop_01_1k.hdr b/assets/blog/vector_magnet/aircraft_workshop_01_1k.hdr
new file mode 100644
index 0000000..ee5bdea
Binary files /dev/null and b/assets/blog/vector_magnet/aircraft_workshop_01_1k.hdr differ
diff --git a/assets/blog/vector_magnet/section_view.png b/assets/blog/vector_magnet/section_view.png
new file mode 100644
index 0000000..9d34b99
Binary files /dev/null and b/assets/blog/vector_magnet/section_view.png differ
diff --git a/assets/blog/vector_magnet/test_section.glb b/assets/blog/vector_magnet/test_section.glb
new file mode 100644
index 0000000..15b4edb
Binary files /dev/null and b/assets/blog/vector_magnet/test_section.glb differ
diff --git a/assets/blog/vector_magnet/test_whole.glb b/assets/blog/vector_magnet/test_whole.glb
new file mode 100644
index 0000000..903511d
Binary files /dev/null and b/assets/blog/vector_magnet/test_whole.glb differ
diff --git a/assets/blog/vector_magnet/vector_magnet.glb b/assets/blog/vector_magnet/vector_magnet.glb
new file mode 100644
index 0000000..dd2bb75
Binary files /dev/null and b/assets/blog/vector_magnet/vector_magnet.glb differ
diff --git a/assets/blog/vector_magnet/vector_magnet.png b/assets/blog/vector_magnet/vector_magnet.png
new file mode 100644
index 0000000..e46a61d
Binary files /dev/null and b/assets/blog/vector_magnet/vector_magnet.png differ
diff --git a/assets/blog/vector_magnet/vector_magnet_section.glb b/assets/blog/vector_magnet/vector_magnet_section.glb
new file mode 100644
index 0000000..0ba0f8b
Binary files /dev/null and b/assets/blog/vector_magnet/vector_magnet_section.glb differ
diff --git a/assets/social/pcb_1.png b/assets/social/pcb_1.png
index 4a91158..2082172 100644
Binary files a/assets/social/pcb_1.png and b/assets/social/pcb_1.png differ
diff --git a/cv.html b/cv.html
index f3b46ab..26aabd3 100644
--- a/cv.html
+++ b/cv.html
@@ -15,6 +15,14 @@ or have a look at my <a href="/blog">blog</a>. Click any item for a little more
     </div>
     
     {% for entry in work %}
+      {% if entry.image_markup %}
+      {% assign image_markup = entry.image_markup %}
+      {% else %}
+      {% capture image_markup %} 
+      <img  src="{{ entry.image }}" alt="{{ entry.alt }}">
+      {% endcapture %}
+      {% endif %}
+
       <details class="{{topic.id}}">
       <summary class="cv"><time>{{entry.period}}</time>
                         <div class = "title-column">
@@ -24,7 +32,7 @@ or have a look at my <a href="/blog">blog</a>. Click any item for a little more
       </summary>
       <div class = "details-container">
           <div class = "details-img">
-              <a href="{{entry.url}}"><img  src="{{entry.image}}" alt="{{entry.alt}}"></a>
+              <a href="{{entry.url}}">{{ image_markup }}</a>
           </div>
           <div class = "details-text">
               {{entry.excerpt}}
diff --git a/index.html b/index.html
deleted file mode 100644
index 641c069..0000000
--- a/index.html
+++ /dev/null
@@ -1,4 +0,0 @@
----
-title: Home
-redirect_to: /blog
----
\ No newline at end of file
diff --git a/blog.html b/index.md
similarity index 91%
rename from blog.html
rename to index.md
index b27b5ef..8981cbf 100644
--- a/blog.html
+++ b/index.md
@@ -1,7 +1,9 @@
 ---
-layout: default
 title: Blog
-permalink: /blog/
+layout: default
+permalink: /
+redirect_from:
+  - /blog/
 ---
 {% for post in site.posts %}
 <article class="h-entry">
@@ -10,4 +12,4 @@ permalink: /blog/
     <summary class="p-summary">{{ post.excerpt | markdownify | remove: '<p>' | remove: '</p>' }}</summary>
     <figure {% if post.hide_image %} style="display:none;" {% endif %} class="blogroll"><img class="u-photo" src = "{{post.image}}"></figure>
 </article>
-{% endfor %}
+{% endfor %}
\ No newline at end of file
diff --git a/run.sh b/run.sh
index aad51a1..47dce04 100755
--- a/run.sh
+++ b/run.sh
@@ -1,3 +1,3 @@
 #!/usr/bin/env bash
-sleep 3 && open --url http://0.0.0.0:4000 &
+sleep 5 && open --url http://0.0.0.0:4000 &
 bundle exec jekyll serve --draft --future
\ No newline at end of file