standardize figure captions

diff --git a/content/articles/minimum-utxo-value/index.md b/content/articles/minimum-utxo-value/index.md
index 15018a1bb545e08c8c0ef76211d629748a015ea8..aac83838853ef6f152e9f3ba62f1fe9fdf78a6da 100644 (file)
--- a/content/articles/minimum-utxo-value/index.md
+++ b/content/articles/minimum-utxo-value/index.md
@@ -25,7 +25,7 @@ The highest fee periods in Bitcoin history have been 2018, 2021, and 2023:
 
 <figure>
     {% image "./minimum-utxo-value_mempool_vbytes_history.svg", "Historical graph of Bitcoin mempool by vBytes" %}
-    <figcaption>Source: <a href="https://mempool.space/graphs/mempool#all">mempool.space</a></figcaption>
+    <figcaption>Source: <a href="https://mempool.space/graphs/mempool#all">mempool.space</a>.</figcaption>
 </figure>
 
 I've selected 4 blocks to deep dive, based on their proximity to mempool peaks in those three years, as well as a more "normal" current block:
@@ -36,7 +36,7 @@ I've selected 4 blocks to deep dive, based on their proximity to mempool peaks i
 
 <figure>
     {% image "./minimum-utxo-value_box-whisker-overall.svg", "Box and whisker plot of fee rates across blocks" %}
-    <figcaption>Outliers removed</figcaption>
+    <figcaption>Outliers removed.</figcaption>
 </figure>
 
 | **Fee Rate (sats/vB)** | **504000** | **680000** | **782400** | **801171** |

diff --git a/content/articles/r1-vs-o1-ai-as-commodity-or-moat/index.md b/content/articles/r1-vs-o1-ai-as-commodity-or-moat/index.md
index 59fa2ae5e99c774c14fd1328927ed48c9cd453ea..9aa5535d37b513ff47846c32d8c2bf69d52fcd6a 100644 (file)
--- a/content/articles/r1-vs-o1-ai-as-commodity-or-moat/index.md
+++ b/content/articles/r1-vs-o1-ai-as-commodity-or-moat/index.md
@@ -27,21 +27,21 @@ DeepSeek trained R1 for an estimated $5.6MM on A100 and H800 GPUs (not the lates
 
 <figure>
     {% image "./input-output-pricing.jpg", "Input/Output Pricing for o1-Class Inference Models ($/1M Tokens)" %}
-    <figcaption>Source: <a href="https://x.com/RnaudBertrand/status/1881709223152878000/photo/1">@RnaudBertrand</a></figcaption>
+    <figcaption>Source: <a href="https://x.com/RnaudBertrand/status/1881709223152878000/photo/1">@RnaudBertrand</a>.</figcaption>
 </figure>
 
 Despite less capital expenditure, R1 has comparable performance to o1 due to several technical innovations. I don't pretend to deeply understand everything on this front, so instead I'll direct you [to this paper](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf), which breaks down what's happening under the hood. Ultimately, R1 is essentially a drop-in replacement for o1.
 
 <figure>
     {% image "./r1-o1-benchmarks.jpg", "Accuracy / Percentile (%) by Benchmark" %}
-    <figcaption>Source: <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf">DeepSeek-AI</a></figcaption>
+    <figcaption>Source: <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf">DeepSeek-AI</a>.</figcaption>
 </figure>
 
 This is incredible for the little guy: run your own model, privately, without the need for massive infrastructure.
 
 <figure>
     {% image "./r1-on-iphone-localghost.gif", "R1 running locally on an iPhone" %}
-    <figcaption>A distilled version of R1 running locally on an iPhone. Source: <a href="https://x.com/localghost/status/1882109711732154387">@localghost</a></figcaption>
+    <figcaption>A distilled version of R1 running locally on an iPhone. Source: <a href="https://x.com/localghost/status/1882109711732154387">@localghost</a>.</figcaption>
 </figure>
 
 ## Is OpenAI Cooked?