updating titles and intro

content/article.md

@@ -1,11 +1,19 @@
 
 ## Introduction 
 
-The `transformers` library, built with `PyTorch`, supports all state-of-the-art LLMs, many VLMs, task-specific vision language models, video models, audio models, table models, classical encoders, to a global count of almost 400 models. The name of the library itself is mostly majority driven as many models are not even transformers architectures, like Mamba/RWKV.  Regardless, each of these is wrought by the research and engineering team that created them, then harmonized into a now famous interface, and callable with a simple `.from_pretrained`. Inference and training are supported. The library supports ML courses, cookbooks, and several thousands other open-source libraries depend on it. All models are tested as part of a daily CI ensuring their preservation and reproducibility. Most importantly, it is open-source and has been written by the community for a large part.
+The `transformers` library, built with `PyTorch`, supports all state-of-the-art LLMs, many VLMs, task-specific vision language models, video models, audio models, table models, classical encoders, to a global count of almost 400 models. 
 
-The ML wave has not stopped, there's more and more models being added. `Transformers` is widely used, and we read the feedback that users post. Whether it's about a function that had 300+ keyword arguments, duplicated code and helpers, and mentions of `Copied from ... ` everywhere, along with optimisation concerns. Text-only models are relatively tamed, but multimodal models remain to be harmonized.
+The name of the library itself is mostly majority driven as many models are not even transformers architectures, like Mamba, Zamba, RWKV, and convolution-based models.
 
-Here we will dissect what is the design philosophy of transformers, as a continuation from the existing older [philosophy](https://huggingface.co/docs/transformers/en/philosophy) page, and an accompanying [blog post from 2022](https://huggingface.co/blog/transformers-design-philosophy) . Some time ago I dare not say how long, we discussed with transformers maintainers about the state of things. A lot of recent developments were satisfactory, but if we were only talking about these, self-congratulation would be the only goalpost. Reflecting on this philosophy now, as models pile up, is essential and will drive new developments. 
+Regardless, each of these is wrought by the research and engineering team that created them, then harmonized into a now famous interface, and callable with a simple `.from_pretrained` command.
+
+Inference works for all models, training is functional for most. The library is a foundation for many machine learning courses, cookbooks, and overall, several thousands other open-source libraries depend on it. All models are tested as part of a daily CI ensuring their preservation and reproducibility. Most importantly, it is _open-source_ and has been written by the community for a large part.
+
+This isn't really to brag but to set the stakes: what does it take to keep such a ship afloat, made of so many moving, unrelated parts? 
+
+The ML wave has not stopped, there's more and more models being added, at a steadily growing rate. `Transformers` is widely used, and we read the feedback that users post online. Whether it's about a function that had 300+ keyword arguments, duplicated code and helpers, and mentions of `Copied from ... ` everywhere, along with optimisation concerns. Text-only models are relatively tamed, but multimodal models remain to be harmonized.
+
+Here we will dissect what is the new design philosophy of transformers, as a continuation from the existing older [philosophy](https://huggingface.co/docs/transformers/en/philosophy) page, and an accompanying [blog post from 2022](https://huggingface.co/blog/transformers-design-philosophy) . Some time ago I dare not say how long, we discussed with transformers maintainers about the state of things. A lot of recent developments were satisfactory, but if we were only talking about these, self-congratulation would be the only goalpost. Reflecting on this philosophy now, as models pile up, is essential and will drive new developments. 
 
 ### What you will learn
 

webpack.config.js

@@ -139,7 +139,7 @@ module.exports = {
                                 
                                 // Extract tenet text for tooltips
                                 const tenetTooltips = {
-                                    'source-of-truth': 'We should be a source of truth for all model definitions. Model implementations should be reliable, reproducible, and faithful to the original performances.',
+                                    'source-of-truth': 'We aim to be a source of truth for all model definitions. Model implementations should be reliable, reproducible, and faithful to the original performances.',
                                     'one-model-one-file': 'All inference (and most of training, loss is separate, not a part of model) logic visible, top‑to‑bottom.',
                                     'code-is-product': 'Optimize for reading, diffing, and tweaking, our users are power users. Variables can be explicit, full words, even several words, readability is primordial.',
                                     'standardize-dont-abstract': 'If it\\'s model behavior, keep it in the file; abstractions only for generic infra.',
@@ -225,22 +225,22 @@ module.exports = {
     <script src="https://d3js.org/d3.v7.min.js"></script>
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <meta charset="utf8">
-    <title>Transformers Feature Showcase</title>
+    <title>Scaling insanity: maintaining hundreds of model definitions</title>
     <link rel="stylesheet" href="style.css">
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/themes/prism.min.css">
 </head>
 <body>
     <d-front-matter>
         <script id='distill-front-matter' type="text/json">{
-    "title": "Transformers Feature Showcase",
-    "description": "An interactive demonstration of transformers library features and design philosophy.",
+    "title": "Scaling insanity: maintaining hundreds of model definitions",
+    "description": "A peek into software engineering for the transformers library",
     "published": "Aug 21, 2025",
     "authors": [{"author": "Pablo Montalvo", "authorURL": "https://huggingface.co/Molbap"}]
   }</script>
     </d-front-matter>
     <d-title>
-        <h1>Transformers Feature Showcase</h1>
-        <p>An interactive demonstration of transformers library features and design philosophy.</p>
+        <h1>Scaling insanity: maintaining hundreds of model definitions</h1>
+        <p>A peek into software engineering for the transformers library</p>
     </d-title>
     <d-byline></d-byline>
     <d-article>