some more clean up (#72)

docs/.vitepress/config.mts

@@ -59,14 +59,15 @@ export default defineConfig({
         text: 'ModelKit',
         items: [
           { text: 'Introduction', link: '/docs/modelkit/intro' },
-          { text: 'Specification', link: 'docs/modelkit/spec.md' },
+          { text: 'Specification', link: '/docs/modelkit/spec' },
+          { text: 'Compatibility', link: '/docs/modelkit/compatibility' },
         ]
       },
       {
         text: 'Kitfile',
         items: [
           { text: 'Overview', link: '/docs/kitfile/kf-overview' },
-          { text: 'Format', link: '/docs/kitfile/format.md' },
+          { text: 'Format', link: '/docs/kitfile/format' },
         ]
       },
       {

docs/.vitepress/theme/components/Home.vue

@@ -197,7 +197,7 @@ import { VueMarqueeSlider } from 'vue3-marquee-slider'
     </VueMarqueeSlider>
   </div>
 
-  <button class="kit-button mt-22">SEE FULL LIST</button>
+  <a href="/docs/compatibility.html" class="kit-button mt-22">SEE FULL LIST</a>
 </div>
 
 <div class="mt-32 md:mt-40 xl:mt-60 px-6">

docs/src/docs/use-cases.md

@@ -1,16 +1,7 @@
 # Use Cases
 
-## Table of Contents
-
-* [Use Case Context](#use-case-context)
-* Collaborating on an AI-Enabled Application for Production
-  * [AI/ML Team Collaboration](#aiml-team-collaboration)
-  * [AI/ML and App Team Collaboration](#aiml-and-app-team-collaboration)
-  * [AI/ML, App, and SRE Team Collaboration](#aiml-app-and-sre-team-collaboration)
-* Collaborating on an Internal Model
-  * [AI/ML and SRE Team Collaboration](#aiml-and-sre-team-collaboration)
-
 ## Use Case Context
+
 Weyland-Yutani Corporation has been adding AI and ML models to its portfolio for both internal- and customer-facing deployment. There are three main groups that are involved in the development lifecycle for their AI/ML work:
 * AI/ML Team, composed of data scientists, data engineers, and MLOps engineers. This specialized group builds, tunes, and validates the models that increasingly touch every aspect of the organization's work.
 * Application Teams, composed of software and hardware engineers. This group builds the applications that the business and customers interact with - over time more and more of these applications are being integrated into the models the AI/ML team works on.
@@ -20,7 +11,9 @@ There has been a heated argument within Weyland-Yutani about how best to manage
 
 ...but all that's about to change! (Hint: the change involves KitOps...surprise!)
 
-## AI/ML Team Collaboration
+## Collaborating on an AI-Enabled Application for Production
+
+### AI/ML Team Collaboration
 
 Rajat is working on tuning an open source foundational model for his company. After several days of work in his Jupyter notebook, he has a model that is outperforming the old model they've been using in production. But before he alerts the App and SRE teams Rajat wants to have another data scientist try the model and verify his findings.
 
@@ -43,9 +36,9 @@ Gorkem loads Rajat's notebook file and runs through Rajat's tests. He Slacks him
 
 Thanks KitOps!
 
-## AI/ML and App Team Collaboration
+### AI/ML and App Team Collaboration
 
-Now that [Rajat and Gorkem have both agreed](#aiml-team-collaboration) that the newly trained model is a real challenger for production, they alert the Application Team who will need to do an integration test between their app and the model.
+Now that [Rajat and Gorkem have both agreed](#ai-ml-team-collaboration) that the newly trained model is a real challenger for production, they alert the Application Team who will need to do an integration test between their app and the model.
 
 Rajat Slacks the application team a heads-up, and Nida volunteers to kick off integration testing. She needs the model and hopes that Rajat included a validation dataset. Nida uses the Kit CLI's flexible pull command to only extract the new model's Kitfile and check whether there's a validation dataset included:
 
@@ -65,9 +58,9 @@ Nida is a bit of an introvert so the fact that she didn't need to ask anyone for
 
 Thanks KitOps!
 
-## AI/ML, App, and SRE Team Collaboration
+### AI/ML, App, and SRE Team Collaboration
 
-When the second [integration pipeline that Nida kicked off](#aiml-and-app-team-collaboration) completed successfully it automatically notified Annika in the SRE team. Annika quickly looks over the integration test results and sees that the model and application are ready for deployment. She issues a PR to Weyland-Yutani's GitOps repository to kick off the production deployment pipeline in GitLab.
+When the second [integration pipeline that Nida kicked off](#ai-ml-and-app-team-collaboration) completed successfully it automatically notified Annika in the SRE team. Annika quickly looks over the integration test results and sees that the model and application are ready for deployment. She issues a PR to Weyland-Yutani's GitOps repository to kick off the production deployment pipeline in GitLab.
 
 Part of the GitOps automation calls the Kit CLI to extract only the model (saving a heap of time by not having to pull a +10GB dataset that isn't needed):
 
@@ -88,7 +81,9 @@ Once the deployment is complete across all galactic regions, Annika sends an upd
 
 Thanks KitOps!
 
-## AI/ML and SRE Team Collaboration
+## Collaborating on an Internal Model
+
+### AI/ML and SRE Team Collaboration
 
 Angel leads an internal data science team that builds models to help Weyland-Yutani's executives make faster and better decisions with the help of AI/ML models. Their team was asked in the most recent monthly business review to identify all the customers they have who might be a churn risk based on the set of common traits and behaviours seen in all churned customers over the last five years.