docs: minor fixes

Signed-off-by: tarilabs <matteo.mortari@gmail.com>

docs/demos/demo2.md

@@ -9,7 +9,7 @@ hide:
 <iframe width="560" height="315" src="https://www.youtube.com/embed/n2Fmt-hsnLM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 </div>
 
-<!-- TODO revise this lorem ipsum -->
+_TODO: revise this summary_
 
 In this follow-up demo, we focus on using OCI Artifacts for Machine Learning model assets and their metadata, specifically within the context of "ModelCar". We start by recapping the previous setup where we wrapped a machine learning model as an OCI artifact and then as a ModelCar. Today, we'll explore three demos: using the ModelCar in a traditional KServe setup, using it within a KServe raw environment, and directly utilizing the OCI artifact in KServe with a custom storage initializer.
 

docs/demos/demo3.md

@@ -9,7 +9,7 @@ hide:
 <iframe width="560" height="315" src="https://www.youtube.com/embed/B3K0z8LMROE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 </div>
 
-<!-- TODO revise this lorem ipsum -->
+_TODO revise this summary_
 
 In this demo, we expand on using OCI Artifacts and underlying infrastructure for storing and distributing machine learning model assets, and their metadata. We focus on Signatures and Attestations, which are crucial for building a _trusted model supply chain_. Ensuring a trusted software supply chain is vital, especially in MLOps, where model Provenance and Lineage are essential to confirm that models put into production are secure and traceable.
 

docs/overview.md

@@ -4,7 +4,7 @@ This project aims to demonstrate how ML model and related metadata can be stored
 
 !!! tip "For the impatients"
 
-    You can jump straight to [Demo 1](demos/demo).
+    You can jump straight to [Demo 1](/demos/demo).
 
 The proposed OCI Artifact for ML model and metadata can be organized and then stored in OCI compliant registries with a format similar to the following:
 

mkdocs.yml

@@ -1,4 +1,5 @@
 site_name: OCI Artifact for ML model & metadata
+site_url: https://tarilabs.github.io/omlmd
 repo_url: https://github.com/tarilabs/omlmd
 repo_name: tarilabs/omlmd
 theme: