fix: dead chapter README Colab links

.url_check_allowlist.txt

@@ -9,10 +9,6 @@
 # Code of Conduct boilerplate
 http://geekfeminism.wikia.com/wiki/Conference_anti-harassment/Policy  # CODE_OF_CONDUCT.md:305
 
-# Colab refs to notebooks no longer in HowTo/workspace
-https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.14.2/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb  # scripts/chapter_1_introduction/README.md:16
-https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.14.2/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb  # scripts/chapter_3_graphical_models/README.md:17
-
 # GitHub refs (workspaces / removed tutorials)
 https://github.com/Jammy2211/PyAutoLogo/blob/main/gifs/pyautofit.gif?raw=true  # README.md:7
 https://github.com/PyAutoLabs/HowToFit/blob/main/scripts/chapter_1_introduction/images/bad_fit.png?raw=true  # scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py:452

notebooks/chapter_1_introduction/README.md

@@ -13,6 +13,6 @@ The chapter contains the following tutorials:
 
 - [Tutorial 3: Non Linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb) — Searching non-linear parameter spaces to find the best-fit model.
 
-- [Tutorial 4: Complex Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb) — Composing and fitting more complex models in a scalable and extensible way.
+- [Tutorial 4: Why Modeling Is Hard](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_why_modeling_is_hard.ipynb) — Why fitting complex models is challenging and how a good scientific approach overcomes common problems.
 
 - [Tutorial 5: Results and Samples](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb) — Interpreting model-fit results and using the samples for scientific analysis.

notebooks/chapter_3_graphical_models/README.md

@@ -14,6 +14,6 @@ The chapter contains the following tutorials:
 
 - [Tutorial 3: Graphical Benefits](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb) — Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one.
 
-- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework.
+- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierachical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework.
 
 - [Tutorial 5: Expectation Propagation](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb) — Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).

scripts/chapter_1_introduction/README.md

@@ -13,6 +13,6 @@ The chapter contains the following tutorials:
 
 - [Tutorial 3: Non Linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb) — Searching non-linear parameter spaces to find the best-fit model.
 
-- [Tutorial 4: Complex Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb) — Composing and fitting more complex models in a scalable and extensible way.
+- [Tutorial 4: Why Modeling Is Hard](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_why_modeling_is_hard.ipynb) — Why fitting complex models is challenging and how a good scientific approach overcomes common problems.
 
 - [Tutorial 5: Results and Samples](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb) — Interpreting model-fit results and using the samples for scientific analysis.

scripts/chapter_3_graphical_models/README.md

@@ -14,6 +14,6 @@ The chapter contains the following tutorials:
 
 - [Tutorial 3: Graphical Benefits](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb) — Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one.
 
-- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework.
+- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierachical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework.
 
 - [Tutorial 5: Expectation Propagation](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb) — Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).