diff --git a/content/Blog/demystifying_battery_technology.md b/content/Blog/demystifying_battery_technology.md
index daa001a..b13808b 100644
--- a/content/Blog/demystifying_battery_technology.md
+++ b/content/Blog/demystifying_battery_technology.md
@@ -101,5 +101,5 @@ Although we briefly discuss other battery chemistries for context, lithium ion b
 
 # References
 
-* https://batteryuniversity.com/
-* https://chem.libretexts.org/Courses/University_of_Arkansas_Little_Rock/Chem_1403%3A_General_Chemistry_2/Text/19%3A_Electron_Transfer_Reactions/19.03%3A_Electrochemical_Cells
+* [Battery University](https://batteryuniversity.com/)
+* [Basic description of battery chemistry](https://chem.libretexts.org/Courses/University_of_Arkansas_Little_Rock/Chem_1403%3A_General_Chemistry_2/Text/19%3A_Electron_Transfer_Reactions/19.03%3A_Electrochemical_Cells)
diff --git a/content/Blog/dwitter_simple.rst b/content/Blog/dwitter_simple.rst
index ac4a9b2..456e4d6 100644
--- a/content/Blog/dwitter_simple.rst
+++ b/content/Blog/dwitter_simple.rst
@@ -3,7 +3,7 @@ Two simple dweets
 
 :date: 2024-02-29
 :authors: Matt Gibson
-.. :tags: graphics, javascript, demoscene
+:tags: graphics, javascript, demoscene
 
 
 
diff --git a/content/Blog/stable_django.rst b/content/Blog/stable_django.rst
index 0bb6e38..f0cc5c0 100644
--- a/content/Blog/stable_django.rst
+++ b/content/Blog/stable_django.rst
@@ -3,7 +3,7 @@ How good is Django?
 
 :date: 2024-03-31
 :authors: Matt Gibson
-.. :tags: django, dokuwiki, tools
+:tags: django, dokuwiki, tools
 
 .. raw:: HTML
 
diff --git a/content/Blog/time_series_skill_issue_tbh.md b/content/Blog/time_series_skill_issue_tbh.md
index ed06220..da50f81 100644
--- a/content/Blog/time_series_skill_issue_tbh.md
+++ b/content/Blog/time_series_skill_issue_tbh.md
@@ -2,6 +2,7 @@ Title: skill issue tbh: ml time series notes
 Author: Matt Gibson
 Date: 2024-04-18
 Tags: statistics, machine learning, time series
+Modified: 2025-01-07
 
 
 Now and again I see people talking about foundation models for time series data. It's one of those things, like the puzzlement over the inability of deep learning models to outperform traditional models tabular data, that makes me think people don't grasp the generality of of tabular and time series data. Time series and tabular data are much more general than images, images and text data. In my opinion, much of the success of current methods relies on exploiting the structure of the data. The generality of these data type imho precludes finding such structure except in specific, limited cases e.g. speech recognition, weather data etc.
@@ -12,29 +13,32 @@ The M competitions have been very important in ML fore timeseries. Refs:
 - https://en.wikipedia.org/wiki/Makridakis_Competitions
 
 
-Also reference data sets are available here: https://forecastingdata.org/
+Also reference data sets for the competitions M* are [available here](https://forecastingdata.org/).
 
-### trad approaches
+## Approaches
 
-20 Oct 2021
-Do We Really Need Deep Learning Models for
-Time Series Forecasting?
-https://arxiv.org/pdf/2101.02118.pdf
+### usual suspects
 
+- lightgbm https://en.wikipedia.org/wiki/LightGBM
+- xgboost https://en.wikipedia.org/wiki/XGBoost
 
-### deep space models 
+A classical approach for time series modelling in machine learning is Gaussian Processes:
 
-S4: deep statespace models 
-https://srush.github.io/annotated-s4/
+- The canonical reference is [gaussian process book](https://gaussianprocess.org/gpml/chapters/RW.pdf)
+- An interesting application to solar energy is [Grouped Gaussian processes for solar power prediction](https://link.springer.com/article/10.1007/s10994-019-05808-z)
 
+#### frequency methods
 
-about ssm 
-https://huggingface.co/blog/lbourdois/get-on-the-ssm-train
+- wavelets
 
-reddit post about ssm:
-https://old.reddit.com/r/MachineLearning/comments/s5hajb/r_the_annotated_s4_efficiently_modeling_long/
+#### other models - HMMs, ensembles, etc
 
+An interesting and somewhat controversial topic is the "self-tuning" prophet models developed by Facebook researchers Sean Taylor and Benjamin Lentham. 
 
+- [paper](http://lethalletham.com/ForecastingAtScale.pdf)  Forecasting at Scale. THE AMERICAN STATISTICIAN Sean J. Taylor and Benjamin Letham
+- [github](https://github.com/facebook/prophet)
+- EOL for prophet package [blog post](https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138)
+- [a seed of the controversy](https://ryxcommar.com/2021/11/06/zillow-prophet-time-series-and-prices/)
 
 ### neural net approaches
 
@@ -60,51 +64,38 @@ resurrecting recurrent neural networks for long squences
 https://openreview.net/pdf?id=M3Yd3QyRG4
 
 
-### gaussian processes
-
-- gaussian process book: https://gaussianprocess.org/gpml/chapters/RW.pdf
-- Grouped Gaussian processes for solar power prediction https://link.springer.com/article/10.1007/s10994-019-05808-z
-
-### other models - HMMs, ensembles, etc
-
-prophet model 
-THE AMERICAN STATISTICIAN
-Forecasting at Scale
-Sean J. Taylor and Benjamin Letham
-http://lethalletham.com/ForecastingAtScale.pdf
-
-https://github.com/facebook/prophet
-
-https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138
+#### deep space models 
 
+20 Oct 2021
+Do We Really Need Deep Learning Models for
+Time Series Forecasting?
+https://arxiv.org/pdf/2101.02118.pdf
 
-### frequency methods
 
-- wavelets
+S4: deep statespace models 
+https://srush.github.io/annotated-s4/
 
-### usual suspects
 
-- lightgbm https://en.wikipedia.org/wiki/LightGBM
-- xgboost https://en.wikipedia.org/wiki/XGBoost
+about ssm 
+https://huggingface.co/blog/lbourdois/get-on-the-ssm-train
 
-### books
- 
-- Time Series Forecasting in Python  Marco Peixeiro
+reddit post about ssm:
+https://old.reddit.com/r/MachineLearning/comments/s5hajb/r_the_annotated_s4_efficiently_modeling_long/
 
 
-Python for Algorithmic Trading: From Idea to Cloud Deployment Paperback – 24 November 2020
-by Yves Hilpisch (Author)
-https://www.amazon.com.au/Python-Algorithmic-Trading-Cloud-Deployment/dp/149205335X/ref=srd_d_ssims_T2_d_sccl_2_5/356-5070353-2846925?pd_rd_w=Ppmna&content-id=amzn1.sym.18fa5695-611e-408b-9728-5579118370e4&pf_rd_p=18fa5695-611e-408b-9728-5579118370e4&pf_rd_r=040MT1XJP5XQ88HA3Y0A&pd_rd_wg=YwpNE&pd_rd_r=6441e172-f568-4a23-9c98-fc6e284d50ce&pd_rd_i=149205335X&psc=1
 
- Practical Time Series Analysis: Prediction with Statistics and Machine Learning 
-https://www.amazon.com.au/Practical-Time-Analysis-Aileen-Nielsen/dp/1492041653
 
 
-Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning Paperback – 24 November 2022
-by Manu Joseph (Author)
-https://www.amazon.com.au/Modern-Time-Forecasting-Python-industry-ready/dp/1803246804/ref=pd_vtp_h_pd_vtp_h_d_sccl_3/356-5070353-2846925?pd_rd_w=SR6Mg&content-id=amzn1.sym.c3e67ad4-8c3b-4d61-8525-47091874fb48&pf_rd_p=c3e67ad4-8c3b-4d61-8525-47091874fb48&pf_rd_r=SKXN2J4TKC3MC2EMXDS8&pd_rd_wg=OQdXD&pd_rd_r=ecb8ff42-6e9d-4d73-91a2-7910d4fc26ce&pd_rd_i=1803246804&psc=1
+## Reference material
 
+### Books
+Time series arise in so many application the literature about them is enormous, but  the resources here are practically focused.
 
-### classical / statistics stuff 
+- A very useful book for learning basic time series is by Robert Hyndman and co called ["Forecasting: Principles and Practice"](https://otexts.com/fpp3/advanced-reading.html)
+- Any econometrics textbook will have a discussion of time series methods and/or it's cousin, panel data. 
+- [Time Series Forecasting in Python] by Marco Peixeiro
+- [Python for Algorithmic Trading (2020) Yves Hilpisch](https://www.amazon.com.au/Python-Algorithmic-Trading-Cloud-Deployment/dp/149205335X/)
+- [Practical Time Series Analysis: Prediction with Statistics and Machine Learning](https://www.amazon.com.au/Practical-Time-Analysis-Aileen-Nielsen/dp/1492041653)
+- [Modern Time Series Forecasting with Python (2022) Manu Joseph](https://www.amazon.com.au/Modern-Time-Forecasting-Python-industry-ready/dp/1803246804/)
 
-https://otexts.com/fpp3/advanced-reading.html
\ No newline at end of file
+### Libraries
\ No newline at end of file
diff --git a/content/Blog/updating_website_thoughts.md b/content/Blog/updating_website_thoughts.md
index 7f87aca..374f098 100644
--- a/content/Blog/updating_website_thoughts.md
+++ b/content/Blog/updating_website_thoughts.md
@@ -195,7 +195,7 @@ Now you can link inside a document like so:
 
 # Pelican static website pros and cons
 
-[reconsider pros and cons]({filename}/blog/updating_website_thoughts.md#pelican-static-website-pros-and-cons)
+<!-- [reconsider pros and cons]({filename}/blog/updating_website_thoughts.md#pelican-static-website-pros-and-cons) -->
 ```
 or in rst
 ```
@@ -207,7 +207,7 @@ or in rst
     `(source 1) <https://jupyterlite.readthedocs.io/en/stable/reference/architecture.html>`_
 ```
 
-and maybe it's time to [reconsider pros and cons]({filename}/blog/updating_website_thoughts.md#pelican-static-website-pros-and-cons).
+and maybe it's time to reconsider pros and cons.
 
 ## How do I add images to my content? 
 
diff --git a/themes/my-basic/templates/category.html b/themes/my-basic/templates/category.html
index ecb7af8..7ccb479 100644
--- a/themes/my-basic/templates/category.html
+++ b/themes/my-basic/templates/category.html
@@ -1,7 +1,7 @@
-{% extends "content_list.html" %}
+{% extends "index.html" %}
 
 {% block title %}{{ SITENAME|striptags }} - {{ category }} category{% endblock %}
 
 {% block content_title %}
-<h1>{{ category }}</h1>
+<h2>Articles in the {{ category }} category</h2>
 {% endblock %}
\ No newline at end of file