Character format issues in emacs

The command cat -v is very useful to show control characters in a file. For example, if you want to debug unusual speech marks (Unicode used instead of ASCII) this is one way to do it.

Replit versus GitHub Codespaces

Replit lets you build and run applications in the cloud, from within a web browser. GitHub Codespaces replicates VS Code in the cloud / kind of replicating a full dev setup in the cloud.

dataclasses in Python

Classes that hold data - cool, right? But boring to implement. Python dataclasses have the solution. But check too what Pydantic has to offer. They are described in PEP557.

What is Pydantic?

Pydantic is used in a number of Python frameworks and libraries - for example, it is used in Langchain extensively.

Pydantic is a widely used data validation library.  

It makes extensive use of the annotations feature in Python. It is worthwhile to understand them in the context of type hints.

Deployment Toolkit (MDT) Support Removed - Try Windows Autopilot

Microsoft has removed support for its legacy enterprise deployment toolkit known as MDT (Microsoft Deployment Toolkit). 

This means no more updates (including for future versions of Windows) or security patches. 

Microsoft have recommended Windows Autopilot for cloud based deployment, or Configuration Manager operating system deployment (OSD) for on-prem infrastructure requirements, as alternatives.

Autopilot can be used to deploy Windows PCs as well as Hololens 2.

Fans lament that MDT was free and did not force Azure cloud adoption.

What is SASE?

SASE is Secure Access Service Edge, which delivers networking and security through a cloud service.

Analytics Libraries Expect Regularised Data

This is a recurrent theme in quantitative computing. 

Analytics libraries expect clean, regularised data, e.g. time series with no missing values.  Real-life data often has gaps and idiosyncrasies - it needs to cleaned often (to create a golden source) but even then subsequently rejigged based on the consumer need. 

This is akin to the Adapter Design Pattern in programming. In the Adapter you adapt an "interface" to another "interface" - for example, an XML dataset is "adapted" into a JSON dataset for JSON consumers.

Statistical libraries in particular are particularly picky about datasets and consistency, particularly when comparing datasets and trying to find relationships or errors between actual and expected values.