Reflecting on deeper work

At the end of the day the weekend will be here. This writing endeavor got off to a slow start today. I’m still feeling rather sleepy for some reason. Even two shots of espresso from my trusty Nespresso machine have not kickstarted the day. Right now I’m using Google Keep to collect notes on things that need more coverage or I should go back and review. My new strategy is to flip the Google Keep cards to green after they have been revisited as part of this grand daily writing project. That helps me at least have some record/memory of what was included and what was not included. One of the drawbacks of writing this early in the morning is that the things that come to mind are not always on the forefront of my thoughts later in the day. When you get into the pattern of blocking and tackling the tasks that come in throughout the day it is much harder to think strategically and deeply about the form, function, structure, and assumptions (FFSA) of complex things. I know that you have to make the time for that type of analysis and review at various points in the day, but sometimes the stream of things happening prevents that type of deeper work. 

This weekend I should have a few blocks of time in the morning to really focus in and work on some things to move them forward. One of the things that has to receive some attention is the week 15 edition of The Lindahl Letter for Substack publication. My efforts to stay ahead of publication have fallen behind and the letter is about to go real time as of Saturday. That has good and bad points. It would probably be in my best interest to work ahead a little bit this weekend and try to draft content for the next 5 weeks, but that might not happen. I just might end up working real time and refining the post each work as it is being written. Part of this 52 week writing effort is to really focus deeply on machine learning and dig into practical applications. That is part of what being a pracademic is all about. In this case it would be about studying applied machine learning in the wild and in academic journals. A responsible academic working from a pracademic mindset would probably translate that into publications. In my case, I’m gearing up to do that as an outcome of the year long research and writing effort. I’m viewing it as a project designed to gain and share knowledge along the way and ultimately refine that into some type of academic article or articles.

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