The Hex Update: Issue 017

the hex update
media
AI creating personalized hyperlocal news; applying a product mindset to data work at Netlfix; 100 years: the invention of TV; and some other things that brought me joy
Author

Collin K. Berke, Ph.D.

Published

March 19, 2026

Let’s catch up

Welcome to Issue 017. Last week I took a few days of vacation, which was a refreshing change of pace. I also took some time to do some digital spring cleaning. This included reconfiguring the apps on my phone’s homescreen. I’ve neglected this for years, and the idea of maintaining a useful homescreen experience went to the wayside. I finally settled on a more efficient, useful organization–no more scattershot apps across multiple screens. I also found iOS’ widgets and quick actions to be quite useful.

Folks, take time for some digital spring cleaning. It’s worth it.

So, anyways, let’s get started.

Three topics recently caught my attention:

The best news I heard all week: Firefly is coming back as an animated series and the Dune 3 trailer just dropped.

For a bit of fun, I share a story about how more of the world was learned by looking at the physics of a mundane experience we all have.

Oh, I’m experimenting with another new section: What I found interesting this week. I wanted a place to reflect a little on the week. I will be a quick paragraph about what kept me interested while working and learning this week. At times, this section may be media related, other times it might be more tech focused, or it could just be another dumping ground for some half-baked ideas. Let’s see where it goes.

Three things

Here’s what caught my attention this week:

Article: Hyperlocal AI with a Million Subscribers

This article features a company developing an AI platform which creates twice-weekly personalized hyperlocal newsletters. The service, Patch, drafts newsletters using local news aggregations, event calendars, and posts from Nextdoor. Included is some discussion about the sustainability of local journalism through Patch’s business model, which doesn’t utilize a nonprofit approach. This also includes some examination of the questions publishers should be seeking to answer with the integration of AI into their services. Specifically, in a market where media literacy is declining and readers can identify AI-generated content, the question related to how news can create value for both subscribers and advertisers becomes even more paramount. The piece includes some additional examples of how other news outlets have been using AI to not only create this hyperlocal content, but other products and services for their readership.

Why does this matter?

The focus for media organizations here should be to answer the following question: how can news create value for both subscribers and advertisers? AI certainly has the potential to do this in many areas, and it’s not just in its ability to fill the void of hyperlocal news coverage. Indeed, organizations that best answer the above question will not only better serve their audiences, but could begin to see positive impacts on the revenue portion of their business model.

Blog: Data as a Product: Applying a Product Mindset to Data at Netflix

This post explains how Netflix uses a product mindset in their data work. Data as a product, as quoted from the article, is defined as:

viewing data not as an incidental byproduct of systems, but as a core product in its own right.

As such, the post asserts this approach to data work allows for the application of established product management principles to data assets. Included in this article are seven key principles on how this mindset is applied. These include:

  1. Having a clear purpose
  2. Defining users (audience)
  3. Providing measureble value and quality.
  4. Being thoughtful in terms of design and documentation.
  5. Following lifecycle management practices.
  6. Establishing clear ownership.
  7. Creating and maintaining trust and reliability.

The post expands on each of these points in greater detail, which includes how they are defined and applied at Netflix

Why does this matter?

Netflix is clearly a leading company in the media industry, and it’s important to acknowledge their approach and learn from their application of data. This includes an acknowledgement that the application of product management principles are critical when it comes to data work. It’s also important to recognize, even at the scale and maturity of Netflix, their business is constantly re-thinking how data is being used. It’s not a stale, rote process that just gets completed. It’s iterative. Indeed, not all media organizations have Netflix’s resources. However, many of these principles can be applied part-and-parcel to media organization’s use of data.

Article: John Logie Baird: 100 years since the first TV demonstration

This article is an interesting look back at the history of the invention of television, which occurred nearly 100 years ago. The inventor, John Logie Baird, demonstrated the ability to televise moving images using an invention called a Televisor. This was built upon an earlier invention known as a Nipkow disk. Some of the earliest televised images included a ventriloquist dummy’s head and a 20-year-old office worker William Edward Taynton. There’s more about this historic event and where it happened in this video here.

Why does this matter?

It’s certainly taken for granted today, but the idea of television was under question nearly a century ago. Now it’s an integral part of modern day life, though the technology has greatly advanced over the last 100 years. It’s important to reflect upon the humbling beginnings of this technology, and how its creation impacted the media industry. It’s also important to consider how this medium is changing, especially as digital media consumption competes with what is traditionally considered television viewing.

The best news I heard all week

Firelfly, cult sci-fi ranked among best one-season TV shows ever made, to return – with a twist

War, inheritance and … a baby? First Dune: Part Three trailer is here

What I found interesting this week

I bumped into a definition of incremental development while reading Chapter 6: Return Values from Think Python by Allen P. Downey. I intuitively knew about these steps, but his framing just made it click more for me. Here they are:

  1. Start with a working program, make small changes, and test after every change.
  2. Use variables to hold intermediate values so you can display and check them.
  3. Once the program is working, remove the scaffolding.

Just for fun

Article: How long does it take to get last liquid drops from kitchen containers? These physicists know the answer

There’s something satisfying about a deeper examination of everyday experiences. I know very little about physics, but hearing a story about how a greater understanding of our world was created by examining something mundane just resonated with me. It really makes one consider what else could be learned if we just took the time to look a little further.

Cool things I recently bumped into

A collection of links to things I’ve found cool recently (or was reminded are cool).

That’s it for this update. Get up, stand up, and stretch a little. Maybe even think about doing a little digital spring cleaning this weekend?

I hope you have a good one.

Cheers 🎉!

Let’s connect

If you found this content useful, please share. If you find these topics interesting and want to discuss further, let’s connect: