iOS 14 Smart Stack

For me, the best new iOS 14 feature is the Smart Stack widget. Widgets in general are neat, but I’m hoping the Smart Stack technology will be implemented in other parts of iOS as well.

If you’re not aware, Smart Stack is basically a way for apps to report when they have something interesting to report. In the example below, it had just started raining in Madison, so the weather widget was surfaced.

The obvious extension of this is for Watch complications. Having a temperature complication that switched to a weather conditions complication to warn you about pending inclement weather is a simple example that would make the Watch more useful and easier to use.

Switching to Calendar when you have a meeting coming up, Reminders when you have a task due, or Stocks when the stock market would be very convenient.

A Smart Stack on the lock screen would also be interesting. While you can already put widgets in the dashboard to the left of the lock screen, a single, customizable Smart Stack on the lock screen itself would be able to quickly surface similarly important information about weather, an important meeting, breaking news, and so on.

Fingers crossed for iOS 15. 🤞

Spying TVs are getting cheaper

But the most interesting and telling reason for why TVs are now so cheap is because TV manufacturers have found a new revenue stream: advertising. If you buy a new TV today, you’re most likely buying a “smart” TV with software from either the manufacturer itself or a third-party company like Roku.

Noah Kulwin in The Outline

It is so creepy when Roku TVs show a message to “continue watching from the beginning” when you’re watching something on an Apple TV. I assume the TV is constantly sending frames of whatever is on screen to Roku servers for analyzing. It seems unlikely that the TV is capable of doing this recognition on its own.

The first time this happened, I finally broke down and bought a Raspberry Pi so I could set up Pi-hole.

I can’t believe this spying is not a huge story.

One Month of AirPods Pro

The initial reviews of the AirPods Pro were incredible, if not a little hard to believe.

It’s true that the noise cancellation is very good though. I work in cafes regularly and still find it sort of incredible how good they are at cancelling out background noise. If someone is having a loud conversation right next to you, you can kind of hear it if the volume is low enough. The background buzz of people talking is completely gone though.

They also, predictably, stay in my ears much more reliably than the previous AirPods. If you get them, definitely try all the tips. I used the medium ones for two weeks and they were fine, but the smaller ones fit even better.

Transparency mode is the killer feature I feel like nobody is really talking about — it’s audio AR. Paired with a future pair of AR glasses and maybe a watch, you can start to see the path to making smartphones obsolete.

The only (small) problem I have so far is that transparency mode is unusable with any kind of hat that covers your ears, which means I won’t be using it very much for the next few months.

Overall, I find the AirPods Pro very exciting.

Pi-hole

I’ve been running Pi-hole, the “black hole for Internet advertisements” for a while now. It started out as an excuse to get a Raspberry Pi, but I consider this a somewhat important security appliance now. One of the nice things about Pi-hole is, like Ghostery, it’s easy to see what’s being blocked. Unlike Ghostery, though, it works for the entire network. So things like the Xbox and smart TV are included. For example, I noticed that TCL TVs track what you’re watching even if you “Limit ad tracking”.

I also run Unbound on the Raspberry Pi, which forwards to Cloudflare and OpenDNS over an encrypted connection. The Docker configuration I use for Unbound is on Github.

Biased Algorithms

Biased algorithms and their effects are something I’ve been interested in exploring recently. It’s not a problem with Mathematics or Computer Science per se — humans with implicit bias come to false conclusions all the time. We’re the source of these problematic algorithms after all. The problem is that these bad assumptions can be deployed on a massive scale and aren’t questioned because we think of the math as infallible.

A recent episode of 99% Invisible, The Age of the Algorithm, discusses this topic and gives some examples of where it is having real, negative effects today.

Most recidivism algorithms look at a few types of data — including a person’s record of arrests and convictions and their responses to a questionnaire — then they generate a score. But the questions, about things like whether one grew up in a high-crime neighborhood or have a family member in prison, are in many cases “basically proxies for race and class,” explains O’Neil.

Essentially, any time you use historical data that was effected by a bias to influence the future, you risk perpetuating that bias.

If you’re interested, Cathy O’Neil also wrote a book called Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.