You also have to work 2-3 positions at the same time, without the required equipment to accurately monitor the location of planes, with faulty alarms going off multiple times per day, and you get blamed if you lose concentration for a second at the wrong moment.

I’ve found apexcharts-card to be fairly configurable and good looking. I’ve put 2 different data types on the one graph and used two axes (price left, % right). Sometimes the values get “stuck”, but a refresh fixes it.

type: custom:apexcharts-card
apex_config:
  legend:
    show: false
graph_span: 12h
span:
  start: minute
yaxis:
  - id: price
    min: ~-10
    max: ~40
    decimals: 0
  - id: renewables
    opposite: true
    min: 0
    max: ~100
    decimals: 0
header:
  show: true
  title: Amber Prices
  show_states: true
  colorize_states: true
series:
  - entity: sensor.amber_general_forecast
    name: General Forecast
    unit: c/kWh
    color: "#3498DB"
    yaxis_id: price
    data_generator: >
      const data = [];

      data.push([hass.states['sensor.amber_general_price'].attributes.nem_date.replace(/0{2}$/,
      "30"), hass.states['sensor.amber_general_price'].attributes.per_kwh*100]);

      for(let i = 0; i <= 24; i++) {
        data.push([entity.attributes.forecasts[i].nem_date.replace(/0{2}$/, "30"), entity.attributes.forecasts[i].per_kwh*100])
      }

      return data.reverse();
  - entity: sensor.amber_feed_in_forecast
    name: Feed In Forecast
    unit: c/kWh
    color: "#ff9800"
    yaxis_id: price
    data_generator: >
      const data = [];

      data.push([hass.states['sensor.amber_feed_in_price'].attributes.nem_date.replace(/0{2}$/,
      "30"), hass.states['sensor.amber_feed_in_price'].attributes.per_kwh*100]);

      for(let i = 0; i <= 24; i++) {
        data.push([entity.attributes.forecasts[i].nem_date.replace(/0{2}$/, "30"), entity.attributes.forecasts[i].per_kwh*100])
      }

      return data.reverse();
  - entity: sensor.amber_feed_in_forecast
    name: Renewables
    yaxis_id: renewables
    unit: "%"
    color: "#2ECC71"
    data_generator: >
      const data = [];

I upgraded from a Pi2 to a second hand thinkpad. It went from underpowered to also running a NAS and a factorio server with plenty of precessing power to spare. I’ve used it for several other projects as needed. Docker compose makes everything trivial.

The holocaust was legal, hiding Jews to keep them alive was illegal.

Following the law does not guarantee you’re doing the right thing.

The square app will not run on a phone that has developer mode enabled. I turned developer mode on to disable annoying animations, so now I can’t take card payments unless I carry around a second phone.

If Google goes through with this, my payment phone won’t be able to run any third party apps.

Australia almost did this right with the National Broadband Network.

Unfortunately, it was then sabotaged by the government after an election changed the majority party.

It seems to be back on track after wasting an extraordinary amount of time and money by installing copper lines, just to completely replace them with the fiber lines that were in the original plan.

I miss the comfort of having a single OS (not multiple distros to choose from), and a father who would reinstall the OS when I broke it too much again.
I miss the Macromedia Flash games, bringing games to school on a floppy disk labeled “homework” (then discovering I’d only brought the shortcut).

All of this is just nostalgia, and while I miss it, I’m happier now with my Linux Distro.
I’ve customised the desktop environment, broken the OS and reinstalled it myself (several times), and copied games to another device while forgetting to copy the folder containing my save files.
I guess some things stay the same.

I really wanted GrapheneOS, but ended up with /e/OS on a fairphone because I refuse to give Google any more money.

After replacing the default launcher with Lawnchair, I’m pretty happy with it. The physical switch (remapped to control the torch) is a nice touch.

Are they going to sue to operating system owners next? What about the web browser that offers the steam installer download?

It sounds like a faulty device to me. I recommend reaching out to customer support at a minimum.

I didn’t know you could use different launchers until a few weeks ago. I’m so much happier with Lawnchair.

Yes, microG is on /e/OS by default. I believe my bank uses some of the security features from Play Services that aren’t in microG.

I’m not 100% sure that it can’t be fixed, but it’s such a minor nuisance that I don’t think it’s worth my time to investigate.

Fairphone 6

My bank app will install, but I can’t login unless I also install Google Play Services. The website works fine without.

No issues with other apps. I do prioritise installing from F-Droid whenever possible.

I can’t say for GrapheneOS, but NFC still works on my Fairphone 6 with /e/OS without play services.
I can’t use NFC for payments, but I also can’t log in to my bank’s app without play services.

I think the most important part of programming that you mentioned and that executives don’t see is that 99% of programming is thinking, and only 1% is typing.

If you understand the codebase, anything you write should work well with it. If you don’t, your code snippet may have side effects that break a different section of the codebase. The majority of the work programmers are paid for is to learn the codebase and properly maintain it.

AI doesn’t maintain, and it doesn’t have the ability to understand. It can only predict what other people have written in a similar situation.
If you wouldn’t write a message to another business executive by repeatedly pressing the next predicted word then blinding pressing send, then you shouldn’t be using AI either.

If it really is a simple one off project in a language I’ll never use again, I’ll use AI.

The downside is that if I didn’t use AI, then I would have learnt the basics of the language and would be able to do the next project much faster. Now, if I want to do another project in the same language, I’m starting from the beginning.

It depends entirely on how much you value learning.

AI is mile wide, skin deep.
It will tell you many things about a wide variety of topics, but it can only provide answers that appear correct on the surface.

Another analogy would be asking someone to multiply two 3 digit numbers in their head and write the answer in less than five seconds. Most people can guess that the answer will have 6 digits, and most people can write a random 6 digit number. Very few people are capable of checking if the given answer is correct.
An AI will give you the equivalent of a 6 digit number. If you don’t know the answer, it looks impressive. It’s only when you are capable of finding the answer for yourself that you realise the AI is usually wrong.

LLMs are made to be really good at language. They are also made to be confident. They will always give well written answers with the highest confidence.
If you want to rephrase an email or improve a resume, an LLM can give valuable feedback on various snippets. That doesn’t mean it’s always right, and it doesn’t mean you should always throw away what you have in favour of the LLM output.

One of the biggest downsides I’ve personally experienced (and you’ve made reference to) is gradually falling out of the practice of thinking. Thinking is a skill that takes constant practice, and it’s really easy to get into the habit of relying on an AI instead of thinking. In less time than you’d expect, you’re out of practice and unable to do simple tasks that you used to do easily.
This wouldn’t be a big deal if AI worked all the time, but in and case where it can’t give an answer, you can no-longer fill in the blanks.

In programming you have 3 tiers of errors: compiler errors, runtime errors, and logic errors.
The easiest is compiler errors - the compiler can often tell you exactly how to fix it. Runtime errors are harder to identify, but an AI can help to resolve them.
The hardest is logic errors. These do not crash the program, and do not notify you of their existence. And AI will not usually notice these errors.

When programming yourself, you often think of all the ways you could solve the task, and the act of thinking often brings edge cases and logic issues to mind. When asking an AI to do the work, the AI does not think and the prompter does not think, so no-one preempts any logic errors. This is already leading to massive amounts of technical debt, the extent of which is yet to be fully realised. One only has to look at recent Windows 11 bugs to see how quality is reduced and debugging time is increased whenever AI is used.

Writing code is 5% of the time/cost, and maintaining code is the other 95%. AI can reduce the writing time, but drastically increases maintenance costs as a side effect. If you want to run a business for any reasonable period of time, you want the exact opposite.

The use of AI actively de-skills workers, increases subtle mistakes, reduces proofreading and error checking, and makes the company reliant on a costly external tool that could change or disappear at any moment.

I once wrote a 2 line, 10 word script that had 9 bugs in it. I’m not overly proud of that one.

Those lazy immigrants, sitting at home doing nothing, taking all our welfare and jobs.

You can’t get a refund if you’ve been paying a subscription for a missing product.