I think most reasonable people understand that politicians are fallible and can be shortsighted and make mistakes. This is part of being human and part of life in government which, at it's core, is just a huge risk management agency. That's not to say that honest mistakes don't carry weight - Bush Jr may have truly believed everything he claimed about Iraq prior to invading it, but it was still a fool's errand, built on a scaffold of lies, and one of the biggest wastes of resources and lives in US history. Bush should be excoriated for that mistake, even if it was honest.

The problem is when intent and motive undermine the attempt at honest governance. If a politician has ulterior motives, such as personal enrichment, or to gain political advantage over an opposing person or party, to save face in the light of a mistake, or any number of other human foibles and failings, that's when it becomes a valid point to make.

For the helium reserve, it seems to have been a victim of shortsighted attempts to reduce the deficit. The program had accumulated almost $2 billion to buy and store it, and some people thought it wasn't needed.

When it comes to Trump, many people don't trust his motives because he's shown repeatedly to do and say things that are dishonest or hide the true intent. It seems fair to ask who stands to benefit from this change in helium classification.

Maybe just RTFA would answer your question:

"It was Bielfeldt’s curiosity about the inner workings of inkjet printers that inspired his winning entry, but capturing the action of ink drops being deposited on paper was a challenge, given that the process happens so quickly. The droplets, for instance, moved faster than the camera’s shutter speed. Bielfeldt’s solution was to deconstruct the printer so that the paper moved under a stationary (rather than moving) printhead, and then take the image in a dark room, using a flash to serve as the shutter, thereby freezing the ink drops mid-air."

Yes, but not for you and me. Graphics designers have been using stylus-displays for years and years, because it's great to draw on the thing you are making instead of drawing on a different surface like a Wacom.

But I'm like you, I don't have a use case for a touchscreen and if you touch my notebook screen I'll be angry because I prefer it without fingerprints, thanks. So my first question is: Do they offer this thing with OLED but without touch? I guess not. Pity.

I've been slowly moving towards my dream setup: Solar panels, battery, EV car, island mode capability. This is one thing that'd be a factor in my buying decision. Using your car as a battery or secondary battery is a big plus, and if they built the necessary equipment to run independent of the power grid, it'd come in a neat package.

While power failures are rare where I live, I expect them to become more common with the move to renewable energies. I support that, but it's more fragile than constantly running power stations. So being able to run your home at least for a few hours on your own energy is something that I consider a good idea.

My employer announced that AI costs will be billed back to departments. Token use (almost entirely among developers) was cut in half without affecting productivity, and perhaps even enhancing productivity because they had to think a bit about the best use of what they had instead of trying random things.

You missed the point.

A friend of mine made a tea-timer app for the iPhone. Where you enter what tea you are making and it sets the correct number of minutes. Today, people would ask ChatGPT before they go on the App Store to search for something like that. That's the point. Not what YOU would do, but what the general public does. And for Jane Doe, ChatGPT has replaced a lot of these specialty apps.

Skills are great until they aren't. The problem is exactly what you say: You can literally create a skill by telling the LLM to create a skill. Which is... why the f do I need to make a skill if the AI already knows how to do it?

Properly created skills, that include reference material, specific instructions, and expert knowledge the LLM doesn't have or that's buried somewhere under 10 layers of reddit posts is genuinely useful. But most skills aren't. I'm constantly underwhelmed by hyped-up skills that, when I look at them, turn out to be bland, basic instructions.

Well said. This is a very succinct way of describing the secondary effects for everyone around someone using AI to feel "more productive". Sending people massive emails you didn't even bother to read, creating huge design documents that are just dozens of meaningless pages and offer nothing of tangible value, or asking fellow developers to reviews 50,000 line code changes you didn't write (or read), etc.

I use AI at work for development and I know that the frontier models in a harness like Claude Code are more capable than many AI critics claim but I also see the results they create in a skeptical light. For a prediction chatbot they are surprisingly good at writing software, but also surprisingly bad compared to a decently skilled human. They can generate massive amounts of design documents and code in a very short time, and easily overwhelm humans trying to read, review, verify, and test the changes. The same thing is happening in other kinds work as well.

Drug use is an apt metaphor. The Dr Feelgood of this decade.

It isn't that hard to separate. That's why we developed the cryogenic method in the first place. Hydrogen and fluorine are much harder to keep stored and process.

But again that's backwards. I let my AI coding assistant write tests exactly so that I have a reproducible, deterministic ground truth that I can inspect for logic errors. If you let the AI do the testing directly, it's a toss-up if today it feels like it or it decides that your desired result is "everything green" so it'll just give you that answer without actually testing anything.

LLMs obviously aren't human. But that's not the point. The point is that you don't need to know a programming language to program a computer.

For example: Especially in the early days of smartphones, there were mountains of small tools that did something that's essentially a simple script. Egg timers, remind-me-tomorrow apps, tons of stuff that's basically a wrapper around a web search, and so on. Almost all of those are today something ChatGPT can do, and are easy enough to describe.

I've actually outsourced the first iteration of that to AI. :-)

At the end of coding sessions, I generall ask it to spin up an adversial agent whose task it is to find flaws, inconsistencies, mistakes, bugs, etc.

I'd still strongly recommend to anyone shipping AI-generated code to have a thorough human code review, but for a first pass at surface level, the AI can check itself if it has some kind of separation between the generator and the reviewer.

Personally, after some experimenting, I've concluded that:

• AI is good at implementing known algorithms.
• AI is good in creating code that flows straightforward from a design document.
• AI is good at code reviews and finding bugs.
• AI (unlike me) doesn't mind writing a few hundred tests.
• But, it tends to over-engineering simple tasks to the point of "why does Hello World need enterprise-software levels of abstraction?"
• It struggles with code that doesn't have a known solution.
• It's difficult to get it into a different lane once it started going down the wrong way.

What I use it for these days is rapid prototyping. Using AI I can make a functioning prototype of an idea I have in a few days instead of a few weeks. I can quickly test out an idea for a feature or change in my existing codebase.

What I also have been using it for a while is as a code-reviewer and for someone like me who is mostly working solo, that's an insanely valuable thing. Not just the bugs themselves, but also the ability to discuss code with someone. Basically, AI is a rubber ducky that replies.

Mickeysoft lost the plot again.

The game-changing element of LLMs and agents is that they have a non-computer-language interface - you can talk to them in plain English (or Chinese or whatever). This makes complex computer interactions available to a much wider audience than any previous computing invention. Adapting humans to the AI is getting that backwards. It's a sign you don't understand the first thing about why AI is so widely successful.

I predict this will be an abject failure and a year from now will quietly be pushed to the very back of the bottom-most drawer, never to be spoken off again (though it is getting crowded in there for M$).

Meanwhile, in the real world, everyone I've talked to about these things already has a skills file to teach the AI about their personal coding style. This is what actual humans came up with, independently several times, and it works: Adapt the AI to you. Plus we've been writing coding style guides for humans for a long time now, we've got plenty of experience doing that at least somewhat right.