I'm just talking about statistical patterns. I know little about Microsoft patches. I abandoned Windows in 2001, right around the time XP was released, and have never looked back.

Not remotely similar to my experience. Granted I'm writing Rust, and the Rust compiler is *really* picky, so by the time the agent gets something that compiles it's a lot closer to correct than in other languages. Particularly if you know how to use the type system to enforce correctness.

Yes, you have to know how to write tests. A few decades of experience helps a lot. I find I actually spend a lot more time focused on the details of APIs and data structures than the details of tests, though. Getting APIs or data structures wrong will cost you down the road.

Also, I suppose it helps a bit that my work is in cryptography (protocols, not algorithms). The great thing about crypto code is that if you get a single bit wrong, it doesn't work at all. If you screw up the business logic just a little bit, you get completely wrong answers. The terrible thing is that if you get a single bit wrong, it doesn't work at all and gives you no clue where your problem might be.

Of course that's just functional correctness. With cryptography, the really hard part is making sure that the implementation is actually secure. The AI can't help much with that. That requires lots of knowledge and lots of experience.

Deep experience is absolutely required. My antennas are quite good after 40 years.

You really have to know what questions to ask, and what answers not to accept. It also helps to know what kinds of errors the LLM makes. It never outright lies, but it will guess rather than look, so you have to know when and how to push it, and how to manage its context window. When stuff starts falling out of the context window the machine starts guessing, approximating, justifying. Sometimes this means you need to make it spawn a bunch of focused subagents each responsible for a small piece of the problem. There are a lot of techniques to learn to maximize the benefit and minimize the errors.

Yeah, I am definitely not doing UI work.

Speak for yourself. I wrote Android's Keymaster implementation in less than a month, and it was about that size, and then re-wrote most of it in a week when it turned out I'd made some core assumptions that Qualcomm couldn't match in their implementation. It was relatively bug-free for a decade -- even when a third-party security research lab spent a month scrutinizing it. They found a handful of things, but nothing serious. I was amazed, especially since I'd seen the reports they turned in on some other code.

That's just one example. In my nearly 40-year career I've had a half dozen crazy-productive weeks like that, and usually when working on particularly-complex bits. If you haven't had that experience, that's unfortunate. It's not something I could do frequently (or would want to), but it's a glorious feeling when you're that deep in the zone.

I have been an on-and-off user of OpenOffice/LibreOffice for many years, and I have to say I've always found it extremely clunky. By all accounts the OOO codebase is pretty convoluted.
Given how many variations of browser-based office suites now exist, I just don't see the point in starting from the OOO design or code.

In short, Java was invented for a reason, and while it has become a victim of legacy cruft as well, the underlying concept of truly portable apps, with a minimum of fuss to jump from platform to platform, still ought to be the preferable path. The problem is that that true platform neutrality/ambiguity pretty much kills Microsoft in all but a few niches, like gaming, but only because hardware vendors put less effort into drivers for other operating systems.

Yes, Office is still king, although I think that crown is beginning to slip, and it may end up being Excel, with its large list of features, that may last the longest. But it isn't 1990, or even 2000 anymore. Developers have multiple ways of developing portable applications, and while MS may (for the nth time) update or swap out its toolchains, the real question is will developers really care?

A year? I've regularly written that much in a month, and sometimes in a week. And, counter-intuitively, its during those sprints when I'm pumping out thousands of lines per day that I write the code that turns out to be the highest quality, requiring the fewest number of bugfixes later. I think it's because that very high productivity level can only happen when you're really in the zone, with the whole system held in your head. And when you have that full context, you make fewer mistakes, because mistakes mostly derive from not understanding the other pieces your code is interacting with.

Of course, that kind of focus is exhausting, and you can't do it long term.

By focusing on the structure, not the details. The LLM and the compiler and the formatter will get the low-level details right. Your job is to make sure the structure is correct and maintainable, and that the test suites cover all the bases, and then to scan the code for anomalies that make your antennas twitch, then dig into those and start asking questions -- not of product managers and developers, usually, but of the LLM!

But, yeah, it is challenging -- and also strangely addictive. I haven't worked more than 8 hours per day for years, but I find myself working 10+ hours per day on a regular basis, and then pulling out the laptop in bed at 11 PM to check on the last thing I told the AI to do, mostly because it's exhilarating to be able to get so much done, at such high quality, so quickly.

Why would they build a machine which just does what humans do in the field instead of building a stationary machine in a factory which builds 20' long sections which fit on a Semi trailer( 2 sections end to end and 2 sections side by side ) and then just have a standard crane on wheels run the sections out onto pre-installed posts? They are talking about "utility scale" installations so the ground has been leveled and prepped so it's not like the custom layouts needed for root-top distributed solar installations.

It seems short sighted when companies build robots which just mimic what humans do with our limited 2 short arms and 5 digit hands.

LoB

That's a valid point, though right now while they're facing competition from startups the dealerships do provide them with a moat that they want to preserve. If/when the startup threat is gone, the automakers will go back to hating the dealerships.

I think people forget how everyone laughed at Tesla because everyone knew that starting a new car company in the United States was impossible. Now we also have Lucid and Rivian. Maybe someday Aptera will manage to get off the ground. This is a novel situation for American carmakers.

They had to say it that way, because the more accurate statement is that the dealership law unfairly advantages existing automakers. It's not about the dealerships being good or bad, it's about the fact that setting up a dealership network takes a lot of time and money and requiring it is a good way to keep new competition out.

The correct term is "regulatory capture". Private businesses use the power of the state to protect, subsidize or otherwise benefit them and harm competitors and potential competitors. It's extremely common and the more pervasive the regulation is, the more common it is. Red tape and government procedures benefit entrenched players who have built the institutional structures and knowledge to deal with them.

This isn't to say that all regulation is bad... but a lot of it is. There was never any consumer benefit to banning direct sales. All regulations should be thoroughly scrutinized for their effects on the market, direct and indirect.

I thought exactly the opposite. I think Rocky is far too "human". I didn't mind it, though, because a lot of the humor would be lost if Rocky was properly alien.

I think that's necessary. Providing explanations of depth comparable to the book would require a 10-hour movie. Squeezing the story down to feature length requires cutting a lot of exposition. In many books there's a lot of description that can be replaced with visuals, but it's pretty hard to do that with a lot of the science.

I'm sure you'd have said the same about Y2K. It's a good thing that some people have more foresight.

Exactly the same as the approach to the movie version of The Martian.

I think you're describing the models from a year ago. Most of the improvements in capability since then (and the improvements have been really large) are directly due to changes that have the AI model talk to itself to better reason out its response before providing it, and one of the results of that is that most of the time they absolutely can explain why they did what they did. There are exceptions, but they are the exception, not the rule.

It's interesting to compare this with humans. Humans generally can give you an explanation for why they did what they did, but research has demonstrated pretty conclusively that a large majority of the time those explanations are made up after the fact, they're actually post-hoc justifications for decisions that were made in some subconscious process. Researchers have demonstrated that people are just as good at coming up with explanations for decisions they didn't make as for decisions they did! The bottom line is that people can't really provide useful insights on why they did what they did, they're just really good at inventing post-hoc rationales.