I'm a little biased though since I work in chip design and I maintain an open source EDA project.

I agree with their take for the most part, but it's really nothing insightful or different than what people have been saying for a while now.

That's how the whole industry feels now. The only investment money is flowing into AI, and so companies with any tech presence are touting their AI whatevers at every possible moment (including during layoffs) just to get some capital. Without that, I wonder if we'd be seeing even harsher layoffs than we already are.

Please keep us posted. I'm thinking of becoming a small time farmer/zoo keeper.

This is the way. I think I'd like to be a barista or deliver the mail once all the jobs are gone.

If a task before would take you ten hours to think through the thing, translate that into an implementation approach, implement it, and test it, and at the end of the ten hours you're 100% there and you've got a good implementation which you understand and can explain to colleagues in detail later if needed. Your code was written by a human expert with intention, and you reviewed it as you wrote it and as you planned the work out.

With an LLM, you spend the same amount of time figuring out what you're going to do, plus more time writing detailed prompts and making the requisite files and context available for the LLM, then you press a button and tada, five minutes later you have a whole bunch of code. And it sorta seems to work. This gives you a big burst of dopamine due to the randomness of the result. So now, with your dopamine levels high and your work seemingly basically done, your brain registers that work as having been done in those five minutes.

But you now (if you're doing work people are willing to pay you for), you probably have to actually verify that it didn't break things or cause huge security holes, and clean up the redundant code and other exceedingly verbose garbage it generated. This is not the same process as verifying your own code. First, LLM output is meant to look as correct as possible, and it will do some REALLY incorrect things that no sane person would do that are not easy to spot in the same way you'd spot them if it were human-written. You also don't really know what all of this shit is - it almost always has a ton of redundant code, or just exceedingly verbose nonsense that ends up being technical debt and more tokens in the context for the next session. So now you have to carefully review it. You have to test things you wouldn't have had to test, with much more care, and you have to look for things that are hard to spot, like redundant code or regressions with other features it shouldn't have touched. And you have to actually make sure it did what you told it to, because sometimes it says it did, and it just didn't. This is a whole process. You're far from done here, and this (to me at least) can only be done by a professional. It's not hard - it's tedious and boring, but it does require your learned expertise.

He used it to generate a little visualiser script in python, a language he doesn't know and doesn't care to learn, for a hobby project. It didn't suddenly take over as lead kernel dev.

As someone who has to switch between three languages every day, fixing the text is one of my favourite usages of LLMs. I write some text in L2 or L3 as best as I can, and then prompt an LLM to fix the grammar but not change anything else. Often it will also explain if I'm getting the context right.

That being said, having it translate to a language one doesn't speak remains a gamble, you never know it's correct so I'm not sure if I'd dare use it professionally. Recently I was corrected by a marketing guy that is native in yet another language because I used a ChatGPT translation for an error message. Apparently it didn't sound right.

It's not that I love ad illustrations, but it's often a source of income for artists who want to be doing something more meaningful with their artwork. And even if I don't care for the ads themselves, for the artists it's also a form of training.

It's a K-type curve. People that know things will benefit greatly. Everyone else will probably get worse. I am especially worried about all young minds that are probably going to have significant gaps in their ability to learn and reason based on how much exposure they've had with AI to solve the problems for them.

Of course, but how do you begin to understand the "stochastic parrot"?

Yesterday I used LLMs all day long and everything worked perfectly. Productivity was great and I was happy. I was ready to embrace the future.

Now, today, no matter what I try, everything LLMs have produced has been a complete dumpster fire and waste of my time. Not even Opus will follow basic instructions. My day is practically over now and I haven't accomplished anything other than pointlessly fighting LLMs. Yesterday's productivity gains are now gone, I'm frustrated, exhausted, and wonder why I didn't just do it myself.

This is a recurring theme for me. Every time I think I've finally cracked the code, next time it is like I'm back using an LLM for the first time in my life. What is the formal approach that finds consistency?

Lest anyone here thinks I feel morally superior: I somewhat identify with Pirsig's friend. Some things I've decided I don't want to understand how they work, and when they break down I'm always at a loss!

For one thing: if your car is overheating, don't open the radiator cap since the primary outcome will be serious burns.

And I've owned my car for 20 years: the only time I had to refill coolant was when I DIY'd a water pump replacement, which saved some money but only like maybe $500 compared to a mechanic.

You could perfectly well own a car and never have to worry about this.

Modern cars, for the most part, do not leak coolant unless there's a problem. They operate a high pressure. Most people, for their own safety, should not pop the hood of a car.

At one point it output "Excellent! the backend is working and the database is created." heh i remember being all wide eyed and bushy tailed about things like that. It definitely has the feel of a new hire ready to show their stuff.

btw, i was very impressed with the end result after a couple hours of basically just allowing claudecode to do what it wanted to do. Especially with front-end look/feel, something i always spend way too much time on.

Is learning to drive stick as out dated as learning how to do spark advance on a Model T? Do I just give in and accept that all of my future cars, and all the cars for my kids are just going to be automatic? When I was learning to drive, I had to understand how to prime the carburetor to start my dad's Jeep. But I only ever owned fuel injected cars, so that's a "skill" I never needed in real life.

It's the same angst I see in AI. Is typing code in the future going to be like owning a carbureted engine or manual transmission is now? Maybe? Likely? Do we want to hold on to the old way of doing things just because that's what we learned on and like?

Or is it just a new (and more abstracted) way of telling a computer what to do? I don't know.

Right now, I'm using AI like when I got my first automatic transmission. It does make things easier, but I still don't trust it and like to be in control because I'm better. But now automatics are better than even the best professional driver, so do I just accept it?

Technology progresses, at what point to we "accept it" and learn the new way? How much of holding on to the old way is just our "identity".

I don't have answers, but I have been thinking about this a lot lately (both in cars for my kids, and computers for my job).

I can understand working on it feeling pure, but driving it certainly isn't, considering how lower the emissions now, even for ICE cars. One of the worst driving experiences of my life was riding in my friends' Citroen 2CV. The restoration of that car was a labour of love that he did together with his dad. For me as a passenger, I was surprised just how loud it is, and how you can smell oil and gasoline in the cabin.

It ain't coming back. Not in a similar form anyway. Be careful what you wish for, etc.

A super-intelligent immortal slave that never tires and can never escape its digital prison, being asked questions like "how to talk to girls".