It gave me the numbers for the various trials. I don't have them handy and I didn't run them manually - so the AI could totally have made it all up. I did not keep the numbers so I have nothing to share.

Sure - it's just a hypothesis.

I do not recall the details! But I did watch as the damn AI did repeat wall clock and sample tests to see where the time was going and try 2-4 different approaches and clocked each one in detail. I felt embarrassed because I *could* do all that and certainly had done it in the past - but it would have taken me a *long* time to set up each tests and analyze the results and try over and over and track the results for each.
As you imply - none of this is particularly tricky - it's mostly just labor intensive and time consuming. And those are both things that AI has no trouble with.

Related - when I tell an agent to optimize for performance - especially if the request is well targeted - it does an *amazing* job. Doing performance work is so well defined and described that it can follow the patterns and do all the checks and get it really right. To do a good job it's really repetitive and there are often a lot of angles to check and .. it just does it really well.

This is like Trump promising $5K if republicans win the midterms. It's just a PR stunt.

Imagine trying to reason out the tax implications of something like this. The privacy implications. Everything.

Elon: "I really wanted to, but lawyers wouldn't let me!"

No way to see that coming.

I predict it could cost as much as $150 Billion!

Do I hear $200B? We're making shit up - go large!

My day job is a (saas) app that's about 20 years old and has been worked on continuously by a team that has varied in size from about 5 to now about 15. It is complicated and connects to several external services. There are parts of the app that are too hairy for AI to reason about. But we have found, recently, that AI is surprisingly good at explaining how some rather complicated (but well written) parts of the app work.
The 2-3 really hairy parts of the app are written in an arbitrarily complicated way that I would never have used and long to rewrite - but they work. Nobody on the team is happy with it - and there is no corporate memory of anyone being happy working with these parts.
We have exceptional test coverage (>95% by lines), so it's just a matter of time before we decide to lay out an AI agent team to rewrite some of the horrible stuff in a way that is much more palatable to both human and agent. This is not something I would have suggested as possible 4-6 months ago.

I'm not sure how to answer that question. My flip answer is "the usual way." But nothing about software is the usual anymore.

100% of the code that .. makes up this project is code that I did not write. But I absolutely told AI what I wanted and in many cases *how* I wanted it. And any code that had "interesting" design elements is code that I reviewed and in some cases told AI to change. Where interesting refers to meaningful architectural decisions.

At this point it is a very light project - somewhat less that 10K lines of code. It is in its minimal viable product phase - and I imagine it will grow to be more than 15 or even 20K LOC. Eventually becoming projects on 3 distinct platforms and becoming substantially larger.

All of which is ridiculous. Fewer than 200 prompts so far, I'm sure. Maybe fewer than 100.

I've got a black swan for ya!
I just shipped software that AI wrote [practically] entirely that I would not have shipped otherwise. I don't think I'm the only one.

Pfft. Morons. It is *super easy* to make predictions.

Accurate ones, however...

Yes, because users understand those kinds of choices.

Too soon!

Way more than cheaper rides.
* App to summon
* Automatic driver mapping to pickup
* Predictable arrival for pickup
* Predictable arrival for drop off
* No BS routing and you knew the cost before pickup/dropoff.

Back in the day I called for cabs ahead of time and had them fail to show up 25% of the time. I've never lived in a dangerous area, so that wasn't it. They just were not reliable or predictable. Uber saw a huge opening for a service and they took it. They also saw a huge opportunity for destroying the existing market solution in an anti-competitive and illegal way and took that, too - banking on the notion that government would not act fast enough to stop them. And they were right.

And because cars are a certain size. Making it half the size for just one person would be nearly as expensive and would be worse for taxi'ing 2 people (which are still a reasonable number of fares).

Account closed.
I created my twitter account when the service was practically new and only used it a handful of times - so I might not be the target audience. Even so, I miss the original idea of it.

Oh, no. I have it on good authority that computer vision is about to exceed human performance.

Wait - what year is it, again? :-p

Imagine 10,000 clusters of 20 homes. Each cluster no closer than 5 miles to.. anything. And all of them behind some mountain or in some valley or somewhere else that's really inconvenient. AI tells me you can run copper 5 miles without a booster - so that's what they did - sometime last century when they ran power. Work was cheap, copper was cheap, and someone was running power so they piggybacked. Easy peasy.

You can't run modern stuff nearly that far - except for fiber. But then you're running fiber to 10,000 hubs and each will probably need some kind of a station to do processing and splitting and further wires. I don't suppose any of this is hard - it's just expensive. And for each of these 10,000 hubs you're going to do all this work for 20 customers that only want to pay $10/month because that's their deal. You will *never* recoup those costs.

So I get why they don't wanna. But that's the deal they get for monopoly power and right of way and all that.