Well, apparently they give you some miniscule rewards points for meeting step or activity goals. I told my wife who was immediately intrigued. Pump out more apps that appeal to women.
This does not check out for me intuitively. You are telling me Dick's sporting goods with 800 stores was able to get to the top of the app store leaderboard, which companies spend a lot of effort and money on, against the largest AI behemoths, by giving rewards on activity goals? I think it has to be something else.
United healthcare does that too and it’s great because it’s just straight cash on a visa gift card. Like 1.25 a week for just doing your normal stuff and more for hitting other goals. Easy to rig too…
Oh come on, that is complete nonsense. I can reunderstand complicated code I wrote a year ago far, far faster than complicated code someone else wrote. Especially if I also wrote tests, accompanying notes, and docs. If you can't understand your old code when you come back to it... including looking through your comments and docs and tests... I'm going to say you're doing it wrong. Maybe it takes a while, but it shouldn't be that hard.
Anyone pretending gen-ai code is understood as well as pre-gen-ai, handwritten code is totally kidding themselves.
Now, whether the trade off is still worth it is debatable, but that's a different question.
The trade-off is worth it in my opinion when you are in a time crunch to deliver a demo, or are asked to test out an idea for a new feature (also in a time crunch).
The hope being that if the feature were to be kept or the demo fleshed out, developers would need to shape and refactor the project as per newly discovered requirements, or start from scratch having hopefully learnt from the agentic rush.
To me, it always boils down to LLMs being probabilistic models which can do more of the same that has been done thousands of times, but also exhibit emergent reasoning-like properties that allow them to combine patterns sometimes. It's not actual reasoning, it's a facsimile of reasoning. The bigger the models, the better the RLHF and fine-tuning, the more useful they become but my intuition is that they'll always (LLMs) asymptotically try to approach actual reasoning without being able to get there.
So the notion of no-human-brain-in-the-loop programming is to me, a fool's errand. I do obviously hope I am right here, but we'll see. Ultimately you need accountability and for accountability you need human understanding. Trying to move fast without waiting for comprehension to catch up (which would most likely result in alternate, better approaches to solving the problem at hand) increases entropy and pushes problems further down the road.
I think the worse situation is the bad AI summaries from search on health issues.
We had a potential pet poisoning, so was naturally searching for resources. Google had a summary with a "dose of concern" that was an order of magnitude off. Someone could have read that and thought all was fine and had a dead cat.
(BTW cat is fine, turned out to be a false alarm, but public service announcement: cats are alergic to aspirin and peptobismal has aspirin. don't leave demented plastic chewing cats around those bottles, in case you too have a lovely but demented cat)
What's really worrying is seeing medical professionals starting to rely on these tools.
My wife had a pretty bad cold during pregnancy and our GP proceeded to prescribe her cough syrup with high alcohol content, because that was what ChatGPT told him to prescribe. We only noticed it once she took the first dose and spit it out again...
I have literally never seen a correct google summary. Maybe y'all are searching for different things than i am, but at this point I've started taking the viewpoint that if I don't know why the ai summary is wrong, then i also don't know enough about the topic to trust its summary enough to determine whether the summary is useful.
The thing that makes me crazy about this article, others like it (and so many of the comments in response to them ) is the naive assumption that whatever the author is used to is always the case.
It's just not. I've been a developer for 25+ years, and in technical diligence looking at companies getting investments for seven, and have done diligence on 100+ companies. It's ALL OVER THE PLACE. It depends on the product, the company, the team, the way the company is managed, the business domain, etc.
For some companies, products, and teams.. the code absolutely is the hard part, and building that insanely sophisticated software is worth millions. I've seen scientific software that sold for millions of dollars per copy.
For others, the code is essentially commodity plumbing around some operational knowledge of a domain that makes nice dough with minimal code complexity. I've also seen founders who were going to spend the rest of their days floating around the bahamas because they wrote the right PHP-ball-of-mud in the right domain at the right time, sigh.
The world of software is vastly broader than the vast majority of programmers think. And there are a lot of very, very different ways to make money in it.
Regardless of the sleep effect (or lack of) they absolutely do work for reducing eye strain for migraineurs.
It's noticeable to me all the time, but if I'm borderline migraining, or recovering from a migraine, the difference between shifted and not is something I can feel instantly. Shifting all the way over enables me to eek out some work after a migraine without it flaring back up again.
Funny how we don't hear that movement much...
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