The gist, that the same principles that make a video game where you save a princess fun to learn (e.g. super mario bros) can be applied to building products, seems so obviously true - yet difficult to put into practice.

The most influential part of this presentation is this quote:

When you build applications that let users be smart, they love you for it. The secret to good game design is simple. Set up situations where there is a problem that must be solved and let the user solve it. Give them subtle clue, but don’t take away that ‘aha’ moment.

Scary part: you have to believe the user is smart.

Having said that, the actual recommendations the article offers are pretty reasonable:

- Do as much as you can with code

- For the parts you can't do with code, use specialized AI to solve it

Which is pretty reasonable? But also not particularly novel.

I was hoping the article would go into more depth on how to make an AI product that is actually useful and good. As far as I can tell, there have been a lot of attempts (e.g. the recent humane launch), but not a whole lot of successes yet.

I've played around with shaders in the past to build particle simulations with millions of points, and it's always really tickled my mind. You basically write functions that operate against a big 2d grid of colors. But because colors are represented by a 4x1 vector [r,g,b,a], you can repurpose this pattern to do general purpose computation (e.g. you can represent a point in a 3D coordinate space as a [r,g,b] color).

You can see this in the codepen in this post. It's literally creating "materials" and "render targets" that are actually just intermediate computation steps in the fluid simulation.

  class Fluid {
      constructor(context) {
          this.context = context;
          this.speed = 4;
          this.forceInitMaterial = this.createShaderMaterial(forceInitFrag);
          this.divergenceMaterial = this.createShaderMaterial(divergenceFrag);
          ...
          this.divergence = this.createRenderTarget();
          this.advection = this.createRenderTarget();
          ...

Look up Mark Harris PhD thesis "Real-Time Cloud Simulation and Rendering" for more details.

The AI craze we have today is built on the continuous, sustained work of a huge amount of people over years and years.

At some point in time, certain ideas are just inevitable. I'm certain back then many people were looking at GPUs and tricked them into performing non-graphics tasks.

I can say that at my last company, redesigning the login page was bottom of the stack of things we wanted our first designer to work on. We just had a centered "Login with Google" button, our logo, and a email + password form. What else do you need?

Simple it good though, agree with that.

I don't think a lot of people back in 2007 could have predicted that the biggest thing to come from mobile would be an app that let teens remix music videos and share with their friends.

This is why I think it is a little pointless to try and create mental models for what products and features to build to capitalize on AI (though it can be fun). It's so early that we're not capable of understanding what's possible yet. If anything, we're probably at the viral "fart app" stage that mobile was in for its first few years.

I think that the biggest thing to come from mobile was always available location, exemplified by Google Maps which already existed before mobile happened. Many of the most successful apps relied on this.

TikTok is different, in that it could have existed on desktop (but would have looked very different) whereas Uber (for example) definitely couldn't.

I wouldn't under estimate the amount of friction reduced in having an app on your phone which a) is always with you, b) can also record top quality video and c) has a data plan good enough to upload there and then.

Yup, definitely. Effective mobile editing was a big driver of it's success.

That being said, while it's definitely more effective and popular on mobile how much of that is just down to more people using the internet on mobile relative to desktop.

This is unlike Maps or Uber which only make sense with mobile and always available location.

https://en.wikipedia.org/wiki/DailyBooth

I wish I could do software development from my phone the way people casually watch tiktok videos. The least unproductive thing I can currently do when trapped with only a phone is read HN.

(Perhaps SMS plus some feature-phone-level of GPS integration.)

One of the core features that made me use Uber was the map - I could see where the driver is going and how far away is he. Also, the app is localised into language that I can understand and I can see the price upfront without having to worry about getting scammed. Recently I had to book a taxi over the phone at the end of the world (literally - Ushuaia) in a language that I can barely speak and the experience was rather stressful in comparison with using an app.

Real-time mapping would be tricker if that's really a killer feature for you.

Or a voice connection with an operator.

You'll pretty much would have had human operators over every interaction. That very much hinders the Uber-like growth of the service.

Falling back to a human operator isn't that bad either: just charge people a little bit extra to talk to a human.

Identity-specific data is sold all the time for everything from advertising to credit scores and what would otherwise be called social credit scores of they were run by the government instead of private companies.

I'd be curious to see how the price compares when selling 10k individuals' data versus aggregate data the 10k people. Presumably if it was cheaper to buy all the individual data I would do that and aggregate it myself.

Private investigators, three letter agencies, and any company wanting to send mailers to my new address when I move all need the individual data to target me specifically.

I totally agree the aggregate data is given more value in a commercial market heavily focused on advertising and now training LLMs, my only point was that there are markets that highly value individual data as well.

I think the point stands that it wouldn't be easy to sell your individual data, and even if you could, it would be for a pittance from someone who is looking to build a large dataset. They certainly wouldn't be giving you an amount that justifies advice like "hold on to your data".

>What if I want to keep my history on but disable model training?

We are working on a new offering called ChatGPT Business that will opt end-users out of model training by default. In the meantime, you can opt out from our use of your data to improve our services by filling out this form. Once you submit the form, new conversations will not be used to train our models

https://docs.google.com/forms/d/e/1FAIpQLScrnC-_A7JFs4LbIuze...

(There might be other concern, eg around privacy, about giving your data away. But worrying about value isn't really one of them as an individual.)

(And btw, piracy only makes the data less valuable, because you lose the ability to sell it to someone who already has it. Not because the game or movie etc would become less enjoyable.)

Data is a moat. If you share it you're giving your advantage away.

You mean how fast that took, right?

So, yeah, there was a maybe five year (and certainly less than ten) period when smartphones went from a fairly niche thing to ubiquity which is very fast compared to technology adoption generally.

Then overlay that with the limiting factors for AI/LLMs.

I had a third-party logistics startup in 2007 that I would have loved to turn into Uber for shipping, but it would be at least a few years before the cell networks and ownership of smartphones reached a point where it was possible.

I don't know if there are limiting factors that will delay LLMs potential to disrupt the status quo.

That's funny for me to read almost 15 years after rjdj [0] There is a difference between an idea that is possible, and an idea whose time has come.

[0] https://en.wikipedia.org/wiki/RjDj

I feel like that's a bit different from what most people think of when they hear "feature factory." When I hear feature factory, I think of an engineering team that has zero input into the product process and just builds whatever PMs or leadership says is important.

In the case of an early startup with no customers, I think if engineering teams get no justification, aren't involved in talking with customers, they are completely within their rights to complain about feeling like a "feature factory." The right solve isn't to say "actually -- we have no customers, so shipping a bunch of stuff isn't feature factory mentality" but to actively engage the team in product discovery conversations with users.

Yep, this was what I had heard referred to as a feature factory. No input of the engineering team, just take stories and churn out code.

https://news.ycombinator.com/item?id=27252066

Personally, I think your demo has a pretty neat / novel UX. I couldn't find a way to generate a large number of examples at once -- did I miss that somewhere?

But as long as the first index in an array is repeat(), anything in the second index will repeat N amount of times.

For example, at our company we have a quite a few of alerts set up. Datadog also automatically detects anomalies. It would be neat if this (or something else) could automatically do an initial triage without being prompted and give me a free headstart on issues that come in.

Otherwise, it feels like it's "work" to learn how to use the product, which seems to miss the promise of AI (doing things for us!).