I work in manufacturing. I think this will be fantastic for stuff like SPC.

I've been finding AI either works great for people or is just awful, and I'm having a hard time pinpointing where the problem is. I don't think there's a big intelligence gap between us, so it's not like others wouldn't be noticing the things you're claiming when using AI. I genuinely believe your experience has been bad, but the part I'm trying to figure out is why it's been bad.

So my questions are: have you given things a fair shot? Have you tried turning things on its side to see if approaching AI usage a different way results in significantly better and more useful results? Do you actually put thought into what you're writing in your prompts (i.e., if I took your prompt and gave it to a junior dev, would they know what to do)? Are you just using the free models available online or do you have a dedicated integration into your development environment through Github Copilot or other vendor(s)?

I mean what I'm about to say with zero offense, but this honestly reminds me of how the elderly generation says, "technology doesn't work for me because it's always broken", and when you go and try to coach them on how to use the technology that's problematic for them, it's as if you're casting black magic.

I don't think AI is the second coming of Christ or that we're anywhere near AGI, but I do believe it's an extremely useful tool that people should be using.

I see solutions using LLMs, VLMs, etc that will achieve things that have been solved before (things like OCR, parsing, or even just code generation). They do these problems really well and really fast... 90% of the time. How do I get a scaffolding setup so I can be 100% confident in my solution (meaning 100% confident that I know what it's going to do)?

With standard ML, I have things like confidence scores to base whether or not I accept an output as valuable. And generally speaking, it's pure (i.e., given the same input I will always get the same output). With LLMs and the like, it feels like this piece is missing.

Comments URL: https://news.ycombinator.com/item?id=49293943

Points: 3

# Comments: 0

Whenever I've used this phrase or seen other people use this phrase it's never been in the context of software engineering. It's always been in the context of literally just spitting out code that a compiler will accept. Believe it or not, a lot of students in CS cohorts have significant struggles fighting the compiler because they just don't understand the language they're programming in and are slow to become proficient (if they ever do). Thus, a lot of people have this idea that writing code, and I mean literally just spitting out something that compiles and runs is hard.

Doing that is indeed quite easy today. That's what we mean by "coding was always the easy part".

Writing code that actually scales into something bigger and is constructed in a way that meets requirements and is flexible? That's a whole different ball game. That's software engineering.

It's insulting.

And given that I've been interested in cars lately, Delivrd is a channel of this guy that negotiates car deals for a living, and it's actually really interesting to see the different behaviors of car dealerships. His videos are longer, but you can kind of just skip around since it's basically just livestream clips.

And finally, I've been watching lots of older spongebob, south park, and family guy episodes. Man those are hitting the spot right now.

Correct me if I'm wrong, but it seems like you've built a fancy looking site for a service that essentially generates a prompt for something that may be related to your business, and throws it into each of the major AI services (chatgpt, claude, gemini, etc) to see if it name drops your brand... and you want to charge $49/mo for that?

If you can get someone to pay you that, more power to you, but sheesh.

I tried lane keep assist in an audi a5 sportback, and it was genuinely terrifying to me. It felt like it was trying to drag me into oncoming traffic when going around curves.

Automatic braking sounds great on paper but practically speaking doesn't work out in my opinion, and once again seems to get in the way. Many people have reported getting frequent alerts while driving or even having ghost braking going on with their Subaru EyeSight systems. This wouldn't be an issue if I could confidently remove the system from my vehicle, but right now I practically can't without doing some heavy lifting on my own.

I'm aware that these systems are pretty integrated, so it's not just a matter of pulling a fuse, but doing some type of reverse engineering to take control over how things internally work. I'm doing the research and am willing to put a lot of time in to make this happen, but I'm curious to know if anyone has explored this themselves already and to hear about their own findings.

Comments URL: https://news.ycombinator.com/item?id=48261313

Points: 2

# Comments: 6

However, we've been seeing advancements in compressing context and capabilities of smaller models that I don't think it'd be too far off to see something like what I'm talking about within the next 5 years.

I think companies that are shelling out the money for these enterprise accounts could honestly just buy some H100 GPUs and host the models themselves on premises. Github CoPilot enterprise charges $40 per user per month (this can vary depending on your plan of course), but at this price for 1000 users that comes out to $480,000 a year. Maybe I'm missing something, but that's roughly what you're going to be spending to get a full fledged hosting setup for LLMs.

1) Apple is not a data company.

2) Apple hasn't found a compelling, intuitive, and most of all, consistent, user experience for AI yet.

Regarding point 2: I haven't seen anyone share a hands down improved UX for a user driven product outside of something that is a variation of a chat bot. Even the main AI players can't advertise anything more than, "have AI plan your vacation".