sky2224

If you're among the top 10% in the US, you're likely doing fine at the moment, but everyone else is struggling. On paper things may seem okay for the government and large companies reaping the benefits of this AI boom, but the current trajectory is not sustainable. Things will get ugly for everyone if we do not course correct on a number of issues like tariffs, Iran war, and more.

Gas prices are at an all time high, housing has never been less affordable, Intel stock just plunged a record 29% in its history, the US is now $40 trillion in debt... this is all just off the top of my head right now.

I think Microsoft laying off 60,000 people was going to happen regardless of this AI boom or even the economy being in the absolute shitter it's in right now.

When the economy was much stronger in 2018 their headcount was around 131,000 people. By 2020 that jumped to 163,000, and by 2022 it reached 221,000. I have personally heard of employees feeling like they had nothing to do.

With their current head count at around 218,000 employees (I'm excluding their Activision head count here), bringing things back down to 160,000 or so employees sounds a bit more realistic, especially given that Microsoft has not been expanding its range of products significantly. They're building out more in the spaces they're in, but they've been pretty conservative on new product launches compared to others.

I frequently have found myself saying, "People pay for that?! But xyz already exists," in the past, but overtime I've learned that (1) marketing is important and (2) if something reduces friction enough to get the wheels turning on something you're trying to achieve, there's always someone (actually a lot of people) out there willing to pay for it even if it's measurably worse than a self-scaffolded solution or even a really good solution that requires a lot of tinkering to get things rolling.

The difference to me is that the models from a decade ago would need to be trained to play the game. As far as I'm aware, Jev has had zero specific training or fine-tuning to play the game. It's simply told, "Hey, here are the rules. Play." That's it.

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

Huh, this explains why I've seen some pretty wildly obtuse far right posts being promoted to the top of the discover page on Snapchat.

> Over the past six months I tried using Claude, chatgpt, Grok and Gemini. At best I got reminders of how things worked. People online say they use them to write their code. The code they supplied to me has NEVER worked or was so convoluted that I threw it away and did it myself.

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.

Because when the government decides you've done something wrong from said video (even if you haven't), it's up to you to prove your innocence. That's a direct contradiction of what the US was founded upon, and creates a very slippery slope.

> Yet we constantly see all this devaluation of the practice of software engineering ("code was always the easy part" - bull-fucking-shit)

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.

I have been asked numerous times now: "Did you ask Claude?"

It's insulting.

I've been pretty interested in cars lately, and Engineering Explained has some great videos. They can stretch a bit longer than 20 minutes, but I'll just pause the video and come back to it later when I have time.

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.

What questions in particular are you thinking of that's purely about the engineering and ultimately not the political agenda?

Why did you need Mythos to do this? There's nothing that seems inherently difficult about what has been built here.

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.

...why? This is something you could automate in about an hour using your own LLM subscription to drive the prompts.

Just mentioning this since no one else has yet: it could be your tonsils and/or adenoids, so it may also be worth seeing an ENT if you suspect this is the case.

Cause driving is fun and these systems get in the way without providing any noticeable benefit to me.

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.

That definitely addresses the privacy issue, but some of the automatic braking and driver awareness monitoring is a bit much for me. For example, Subaru now puts a camera in the dash that frequently scans your face and makes a beeping noise if it thinks you're not paying attention. As you can imagine, it beeps a lot.

You made a good point that I didn't think through fully. It's the concurrent user aspect that heavily impacts things. Currently, you'd probably need quite a bit more investment to the point of having a mini data center to do what I'm proposing.

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.

This is part of the reason why I'm really worried that this is all going to result in a greater economic collapse than I think people are realizing.

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.

Honestly, I think part of the reason Apple hasn't jumped deep into AI is due to two big reasons:

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".

It's probably one of the biggest headlines right now. OpenAI has about $96 billion in debt and they don't have a revenue generating product yet.

No, I don't think so. You've paid for a service that will run an AI model given some prompt. There have been zero guarantees made that it will actually solve your problem.

As others have stated too, how do you define what an incorrect output is?

Can you provide examples of YC startups that knowingly broke laws and just dealt with those issues later? I'm not very aware.

This is a sales post in disguise.

> 03 audio sourced from the web

Where? How do I know you're not pulling from some shady repository?

Chegg is a service many students used to get guidance and answers to homework problems for whatever courses they were taking. It was a sinking ship once GPT 4 came out, but GPT 5 was really it's final nail in the coffin.

I don't know any student that really uses it now.

No kidding, it took my CPU usage from 1% to 55% instantly sheesh

> I am suspicious of grifters and would like to find trustworthy advice.

If you want actual good advice: go to a doctor.

Don't go to a chiropractor, don't go to hackernews. Go to a doctor. You can either start with a physical therapist in your area or start with your primary care doctor to get a referral.

I'm assuming you're in the US, so I know it's expensive but this will genuinely shorten your life span if you let it get significantly worse.

I feel like people genuinely don't understand what vibe coding means.

Just cause you're using an LLM doesn't mean you're "vibe coding".

I regularly use LLMs at work, but I don't "vibe-code", which is where you're just saying garbage to the model and blindly clicking accept on whatever is spit out from it.

I design, think about architecture, write out all of my thoughts, expected example inputs, expected example outputs, etc. I write out pretty extensive prompts that capture all of that, and then request for an improved prompt. I review that improved prompt to make sure it aligns with the requirements I've gathered.

I read the output like I'm doing a deep code review, and if I don't understand some code I make sure to figure it out before moving forward. I make sure that the change set is within the scope of the problem I'm trying to solve.

Excluding the pieces that augment the workflow, this is all the same stuff you would normally do. You're an engineer solving problems and that domain you do it in happens to involve software and computers.

Writing out code has always been a means to an end. The productivity gains if you actually give LLMs a shot and learn to use the tools are real. So yes, pretty soon it's going to become expected from most places that you use the tools. The same way you've been expected to use a specific language, framework, or any other tool that greatly improves productivity.

I wasn't implying that clever prompting needed to be used. I'm just trying to confirm that the person I was replying to isn't just saying what essentially amounts to "build me X".

When I write my prompts, I literally write an essay. I lay constraints, design choices, examples, etc. If I already have a ticket that lays out the introduction, design considerations, acceptance criteria and other important information, then I'll include that as well. I then take the prompt I've written and I request for the model to improve the prompt. I'll also try to include the most important bits at the end since right now models seem to focus more on things referenced at the end of a prompt rather than at the beginning.

Once I do get output, I then review each piece of generated code as if I'm doing an in-depth code review.

Can you provide an example of how you actually prompt AI models? I get the feeling the difference among everyone's experiences has to do with prompting and expectation.