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.
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".
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?
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 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.