Damn, and only for $180-$230k/y?

That’s highway robbery salary for employment in an industry as ripe as that, somehow makes me like them even less.

🍾🔥

No, but you do, and guaranteed that as they buy up and shut down competitors they will squeeze you for everything you’re worth over largely inelastic demands like pillows, or medicine, or soap…etc

Also haircuts? Haircuts, unless we’re really that deep in corporate hell, are between you and the barber/shop. Billionaires aren’t cutting your hair, the person cutting your hair, the person who owns the salon or barbershop ever, is a peon just like you and me. Small business owners are in the same class as us.

Naw, flawed argument, they steal our resources and then fund themselves by illegal and manipulative means.

Also the economy is shifting where the top 1% are moving closer to being 50% of spending, making the argument rather void.

Could be simpler, yep.

If it was that straightforward then reality would be different huh?

Problem being that the majority of software engineers really don’t know how to build software well.

And are using AI as a tool to just accelerate their lack of engineering.

You could include energy usage in the equation, but if you’re buying a $10,000 Mac Studio to edit your PDFs and watch YouTube, then that’s your own decision. That’s also not what we’re talking about here.

The Mac is still going to be incredibly energy efficient.

Let’s break it down for Qwen 3.5 MoE.

• M4 Ultra Mac Studio:
  • 22t/s
  • ~ 210Wh
  • 9.55 watts per token/s
• B300 GPU:
  • ~ 2160t/s per GPU
  • ~ 1750Wh (14Kwh 8 GPU System)
  • 0.88 watts per tokens/s

The Mac Studio comes out using ~ 11-12x more energy per token. Making it extremely energy inefficient at this task in comparison.

They really aren’t.

Have you used open weight models for serious jobs? Ones that you can actually run effectively on a Mac studio? (Not deepseek v4 pro, not Kimi K3, not GLM 5.3)

They work alright, at best, sometimes.

And the ones you can’t run effectively, locally, like Kimi K3 are considerably better at demanding tasks like software engineering. But even then they still suck at that job compared to frontier Anthropic and OpenAI models. I’ve been building with all of the above, and the open weight models just overall suck at serious high demand software workloads.

No, no, they have not.

I did the math the other day, and the cost of the mac versus the token rate that you get off of it for a decent model would take you over 70 years of 100% 24/7/365 usage to pay for itself versus just using hosted inference.

They are wildly poor options for local AI models, at least on a cost basis. If you don’t care about spending $10,000 to run a halfway okay model at 100x the cost, then go for it.

Pmuch this.

How do they feel about machine language translations? It’s been “AI” (ML model) since the late 90’s.

Traffic prediction?

Medical scan data processing?

And so so many more specific uses of ML models that have been a part of daily life for literal decades.

Call out LLMs specifically.

Good luck.

Lemmy is damn near the when it comes to wanting to hold an opinion on a topic without having first understood that topic.

Yeah, our society is fucked, that’s what

Which is illegal according to EPA regulations, last I knew.

Yeah, here we are.

3-6 active sessions on different work trees isn’t unheard of either

Agent loops for SWE burn a LOT of tokens.

I’m unfortunately temporarily disabled and can’t use my hands for another 2 months. So I’ve leaned heavily into AI based workflows to keep my job in the meantime.

Aside from the nightmare of keeping quality high, not atrophying skills, and avoiding a lack of domain knowledge. It works reasonably okay.

Token usage is insane though. A productive day might cost a few hundred dollars in tokens all things considered. Quality is expensive as well, a good 1/4 of that are automated systems that exist to identify defects, quality, coherence…etc issues early.

Mixed bag.

There are legitimately high value problems that AI works well against. But ungoverned proliferation has a net negative ROI across complex and difficult to measure areas.

When applied expertly, it works great. When blindly handed to your entire workforce as a panacea, not so much

Oh, there are definitely ethical ways to use these models. It’s just that those methods are not being enforced by local counties or your governments. Thus, companies are able to do whatever the hell they want, which means it’s going to be unethical by default.

What we need is regulation, enforcement, and a stop letting these companies trample all over everything they want to.

Each new model of OpenAI’s has had increased costs. Where are you getting this trending down thing from?

Anthropic models have stayed largely the same.

I accidentally went to Google for the first time in months and was wtf’ing over how many results are sponsored.

The complaints about Kagi are valid, mostly, (there’s always that one guy that runs around complaining that Kagi uses Yandex). But damn I’ve gotten spoiled by the results and the ranking customization…

Because fans are not highly tuneable devices?

They vary greatly, even within the same device, even at the same rpm.