What is that supposed to mean? They're trying to be an llm search engine that's not some radical new concept

the problem with that argument is that the vision pro exists, where they clearly overestimated demand, and where even among people with interest in VR and disposable income, the compromises on battery life and weight were actually too much to bear. Forecasting is just hard and you always need to be especially skeptical when you yourself are doing the forecasting for something you want to succeed. All those arguments for the neo line up in retrospect hindsight is always 2020

this article's also about enterprise demand specifically. That's a bit surprising to me as well frankly. I'd have thought the primary market for mac studios would be hobbyists/enthusiasts with a bunch of disposable income who are willing to pay 18k for a 512 gb machine to run glm 3.5 flash or 9k to run deepseek v4 flash locally. It's competing with a $200/mo subscription or renting server gpu time for open source models during a memory shortage - and idk if it's going to be powerful enough to train or fine tune so it's really just inference. seems reasonable to be surprised

Also, > tell them it's their responsibility if it fails, so they can't blame it on you when it does fail.

That's definitely not how any of this works. They can absolutely blame whoever they want and often have the power dynamic in place to get away with it. Especially in this case where they'll just say the author was given a senior title and a raise so it's on them to deliver and their attempts to shift the blame to management won't work to cover up the engineers failure to deliver etc. You just have no leverage as an engineer joining a company that doesn't care or doesn't need devs to the extent they go extended periods of time without a single one. And if you don't build that leverage within the company you pretty much have no option but to go somewhere else if put in a bad situation.

Plus you don't want to take a risk on training a new grad anymore because usually they'll leave after a couple years, which was a good thing since you'd backfill with a new grad some other company had trained/ideas circulated in the industry/early career engineers built their networks and got exposure to different tech stacks and company styles etc. that's really tough now too since nobody else is hiring and training juniors so you would be wasting time and effort training some for some other company to hire away - nash equilibriulm vibes.

So yes something shifted- the new grad/junior market is in the gutter rn. And if AI plateaus in this range of capability idk what happens as the current generation of engineers age out/retire. The juniors that aren't being trained today are going to lead to a vacuum of senior engineers tomorrow.

And in terms of accountability/feeling the impact of their poor decisions - the blog is already describing them throwing the engineer under the bus, telling them they need to pull their weight, blaming them for mistakes, etc. Management isn't going to feel the impact of anything. Theyve got a convenient fall guy right there and they're already setting him up to take the blame if stuff goes south.

Sometimes stuff just sorta sucks ¯\_(ツ)_/¯

>Also stop saying “please” to an LLM. It does not have any feelings.

It emulates having feelings and it's a next token predictor. The next token in a dataset where you respond to an engineer rudely and call them stupid is rarely said engineer locking in and delivering incredible code. You have to play along to get the output you want.

> Again, I recommend people try running small LLMs locally where temperature and other settings are fully exposed and configurable to see this themselves.

This is like saying you should experiment with a paper airplane to see why fighter jets are overrated. Also the general understanding of temperature is not super solid here - it's not just that its "too boring" without it - random sampling is required for the models to work.

>Why are benchmarks showing they're still improving?

Ironically this section is far too generous to LLMs and benchmarks. lLMs cheat and companies benchmax. Don't trust benchmarks. They lie

Also in the what I do section - are these using local LLMs too? "Sometimes looping llms on itself can make it fix its own errors" feels very 2025 - the modern state of things is more "we've given up on one shots and getting it to produce the right answer immediately, set it up with a test harness so it can fix its own mistakes and let it loop otherwise it won't work." Also "asked fellow developers to make sure their code is well structured, easy to follow and documented. LLMs unfortunately make it easier for people to cheat in this regard" - really? Easy to follow? Maybe gpt models with good steering but trying to get an anthropic model to speak coherently and clearly and write documentation that isn't incomprehensible slop is a Herculean effort

I think maybe if you're at a company like that, you should be very cautious in suggestions/timeline/etc about literally anything and everything technical. Certainly much more than you would be at a company that has a basic level of software engineering competency. It's kind of a different world...

> Blog post clearly generated by software x