FWIW, I bought one knowing this, and have zero regrets. Amazing hardware, it's the best personal display on the market. I use it for the majority of my movie watching.
79 karma · joined May 22, 2023
FWIW, I bought one knowing this, and have zero regrets. Amazing hardware, it's the best personal display on the market. I use it for the majority of my movie watching.
I would have loved this in college. I know most don't, but I think we'll hit a point where the people who don't know enough to assess when model output is sub-par will fall by the wayside.
I'm not arrogant enough to think that I won't be included in this blast radius, so I try to dig as deeply as I can whenever possible (and where it makes sense,IE: I couldn't tell you all of the details of LLVM or hypervisor internals)
Personally, this is why I foresee Anthropic/OpenAI/Google taking a bath once their creditors come calling. I think the individuals most attached to a specific app are the ones least willing to pay the true cost of inference. (IE: your grandma using chatgpt to ask for recipe recommendations probably isn't going to pay $400/mo).
We just bought an EV last year and tried everyone. The teslas were the second worst in our opinion. The worst were the rivian products, just horrible UX, poor engineering, poor assembly, clear rent-seeking behavior, etc.
BMW, Ford, and GM make better vehicles and better EVs.
CarPlay does this on my F150 Lightning. It manages state, preconditioning when routing to a charging stop, will suggest charging stops as I'm routing, etc. etc.
There's really nothing special about GM's implementation IMO, except that they charge you monthly to access it.
It’s useful, but when users here say they’re vibe coding 98% of their work, I have to think they’re not working on anything complex.
The changes for cooling, etc. will be substantial, but the problem space is already well-known by the team, so the time to market probably won't be as long as we think.
I also use it to commute, and it's even better at that (part of that is mine being the Platinum trim). Quiet, smooth, powerful, has Android Auto/CarPlay (unlike GM's products), etc.
They really are a fantastic vehicle for those who don't need to quickly tow heavy trailers 400 miles. Especially on the used market.
I think the issue was that Ford wasn't making much margin on them and they weren't moving sufficient volume to make up for that. (around 20K/yr avg)
Producing a lot of code isn’t proof of anything.
The same goes for code as well.
I’ve explored Claude code/antigravity/etc, found them mostly useless, tried a more interactive approach with copilot/local models/ tried less interactive “agents”/etc. it’s largely all slop.
My coworkers who claim they’re shipping at warp speed using generative AI are almost categorically our worst developers by a mile.
They’ve made plenty of things. I liken them to the Lexus of consumer electronics; expensive for what they are, thoughtfully designed, and conservative in their approach to adopting new trends.
I understand that some degree of formalism is required to enable the sharing of knowledge amongst people across a variety of languages, but sometimes I'll read a white paper and think "wow, this could be written a LOT more simply".
Statistics is a major culprit of this.
Any nation with any amount of leverage has abused it.
It rarely makes economic sense to deploy workloads onto the public cloud unless you have critical uptime requirements or need massive elasticity.
I have yet to see anyone show me an AI generated project that I'd be willing to put into production.
IDK, I feel like 'vibe coders' or people who heavily rely on LLM's have allowed their skills (if they ever existed) to atrophy such that they're generally not great at assessing the output from models.
The first MacBook Airs were wildly impractical and expensive.
The first iPad suffered from the same issues.
Various iterations of the iPod nano were functionally kneecapped.
I see a lot of cherrypicking and not a lot of reasoning in this essay.
The benefits you might gain from LLMs is that you are able to discern good output from bad.
Once that's lost, the output of these tools becomes a complete gamble.
I wonder how that will work with the networking reqs the DoD has. Probably some direct link to a gov VPC I suppose.