Surely it costs more than that to run the internet connection as well. I know they choose to do that for other features and probably get good network deals, but the cost is tangible and I would be surprised if it worked out long term.

The hardest part is managing providers across multiple countries, if you want, for example, connectivity in Kazakhstan you either use a super expensive provider that supports a lot of countries, or you have to set up and manage separate contracts with providers in Kazakhstan.

However if you care only about a one or two markets and cars are often already built inside that market...

Cars in in such large numbers that it isn't a big deal to get a bulk deal with each provider seperatly. Though maybe Kazakhstan isn't worth covering at all, I'm not sure what their economy is like.

It was quite complicated for our company since our fleet was in the <50k in size. I worked with specialized trucks.

Complex and a big deal are different axis. I assure you the deals are complex. They are just normal and doable at that scale.

Hey cool, I have two of these for a split keyboard. Working great! I am glad the business side seems to have done well, I usually assume these kinds of projects are passion projects that don't always make ends meet.

The whole Amnesia series, and SOMA (cannot recommend enough) also do something similar. Of course it's horror rather than tactical.

Ferrari makes hypercars, they know a thing or two about making aerodynamics look good. It's a primary concern of all their designs and yet all their other designs look a lot better than this.

I think they are just falling into the same trap all other manufacturers do at first. They think the customer buying the EV is a different customer, who didn't like their other cars. So they make the techno-future mobile for a customer that doesn't exist.

Just make the same cars with an EV drivetrain, that's what the person who loves your brand but is in the market for an EV wants.

Legacy car manufacturers have done just that (forcing an EV into an ICE chassis). The results generally suck and the pure EV manufacturers like Tesla and BYD have kicked their ass in the market.

You can use a similar design to your existing fleet without a literal retrofit of an existing chassis to shoehorn a battery and electric drive train in there.

The retrofits usually are less preferable not only because of pointless inconveniences like transmission tunnels, but because they'll be the manufacturer's first toe dipped into the EV waters. The retrofit chassis speaks to either a rush to market, or a cautious approach not wanting to commit too many resources. The former says it'll have issues, the latter says they might bail on it and leave you stranded for service and repairs. Or both at once.

That was kinda different thing. It was legacy manufacturers scrambling to push out any EV they could get together so they are not left behind too much. But in meantime they started working on genuinely new designs (like Hyundai Ioniq, Mercedes EQS, BMW Neueu Klasse) or they adjusted their platforms to better accommodate electric drive trains (like Audi e-tron).

I wonder this in general, what's the impetus for writing new frameworks and such? Are we already seeing a slow down in that space? HN front page certainly paints that picture.

You're better off plonking down an existing framework and getting all the structural boilerplate benefits the LLM can leverage.

LLMs are far better at frameworks they have a lot of training data for, if have been around for a while. They write more idiomatic, ecosystem friendly code. Does that still matter?

There is a pretty big difference between a citizen driving their car into danger, and a service provider driving their car into danger with you in it.

You wouldn't accept that from a taxi driver either. Pausing the service is the right move.

I completely agree pausing service is the right move. I'm not defending Waymo. More laughing at my fellow ATLiens.

They are also not getting the same quantity or quality of data as was possible in the first years of "ingest". Compared to the beginning, from here on it is more like a drip feed of new training data. Still immense volumes of data, but we are talking 1 year of data production from society versus centuries of text and data ingested in a short time frame.

For pre-training, yes. But for post-training you need high-quality labelled datasets for reinforcement learning. So far AI has been most successful in coding, because you can translate the usage into such datasets, and thus produce a virtuous cycle: More usage produces more data, which produces better models, which drives more usage.

The question is whether this same model can successfully be applied in disciplines like medicine, law, engineering, etc.

I think it's a mix across the different kinds of people.

For me my car isn't loud right now, but I do just genuinely enjoy the thrill and sound of the car in "track" setup. It's too loud to drive on the street but it's a thrill on track. The loudness isn't the point and I wish it were quieter, but the different exhaust components give it the raw visceral sound that I love.

I guess you can think of it like the difference between music on the TV or music at a concert, the sound is literally different not just louder, and the context makes everything more visceral.

Happened for my car community too, anecdotal of course.

Two aspects I think, the clout chasers move on, and the remaining cohort are older with a bit more empathy for community and also better things to do than provoke the cops. No speaking for everyone with that last bit, there are still those that thrive on the chaos.

There's also tech to solve for having your cake and eating it too. Get a valve, loud for track days, quiet for the commute.

Outsourcing seems to come in cycles, where it's tried, fails due to communication issues (resulting in quality issues), then things get inhoused again.

I do think there is some opportunity for AI to smooth out the communication aspect, but I think what we will actually see is larger volumes of poorly guided work coming through for each feature. The AI does not fix the lack of deep systems understanding which is why inhousing is always the antidote to bad outsourcing.

I need to make this clear, there are great devs on either side of the various oceans, the issue is usually communication between two parties with nuturally mis-aligned incentives.

I’ve had a lot of success in past with the Apple approach. I design and architect locally but build it overseas. I think AI and the post-WFH office work culture really helped executives get over the hump / learn to make decisions and lead without being in the same physical space daily. Also, feel like the communication gap is largely a solved problem at this point. It is incredibly common to find English speakers in this profession from any country. The trick is learning to project management. At times, you simply just give the person objective instructions of what to build and the exact rendering and color palette. Or the exact packages you they can use as dependencies. But largely the world communicates together much better than the previous wave of outsourcing.