all of these problems are better articulated at the level you just explained them. the code for these issues is convoluted and is only of use when an entity (human or not) can actually manipulate the symbolic text that achieves that task. a random oauth stub is of 0 use to the most skilled programmers without documentation as to what contracts and invariants are. bits in a file is just a means
The battery uncertainty is real, but I think the bigger issue is information asymmetry.
Looking at actual market data, the spread on used EVs is wild - a 2022 Tesla Model S ranges from $57 to $112k depending on trim/condition (https://cardog.app/tools/valuation/tesla/model_s/2022). That's a $60k spread on the same year vehicle. Compare that to ICE vehicles where the range is typically much tighter.
When buyers can't confidently price an asset, they discount heavily. The depreciation problem might actually be a data problem - we just don't have the standardized battery health reporting and historical comps that exist for ICE vehicles yet.
Obviously we don't have ICE levels of data, but as far as available data that we do have, that battery uncertainty is probably unwarranted. Battery life seems to be dropping much slowly than early estimates predicted (and this is including vehicles with >100,000 miles, and >10 years of driving history). Risk acceptance is not a thing that has one right answer, so I won't try and say that people are wrong for how they are assessing this risk, but I know that I personally had zero compunctions at all when I recently bought a used EV, and just appreciated the price I was getting.
Now is probably the golden age for buying used EVs, because eventually this notion that the batteries are untrustworthy is going to go away (you can argue about whether this will occur because the technology improves vs. people will better realize where it already is, but it will happen).
Not even just the battery (although that probably is the biggest one), but maintenance in general.
If I buy a 5 year old Corolla with 50k miles on the clock, I have a pretty good idea of what maintenance is going to like for the next decade, and I know a mechanic who can do the work.
I have no idea at all what will happen with a comparable Tesla over 10 years.
Everyone likes to focus on the battery, but in my experience with Ford, Honda and Nissan, there's more frequent expensive surprises in gas engine sedans.
Replacing the passenger occupant detection sensor for the airbag system in my 2007 Ford Fusion cost $2K. After a series of other issues with things like the transmission and fuel injector, I ultimately traded it in for $500.
I got a used Nissan Leaf with low mileage for $18K a few years ago and haven't taken it in for anything yet. Battery health is still at 90%, and I could get that replaced for around $6K if I needed to.
I feel a palpable sense of relief that the surprise maintenance bills have stopped.
This is including the Model S Long Range (decent performance, more focused on efficiency) with the Model S Plaid (fastest accelerating street-legal car in the world?). It's really not realistic. The median is $68k, which is probably much closer to the typical price you'd pay.
answering correctly is completely dependent on the attention blocks to somehow capture the single letter nuance given word tokenization constraints. does the attention block in kimi have a more receptive architecture to this?
"figure out how your employer makes money and position your ass directly in-between the corporate bank account and your customers' credit card information."
we're going to learn why companies were invented the hard way this decade aren't we
MCP feels overengineered for a client api lib transport to llms and underengineered for what ai applications actually need. Still confuses the hell out of me but I can see the value in some cases. Falls apart in any full stack app.
