Toyota has been way, way behind on electrification. I suspect they’ve been Innovator’s Dilemma’d are are in a death spiral that they haven’t even noticed yet
yeah. I worked on the internal banking connection at Square in the old days (~2011) and it was a _nightmare_. like, pre-TCP/IP connectivity that depended on dedicated copper to do teletype in COBOL-style fixed length fields in a cryptic format that was only specified in scans of paper documents. We had to write an adaptor that looked like, you know, REST on one end but then shoveled all of the traffic onto that single upstream connection, and then had to try to map responses that came back out of order to the right client. Miserable stuff and a threading nightmare.
I have on old fleet truck: four full doors and an eight-foot bed. I love it, it’s getting quite old, and I have no idea how I’m ever going to replace it
yeah he's really underselling the recent models' ability to do the "Designing the solution conceptually" part. I still have to be in dialogue with the AI - I ask a lot of questions, we iterate towards something - but I cover conceptual ground much more quickly this way. and then I still have to be the glue between "design" and "writing" steps, and have to manage that carefully or I get slop.
if you look at the change in capability over time, it looks like the AI are climbing this hierarchy. "Centaur" seems to already be giving way towards "research assistant". I hesitate to make predictions but I would not place money on things stabilizing here.
if the AI transition really turns into an Artificial Labor revolution - if it really works and isn’t an illusion - then we’re going to have to have a major change in how we distribute wealth. The bad future is one where the owner class no longer has any use for human labor and the former-worker class has nothing
sure but the bigger models don’t make these trivial mistakes, and I’m not sure if translating the LLM english sentences into LISP and trying to check them is going to be more accurate than just training the models better
I’m still trying to understand this. Could it be that these are much larger codebases than I’m used to? I actually haven’t seen software analysis of codebases provide much value
I’m in the same boat, I really like the idea of it but the actual use of it eludes me. It’s like there’s a cultural gap, even when they’re talking about the practical applications of the system it’s incomprehensible; I eventually came to the conclusion that they are doing a lot of work to deal with situations that I have never even heard of people being in
my iPhone gets into a state lately where a pane will suddenly lose the the ability to _scroll_. it can happen in any app, but I see it a lot in Safari. Like, what is even happening, this is a fundamental UI interaction. The only way to fix it is to close the tab or force-quit the app. Super weird.
I have a vague recollection that GitHub is optimized for whole repo cloning and they were asking projects not to do shallow fetching automatically, for performance reasons
then why don’t they co-locate teams when they get RTO’d? I keep hearing about people who have to go sit in a mandatory hot desk but are still stuck on Zoom all day. Seems like the worst of both worlds
my rule of thumb is that the average startup has no more than one novel piece of code, and it is usually one of the first things written. Then after that there’s enough plumbing and UI work to keep a few hundred regular engineers busy
I don’t disagree! but among Americans who do not go to church, most of them have parents or grandparents or great-grandparents who attended Christian church regularly, and some of that culture is still prevalent in subtle things like “protestant work ethic”
MCP tends to be much simpler, less powerful than an API that you’d actually try to develop against. The LLMs need the most simplified access patterns possible