bhadass

these days it's just Microslop

its very transparent what they're doing

they're almost certainly going to replace all the board memebers with political loyalists. the board members served six year terms specifically so they'd span multiple administrations and stay independent.

firing them all at once lets you stack the entire board with people. it's not about making science better, it's about removing the people who'd say no.

the honest answer is it's not really about science at all; its about removing independent oversight.

the "benefit" from his perspective is the same playbook trump admin has been running across every federal agency, he wants to replace independent experts with loyalists, remove checks on executive power, and redirect spending toward admin priorities.

the board members served six year terms specifically to insulate science funding from political cycles. that's a feature to everyone else and a bug to this administration.

I dunno, my experience mirrors the parent posters: we use opus for all our coding, but gpt 5.4 for all of our enterprise agentic work via api (much bigger amount of tokens). it just seems to be more optimized for this.

i agree that their products are quite stale, but a lot of that is cuz they're so deeply entrenched in enterprise workflows in a way that's incredibly hard to displace. switching costs for jira alone across a large org are brutal.

could an AI native competitor eventually eat their lunch? sure. but "no new sane company wants to use their products" is a stretch when their customer count is literally still growing double digits.

i would not say "writing on the wall" at all.

but will iran accept the taco? at this point, it seems like it may get ideological for them, and they play a different war (jihad) which would require diverting prolonged resources. we may not be able to simply just leave and revert to normal.

but the slop will likely better as models improve I guess

openclaw is just one of many now, there are new ones weekly.

i agree w/ the the complexity analysis point, but that theoretical understanding actually translates to real world deployment decisions in both subfields. knowing an algorithm is O() tells you surprisingly little about whether itll actually outperform alternatives on real hardware with real cache hierarchies, branch predictors, and memory access patterns. same thing with ML (just with the very different nature of GPU hw), both subfields hve massive graveyards of "improvements" that looked great on paper (or in controlled environments) but never made it into production systems. arxiv is full of architecture tweaks showing SOTA on some benchmark and the same w/ novels data structures/algorithms that nobody ever uses at scale.

but isn't this like a lot of other CS-related "gradient descent"?

when someone invents a new scheduling algorithm or a new concurrent data structure, it's usually based on hunches and empirical results (benchmarks) too. nobody sits down and mathematically proves their new linux scheduler is optimal before shipping it. they test it against representative workloads and see if there is uplift.

we understand transformer architectures at the same theoretical level we understand most complex systems. we know the principles, we have solid intuitions about why certain things work, but the emergent behavior of any sufficiently complex system isn't fully predictable from first principles.

that's true of operating systems, distributed databases, and most software above a certain complexity threshold.

I swear the Tay incident caused tech companies to be unnecessarily risk averse with chatbots for years.

yeah, but isn't the whole point of claude code to get people to provide preference data/telemetry data to anthropic (unless you opt out?). same w/ other providers.

i'm guessing most of the gains we've seen recently are post training rather than pretraining.

yup, bezos said "we will be able to beat the cost of terrestrial data centers in space in the next couple of decades". presumably this means they'll need huge ass radiators, so its all about bringing down launch costs since they'll need to increase mass.

they should just acquire one of the many agent code harnesses. Something like opencode works just as well as claude-code and has only been around half of the time.

attitude control in general