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

true on the naming, but i think geometric/clifford algebra has its own mysterious aura precisely because it can be framed as "suppressed" or "overlooked".. plus it genuinely does have elegant mathematical structure backing up the hype

funny thing is quaternions had that exact same energy in the computer graphics community for years. after ken shoemake introduced them to CG in 1985, there was a long period of "why are we using euler angles like cavemen when this exists??". now quaternions are well known tooling for people in graphics and the mystique has worn off at least in that community.

EDIT: More interestingly, I find an issue, what do I even DO? If it's not related to integrations or your underlying data, the black box just gave nonsensical output. What would I do to resolve it?

Lots of stuff you could do. Adjust the system prompt, add guardrails/filters (catching mistakes and then asking the LLM loop again), improve the RAG (assuming they have one), fine tune (if necessary), etc.

Some of NVIDIA's models also tend to have interesting architectures. For example, usage of the MAMBA architecture instead of purely transformers: https://developer.nvidia.com/blog/inside-nvidia-nemotron-3-t...

solid analysis but i think you're missing the logical endpoint here: this doesn't end with companies "relearning scarcity"... it ends with the permanent contractor-ification of various types of work at these tech companies (not just tech roles, but other types of roles at these companies). already, contractor-to-employee ratio has gotten higher and higher at these companies in recent years and I expect this to continue.

ZIRP (especially the "double tap" ZIRP in 2021/2022) created this monster (bootcamp devs getting hired, big tech devs making "day in the life of" tiktok vids).

contractors give:

instant scale up/down without layoff optics

no benefits overhead

no severance obligations

easy performance management (just don't renew)

this mirrors what other industries typically do after large restructuring waves ... manufacturing got temp agencies and staffing firms as permanent fixtures post-rust belt collapse. tech is just catching up to the same playbook.

why not just go full bore to duckdb?

i'd say the crypto angle is only one factor. as is usual in the real world, effects are multifactorial.

clawdbot also rode the wave of claude-code being popular (perhaps due to underlying models getting better making agents more useful). a lot of "personal agents" were made in 2024 and early 2025 which seem to be before the underlying models/ecosystems were as mature.

no doubt we're still very early in this wave. i'm sure google and apple will release their offerings. they are the 800lb gorillas in all this.

oh interesting, so it just... didn't use them? lol. i guess the model's training data already has enough web knowledge baked in that it could wing it. curious if explicitly prompting it to reference the specs would change the output quality or time to solution.

very excited to see the agentic sessions when you release them.. that kind of transparency is super valuable for the community. i can see "build a browser from scratch" becoming a popular challenge as people explore the limits of agentic coding and try to figure out best practices for workflows/prompting. like the new "build a ray tracer" or say nanogtp but for agents.

very impressive!

it's amazing how far we've come in 20 years. i was a (very minor) contributor to khtml/konqueror (before apple got involved w/ webkit) in the early 2000s, and back then it was such a labor intensive process to even create a halfway working engine. like, months of work just to get basic rendering somewhat correct on a very small portion of the web (which was obv much smaller)

in addition to agentic coding, i think for this specific task having css-spec/html-spec/web-platform-tests as machine readable test suites helps a LOT. the agent can actually validate against real specs.

back in the day, despite having gecko as an open source reference, in practice the "standards" were whatever IE was doing. so you'd spend weeks implementing something only to discover every site was coded for IE's quirks lmao. for all of their other faults, google/apple and other contributors helped bring in discipline to that.

hard to do "credit assignment", i think network effects go brrrrrr. karpathy tweeted about it, david sacks picked it up, macstories wrote it up. suddenly ppl were posting screenshots of their macmini setups on x and ppl got major FOMO watching their feeds. also peter steinberger tweets a lot and is prolific otherwise in terms posting about agentic coding (since he does it a lot)

its basically claude with hands, and self-hosting/open source are both a combo a lot of techies like. it also has a ton of integrations.

will it be important in 6 months? i dunno. i tried it briefly, but it burns tokens like a mofo so I turned it off. im also worried about security implications.

collaboration is the killer feature tbh. overleaf is basically google docs meets latex.. you can have multiple coauthors editing simultaneously, leave comments, see revision history, etc.

a lot of academics aren't super technical and don't want to deal with git workflows or syncing local environments. they just want to write their fuckin' paper (WTFP).

overleaf lets the whole research team work together without anyone needing to learn version control or debug their local texlive installation.

also nice for quick edits from any machine without setting anything up. the "just install it locally" advice assumes everyones comfortable with that, but plenty of researchers treat computers as appliances lol.

respect to icaza for his contributions (tho I was on the KDE side of the gnome/kde desktop "wars"), but has the "Whatever he works on today will likely be part of a stack you work on in a few years" been true for a long time?

i'm rather unfamiliar with his work post-mono.

there is also sunnypilot, which is a fork of openpilot, and supports more behaviors and cars: https://github.com/sunnypilot/sunnypilot

ya, exactly... i'd also say that when you compress large amounts of content into weights and then decompress via a novel prompt, you're also forcing interpolation between learned abstractions that may never have cooccurred in training.

that interpolation is where synthesis happens. whether it is coherent or not depends.

yep the base model is the compression, but RLHF (and other types of post training) doesn't really change this picture, it's still working within that same compressed knowledge.

nathan lambert (who wrote the RLHF book @ https://rlhfbook.com/ ) describes this as the "elicitation theory of post training", the idea is that RLHF is extracting and reshaping what's already latent in the base model, not adding new knowledge. as he puts it: when you use preferences to change model behavior "it doesn't mean that the model believes these things. it's just trained to prioritize these things."

so like when you RLHF a model to not give virus production info, you're not necessarily erasing those weights, the theory is that you're just making it harder for that information to surface. the knowledge is still in the compression, RLHF just changes what gets prioritized during decompression.

better mental model: it's a lossy compression of human knowledge that can decompress and recombine in novel (sometimes useful, sometimes sloppy) ways.

classical search simply retrieves, llms can synthesize as well.