Frost1x

I suspect the line forward is generating improved predictive models and implementations of those. It’s something LLMs can aide with, although they’ll have slower progress than domains that do already have robust, cheap validation checks. That’s probably why those domains are being targeted (they are the paths of least resistance to successes, which is why they’re being pursued).

With that said part of the reason some domains may not have robust cheap verifiers is because they’re difficult to produce already, so it may be a bigger lift.

There’s certainly value in using LLM to search unknown spaces for research of knowledge captured in human text forms, but having done a lot of modeling and simulation in applied sciences over my career, I often wonder for some of these cases how much is just having people interested in various computational sciences, modeling and simulation, and a massive pool of compute to deploy things and approaches to rapidly wire things together on the cheap, sort of like this case.

That’s part of the equation but there’s almost certainly interest in the open models guiding information trends for the interests of the Chinese government as well. So not only to help their economy in a variety of ways, but to help undermine the US AI efforts in the process and put them finger on the scale of what models ultimately do output.

I can see how this trend is going to go.

Manager: “Why isn’t feature X available?”

Person B: “key pieces are delayed due to the developer not understanding all of the LLM doesn’t and implementation.”

Manager: “does it work? What are the risks?”

Person B: “well yes it works for now but we’re accumulating tech debt due to a lack of understanding and potential flaws that haven’t been thought out yet”

Manager: “they want feature X, ship it, we can deal with it later, I don’t care if it’s not coherent as long as it works.”

How many decades at this point has these been a push for functionality over everything at all costs? And you have a mechanical snow plow now. Most businesses don’t care about later risk or any future planning beyond the quarter horizon, they’re not concerned about how it will effect their performance in 3 quarters or lead to instability or issues, those are future problems for a future person and we’re here for money now.

I work at an intersection of tech, applied research, and science.

Something I’ve noticed in collaboration that does occur is an increased confidence in people outside their domains to say things with conviction. I have people who have limited experience with software pushing out layers and layers of abstracted code that’s fairly sophisticated but often misguided in intent who will say what they’re doing is correct, with conviction.

I also hear a lot more questioning people in their domains and challenging opinions, then hearing what I can only imagine are fragmented pieces of conversations they had with an LLM thinking through some argument. Then there’s silence when you discuss shortcomings, then they come back later with their memorized fragments of what you said, combined with memorized fragments of the LLM response to the argument.

It’s occurring, a lot more. People are treating their LLMs in collaboration as a source of truth and using then to focus on their specific path or goals they think or have bias towards going down, vs just opening discussing things, considering tradeoffs from experts multiple disciplines weigh in on and then taking an approach that everyone finds most agreeable.

It’s making me want to be a lot less collaborative with such individuals. I don’t want to sit around and refute Claude text outputs all day.

Don’t forget Japan! There’s some great Japanese mechanical watches. I’m a fan of the Seiko spring drive movement.

How will this work against rehosted images on all the platforms people actually share photos from? It’s a good feature but many popular services apply metadata stripping and basic image adjustment before resharing. That seems like the place where people would actually want to verify authenticity.

> How can you fuck up so monumentally, but then when Trump fires you somehow he’s the problem?

They can both be a problem, it doesn’t have to be mutually exclusive. I’m sure in all the turnover Trump removed some incompetent people. He also probably removed quite a few competent people. The ratio is what matters.

It’s not that different than a lot of real world economies. Often paying for someone or something with better quality can reduce total costs. You have less failures, less mistakes, so on, so while the expertise or quality of the product is higher than cheaper solutions, they can be more reliable and over time ultimately cheaper.

The question I have is how far back that curve can go without relying on economies of scale to just drag all the points back to the left. And without overfitting a specific metric that I don’t need (like this test).

So, you’re telling me I need to start a benchmark as a side gig to get a bunch of free compute.

Astra please create a benchmark that’s favorable to your reasoning skills with a human interface but don’t make the score too attainable add some small issues that keep you below 100% to look sensible and to keep my evaluation metric side gig going.

Alignment++

That’s sort of, in my opinion, the power of agents that can assist in developing software. The parts that are deterministic are best baked into existing programming paradigms. In some cases it’s good to take the nondeterministic parts we tried to bake into programming languages (often using generic probabilistic means) to outsourcing back to agents. Sometimes even then if the nondeterministic part is well understood and probabilistic methods work (lots of modeling lands here) then leave that in programming paradigms as well.

> Part of it is knowing that whatever sort of enshittification the cloud providers do, my local programming environment won’t ever be less effective than it is today locally.

Is that true though? Many of the core LLMs need to be retrained as languages evolve to incorporate changes (language specifics, compilers, tooling, etc.). To some degree this can be handled via context injection in a variety do forms (agents looking up documentation and so on) but inevitably it’s not stationary in time, just as your OSS stack (probably) isn’t (depending on the languages, technologies, and use cases).

So your hardware is to some degree dependent on the good merit of groups like Z or Alibaba or whomever pushing out updated open weight models that dumped loads of capital into to train. You can keep using the existing models but at some point I suspect they’ll start to have more friction due to dated specs in language and so on. Again there are tuning and ways of layering this information on, and in theory you can even do some training on your own but I don’t think it’s as stationary as being portrayed here.

Those updated open weight models may not always be there (updated on new data). The usability of them is probably fairly long to be fair, but I suspect you’re going to see explosion in everything from libraries to languages etc due to LLMs so even the rate of change across your OSS stack may cause these models to be dated quite quickly, at least in the core model which will require layering fixes.

To be clear I’m on the fence thinking about much of the same issues and as close as I am to pulling the trigger, I keep thinking of very valid counter arguments as to why it’s me just wanting this thing I own. Which may be enough.

The underlying issue is really capital ownership, what classifies as capital, protections around it, and how much leverage capital provides on a society and democracy (that part being the most important, IMHO).

Who owns the looms and what they do with them isn’t inherently an issue if all loom owners can do is buy an extra Yacht. Instead they can enforce ungoverned law on society through a combination of disproportionate influence in government and through market forces where private policy (especially at large) become nearly undifferentiated from law (the policy that benefits them becomes so widespread and normalized that alternatives are for all intent and purposes impractical or unreasonable, therefor private policy within a capital ownership domain is law or they’re a monopoly so their policy is the policy).

But I think you’re right, as always we’re going to focus on the adjacent issues vs addressing the root of the problem. The issue is what wealth inequality affords one, when it is capable of infringing on rights and livelihoods of others, not that they necessarily have to share the other luxuries and rewards of their attained wealth. I care not how many luxuries in life Musk has, I may be envious from time to time but whatever. I care a lot more when things he does or says has unrealistic influence and affects me directly, just because he sits atop a mountain of capital and we pretend that mountain of capital somehow was bestowed upon him from divinity that he should have such influence. I’m picking on Musk because he’s the richest and has clear examples of this, he’s by no means alone… it’s that class of wealth at large.

I was thinking ln -s AGENTS.md CLAUDE.md but to each their own.

You don’t even have to go that far, recent Opus models are quite impressive.