β-redex

Karma: 373

β-redex 8 Dec 2025 22:26 UTC
1 point
0
in reply to: Vladimir_Nesov’s comment on: Vladimir_Nesov’s Shortform
Do you know why it takes such a long time to deploy a new rack system at scale? In my mind you slap on the new Rubin chips, more HBM, and you are good to go. (In your linked comment you mention “reliability issues”, is that where the bulk of the time comes from? (I did not read the linked semianalysis article.)) Or does everything, including e.g. cooling and interconnects, have to be redesigned from scratch for each new rack system, so you can’t reuse any of the older proven/reliable components?

β-redex 4 Dec 2025 20:15 UTC
1 point
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in reply to: Daniel Kokotajlo’s comment on: Daniel Kokotajlo’s Shortform

Starship can launch something like 150 metric tons to orbit iirc.

Well this is one of the main assumptions I am doubting. We haven’t seen Starship carry anything close to that. AFAIK none of the flights so far were done with a mass simulator, the most it carried was a couple of starlink satellites, which I don’t think would weigh more than like 1 ton.

Also, to what orbit? Low earth orbit, geostationary orbit, or an interplanetary transfer trajectory are completely different beasts. (But I guess for most of the examples you list for economic impact you mean LEO.) And with what reuse profile? Both booster and upper stage reuse, or just booster, or nothing? That obviously factors massively into cost, for the lowest cost you want full reuse.

Upper stage reuse in particular is completely new and unproven tech, they promised that with the Falcon 9 too but never delivered.

I would be interested in e.g. seeing a calculation of a LEO launch with booster return to launch site, and with upper stage landing on a drone ship. (Idk what equations you need here, or if you need some simulator software, the extent of my knowledge is the basic rocket equation, and that I have played Kerbal Space Program. In particular aerodynamics probably complicates things a lot, both for drag on ascent, and for braking on descent.)

What is the claimed specific impulse of the raptor engines, and what might be the actual figures? (And also keep in mind that the vacuum engines of the upper stage will be less efficient at the sea level landing, though probably that does not matter much as you burn most of your velocity via aerobraking.) How much fuel are you carrying in which stage, and what reserve do you need for the landings?

At least seeing these numbers check out, without anything physics defying would already be a plus, without even getting into any of the engineering details.

main uncertainty IMO is the heat tiles...

Agree, in particular I don’t see how they will be fully reusable? (AFAIK right now they are ablative and have to be replaced.) I remember years ago there was some presentation that the ship will be “sweating” liquid methane to cool itself on reentry, this being tossed in favor of a non-reusable solution does not instill confidence in me.

what about the fuel and propellant costs?

I agree that the exact fuel price does not matter much, once you get to the point where it’s the main driver of cost you have already reached the level for transformative economic impact.

β-redex 4 Dec 2025 15:59 UTC
3 points
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in reply to: Daniel Kokotajlo’s comment on: Daniel Kokotajlo’s Shortform

SpaceX is working on Starship, which is afaict about as close to being finished as the aforementioned competitor rockets, and when it is finished it’ll should provide somewhere between $15/kg and $150/kg.

Does some independent analysis exist that goes through the calculations to come up with those performance numbers for the Starship design, and maybe estimate how far Starship development is from commercial viability? My impression is that at this point no claims by SpaceX/Tesla should be given any credence, given their abysmal track record with those. (Red Dragon Mars 2018? Starship Mars 2022? Tesla FSD?) On the other hand, it can be easy to overcompensate because of this, just because many of their claims have no basis in reality, does not automatically mean that their technology is bad. Hence, it would be nice to see someone do a thorough analysis.

β-redex 2 Nov 2025 17:49 UTC
3 points
0
in reply to: the gears to ascension’s comment on: Why Is Printing So Bad?

pure math

Actually, I have been diving into some specific topics lately, and simultaneously formalizing the theorems in Lean to help with understanding. The amount of omissions and handwaving going on in “proofs” in textbooks is insane. (To the point where I am not smart enough to figure out how to fill in some omissions.)

And I know that textbooks often only present a summary of a proof, and cite a more detailed source. But sometimes there is no citation at all, and even in cases where a citation exists, it might not contain the missing details.

seems like you can get this in pure math between conflicting formal systems

Hm… I don’t feel like this is what’s happening in most cases I encounter? Once I have a detailed pen-and-paper set-theoretic proof, it’s mostly straightforward to translate that to Lean’s type theory.

β-redex 19 Oct 2025 20:10 UTC
4 points
1
on: Give Me Your Data: The Rationalist Mind Meld
I feel like sometimes I have a hard time keeping track of the experiences that form my intuitive beliefs. Sometimes I want to explain an abstract idea/situation and I would like to bring up some examples… and often I have a hard time of thinking of any? Even though I know the belief was formed by encountering multiple such situations in real life. It would be cool if my brain could list the “top 5 most relevant examples” that influenced a certain intuitive belief, but, in the language of this article, it seems to just throw away the training data after it trained on it.

Case in point: I cannot easily think of a past situation right now where I tried to explain some belief and failed to come up with examples...

β-redex 19 Oct 2025 17:30 UTC
2 points
0
in reply to: β-redex’s comment on: Jan Betley’s Shortform
Well, today GPT-5-Codex solved it on the 2nd try. (The first version it gave was already conceptually correct, but I guess had some subtle bug. After I told it to fix it and test the fix, it gave a working solution.)

I am just surprised how well the agentic loop is working. It cloned the specific Lean version’s source code I was asking for, inspected it to understand the data structure, downloaded a release tarball to test it, all without losing track of its goals. All this would have been unimaginable ~a year ago.

So yeah, in 7 months (but maybe even 2 if you count the base GPT-5 attempt) we went from “not even close” to “solved” on this problem. Not sure how I should feel about this...

β-redex 28 Aug 2025 10:59 UTC
1 point
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in reply to: eggsyntax’s comment on: eggsyntax’s Shortform
I wonder if there are people/groups who (implicitly) do the same with ChatGPT? If the chatbot says something it is considered truth, unless someone explicitly disproves it. (I think I have read stories hinting at this behavior online, and also met people IRL who seemed a bit to eager to take the LLM output at face value.)

β-redex 17 Aug 2025 0:23 UTC
4 points
0
on: Debugging for Mid Coders

How do you learn to replicate bugs, when they happen inconsistently

I don’t have definitive advice here, I think this is a hard problem no matter your skill level. You can do things in advance to make your program more debuggable, like better logging, and assertions so you catch the bug closer to the root cause.

A more general pattern to look for is some tool that can capture a particular run of the system in a reproducible/replayable manner. For a single program running locally, a coredump is already quite good, you can look at the whole state of your program just before the crash. (E.g. the whole stack trace, and all variables. This can already tell you a lot.) I have also heard great things about rr, supposedly it allows you to capture a whole execution and single step forwards and backwards.

For distributed systems, like web applications, the problem is even harder. I think I have seen some projects aiming to do the whole “reproducible execution” thing for distributed systems, but I don’t know of any that I could recommend. In theory the problem should not be hard, just capture all inputs to the system, and since computers are deterministic, just replay the inputs. But in practice, given the complexity of our software stacks, often determinism is more of a pipe dream.

How does one “read the docs?”

Something something “how to build up a model of the entire stack.”

I think these are closely related. I imagine my “model of the entire stack” like a scaffolding with some knowledge holes that can be filled in quickly if needed. You should not have any unknown-unknowns. If I notice that I need more fidelity in some area of my model, that’s exactly the docs I read up on.

When reading docs, you can have different intentions. Maybe you are learning about something for the first time, and just want to get an overall understanding. Or maybe you already have the overall understanding, and are just looking for some very specific detail. Often documentation is also written to target one of those use-cases, you should be aware that (well) documented systems often have multiple of these. This is one model I came across that tries to categorize documentation (though I am not sure I subscribe to these exact 4 categories):

(from https://diataxis.fr/)

Getting back to the “model of the entire stack” thing, I think it’s very important for how (I at least) approach computer systems. I think this article by Drew DeVault in particular was an important mindset-shift back when I read it. Some quotes:

Some people will shut down when they’re faced with a problem that requires them to dig into territory that they’re unfamiliar with. [...] Getting around in an unfamiliar repository can be a little intimidating, but do it enough times and it’ll become second nature. [...] written in unfamiliar programming languages or utilize even more unfamiliar libraries, don’t despair. All programming languages have a lot in common and huge numbers of resources are available online. Learning just enough to understand (and fix!) a particular problem is very possible

I now believe that being able to quickly jump into unfamiliar codebases and unfamiliar languages is a very important skill to have developed. This is also important because documentation is often lacking or non-existent, and the code is the “documentation”.

Also, I feel like the “model of the entire stack” thing is a phase shift for debugging once you get there. Suddenly, you can be very confident about finding out the root cause of any (reproducible) bug in bounded time.

If at any point you notice that your unfamiliarity with some part of the system is impeding you in solving some problem, that’s a sign to study that area in more detail. (I think this is easier to notice when debugging, but can be equally important when building new features. Sometimes your unfamiliarity with a certain area leads you to build a more complex solution than necessary, since you are unable to search paths that route through that area. A map analogy here would be you having a dark spot on your map, and noticing whether it’s likely that between two points, there could be a shorter path through the dark area.)

β-redex 7 Aug 2025 22:36 UTC
2 points
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in reply to: β-redex’s comment on: Jan Betley’s Shortform
GPT-5 attempt: https://chatgpt.com/share/689526d5-8b28-8013-bcbf-00f76cd37596

It at least hallucinates less, but after recognizing the difficulty of the problem it just gives up, gives a (by its own admission) half-assed solution that cannot work, and goes on to explain why I should be asking for something different, and given my constraints a solution is not practical. (See sibling comment for a very much practical solution I wrote in ~2 hours.)

β-redex 7 Aug 2025 18:27 UTC
16 points
0
in reply to: tdko’s comment on: tdko’s Shortform
This 25m 80%-time horizon number seems like strong evidence against the superexponential model from ai-2027. On this graph the superexponential line shows 4h at the end of 2025. I feel like GPT-5 will be the biggest model release of the year, I don’t see how we would see a model with an 8x time horizon of GPT-5 this year.

β-redex 31 Jul 2025 20:38 UTC
4 points
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in reply to: Said Achmiz’s comment on: My Empathy Is Rarely Kind

Ok, now this is actually really weird. You’re saying that the only reason you can think of to want to feel empathy toward other people… is to make yourself happier…?

I do most things in my life to try to make myself happier. (And I think this is the revealed preference of most people, even if they don’t admit it, because being seen as “selfish” is sometimes socially not tolerated well.)

(But I don’t have a fully fleshed out moral philosophy, and I would not feel confident in asserting that “I do everything only to make myself happier”. E.g. I do notice myself being nicer to people in general than what would naively be predicted under the “100% selfish” model.)

β-redex 31 Jul 2025 6:37 UTC
3 points
0
in reply to: Said Achmiz’s comment on: My Empathy Is Rarely Kind

People do sometimes write LW posts about things that aren’t, like… serious problems in their own personal lives.

I felt like the post was written in a tone describing a personal issue. And if this is not a personal issue for OP and they have some easy way of dealing with it, that seems like a weird thing to omit from the post.

I don’t really know why so many commenters are talking about things like a “psychological need to have people to feel empathy towards”. Where does OP talk about having any such thing…?

That was pure inference on my part. I do think having people to feel empathy towards makes me happier. I felt sad when I was grappling with similar questions years ago as OP, and I had to accept that I won’t be able to feel empathy towards most people, I should just model them as part of the external world, and not try to imagine myself in their place.

If OP has some other reason for wanting to empathize with people, I would like to know, because I can’t really imagine other important reasons.

β-redex 30 Jul 2025 21:37 UTC
6 points
5
on: My Empathy Is Rarely Kind
I don’t quite understand why this is a large enough problem in your life even to trigger writing a LW post about?

I imagine that you would just curate your group of friends to consist of people you can empathize with, to fulfill your psychological need to have people to feel empathy towards, and just model all other humans as you would model any other part of the world.

Unless I am misunderstanding what you mean by “being able to empathize with someone”, and you have a genuine shortage of people who would fulfill your requirements? (In my mind it’s not insanely hard to find such people, I think above a certain level of communication and self-reflection ability this is a non-issue.)

β-redex 30 Jul 2025 21:17 UTC
5 points
0
on: My Empathy Is Rarely Kind
if I were next to her, I could role-play as a supportive friend or partner.
In situations like this I might tell them something like “you probably know that if I were in your shoes I would have a straightforward solution to this, but I understand that you don’t like my solution, and I am willing to just listen if that’s helpful to you”. Basically you are telling them upfront that you will be role-playing the supportive friend.
Interestingly, I don’t remember a single instance where someone got mad at me for this, which is somewhat surprising to me.

β-redex 15 Jul 2025 21:04 UTC
3 points
2
on: Do confident short timelines make sense?
During the live debate Tsvi linked to, TJ (an attendee of the event) referred to the modern LLM paradigm providing a way to take the deductive closure of human knowledge: LLMs can memorize all of existing human knowledge, and can leverage chain-of-thought reasoning to combine that knowledge iteratively, making new conclusions. RLVF might hit limits, here, but more innovative techniques might push past those limits to achieve something like the “deductive closure of human knowledge”: all conclusions which can be inferred by some combination of existing knowledge.
This “deductive closure” concept feels way too powerful to me. This is even hinted at later in the conversation talking about mathematical proofs, but I don’t think it’s taken to its full conclusion: such a deductive closure would contain all provable mathematical statements, which I am skeptical even an ASI could achieve.^[1]
To spell this out more precisely: the deductive closure of just “the set theory axioms” would be “all of mathematics”, including (dis)proofs for all our currently unproven conjectures^[2] (e.g. P ≠ NP), and all possible mathematical statements.^[2]
1. ^
  Well, as long as we want to stick to some reasonable algorithmic complexity. Otherwise the “just try all possible proofs in sequence” algorithm is something we already have and works perfectly.
2. ^
  Well, as long as they are not undecidable.

β-redex 30 Jun 2025 16:17 UTC
2 points
0
in reply to: johnswentworth’s comment on: johnswentworth’s Shortform
Conditional on LLMs scaling to AGI, I feel like it’s a contradiction to say that “LLMs offer little or negative utility AND it’s going to stay this way”. My model is that we are either dying in a couple years to LLMs getting us to AGI, and we are going to have a year or two or of AIs that can provide incredible utility, or we are not dying to LLMs and the timelines are longer.

I think I read somewhere that you don’t believe LLMs will get us to AGI, so this might already be implicit in your model? I personally am putting at least some credence on the ai-2027 model, which predicts superhuman coders in the near future. (Not saying that I believe this is the most probable future, just that I find it convincing enough that I want to be prepared for it.)

Up until recently I was in the “LLMs offer zero utility” camp (for coding), but now at work we have a Cursor plan (still would not pay for it for personal use probably), and with a lot of trial and error I feel like I am finding the kinds of tasks where AIs can offer a bit of utility, and I am slowly moving towards the “marginal utility” camp.

One kind of thing I like using it for is small scripts to automate bits of my workflow. E.g. I have an idea for a script, I know it would take me 30m-1h to implement it, but it’s not worth it because e.g. it would only save me a few seconds each time. But if I can reduce the time investment to only a few minutes by giving the task to the LLM, it can suddenly be worth it.

I would be interested in other people’s experiences with the negative side effects of LLM use. What are the symptoms/warning signs of “LLM brain rot”? I feel like with my current use I am relatively well equipped to avoid that:
- I only ask things from LLMs that I know I could solve in a few hours tops.
- I code review the result, tell it if it did something stupid.
- 90% of my job is stuff that is currently not close to being LLM automatable anyway.

β-redex 10 Jun 2025 22:29 UTC
1 point
0
in reply to: habryka’s comment on: β-redex’s Shortform
Out of curiosity, why do you post on Twitter? Is it network effects, or does it just have such a unique culture that’s not available anywhere else? (Or something else?) Do you not feel any kind of aversion towards the platform, which would potentially discourage you from interacting? (I don’t mean this to sound accusatory, if your position is that “yes Twitter is awesome and more rationalists should join” I would also like to hear about that.)

β-redex 10 Jun 2025 15:55 UTC
1 point
0
in reply to: CstineSublime’s comment on: β-redex’s Shortform
As you highlight, asking everyone to set up their own cross-posting solution is probably not viable. But if there was some service run by the LW team that had a simple guide for setting it up (e.g. go to your LW account, get your Twitter API key, copy it here, grant permission, done.) and it took ~5 minutes, that would lower the barrier to entry a lot and would be a huge step forward.

β-redex 10 Jun 2025 15:42 UTC
3 points
2
in reply to: quetzal_rainbow’s comment on: β-redex’s Shortform
Not even fitting for Quick Takes? We could have a “Quicker Quick Takes” or “LW Shitposts” section for all I care. (More seriously if you really wanted some separate category for this you could name it something like “LW Microblog”.)

Also a lot of Eliezer’s tweets are quite high effort, to the point where some of them get cross-posted as top level posts. (E.g. https://www.lesswrong.com/posts/fPvssZk3AoDzXwfwJ/universal-basic-income-and-poverty )

β-redex 9 Jun 2025 21:20 UTC
36 points
27
on: β-redex’s Shortform
Why is a significant amount of content by some rationality adjacent people only posted on X/Twitter?
I hope I don’t have to explain why some people would rather not go near X/Twitter with a ten foot pole.
The most obvious example is Eliezer, who is much more active on Twitter than LW. I “follow” some people on Twitter by reading their posts using Nitter (e.g. xcancel.com ).
What triggered me to post this today is that it seems @Aella set her account to followers-only (I assume due to some recent controversy), so now the only way for me to read her tweets would be to create a Twitter account.
Why can’t some of this content be mirrored e.g. as LW Quick Takes, or on basically any other platform outside Twitter’s walled garden? A lot of internet personalities cross-post their stuff to multiple platforms these days. One of the best examples I have seen is Molly White, she has her own self-hosted microblog feed which is pushed to Twitter, Bluesky, and Mastodon simultaneously: https://www.mollywhite.net/micro
I think even just having Eliezer’s content available somewhere else would be valuable enough to the community for the LW team to possibly assist with some technical solution here.