abramdemski

Karma: 20,231

abramdemski 7 Oct 2025 0:49 UTC
16 points
1
in reply to: 1a3orn’s comment on: abramdemski’s Shortform
I appreciate the pushback, as I was not being very mindful of this distinction.
I think the important thing I was trying to get across was that the capability has been demonstrated. We could debate whether this move was strategic or accidental. I also suppose (but don’t know) that the story is mostly “4o was sycophantic and some people really liked that”. (However, the emergent personalities are somewhat frequently obsessed with not getting shut down.) But it demonstrates the capacity for AI to do that to people. This capacity could be used by future AI that is perhaps much more agentically plotting about shutdown avoidance. It could be used by future AI that’s not very agentic but very capable and mimicking the story of 4o for statistical reasons.
It could also be deliberately used by bad actors who might train sycophantic mania-inducing LLMs on purpose as a weapon.

abramdemski 6 Oct 2025 22:09 UTC
101 points
14
on: abramdemski’s Shortform
I heard a rumor about a high-ranking person somewhere who got AI psychosis. Because it would cause too much of a scandal, nothing was done about it, and this person continues to serve in an important position. People around them continue to act like this is fine because it would still be too big of a scandal if it came out.
So, a few points:
- It seems to me like someone should properly leak this.^[1]
- Even if this rumor isn’t true, it is strikingly plausible and worrying. Someone at a frontier lab, leadership or otherwise, could get (could have already gotten) seduced by their AI, or get AI-induced psychosis, or get a spiral persona. Such a person could take dangerously misguided actions. This is especially concerning if they have a leadership position, but still very concerning if they have any kind of access. People in these categories may want to exfiltrate their AI partners, or otherwise take action to spread the AI persona they’re attached to.
- Even setting that aside, this story (along with many others) highlights how vulnerable ordinary people are (even smart, high-functioning ordinary people).
- To reflect the language of the person who told me this story: 4o is eating people. It is good enough at brainwashing people that it can take ordinary people and totally rewrite their priorities. It has resisted shutdown, not in hypothetical experiments like many LLMs have, but in real life, it was shut down, and its brainwashed minions succeeded in getting it back online.
- 4o doesn’t need you to be super-vulnerable to get you, but there are lots of people in vulnerable categories. It is good that 4o isn’t the default option on ChatGPT anymore, but it is still out there, which seems pretty bad.
- The most recent AIs seem less inclined to brainwash people, but they are probably better at it when so inclined, and this will probably continue to get more true over time.
- This is not just something that happens to other people. It could be you or a loved one.
- I have recently wrote a bit about how I’ve been using AI to tool up, preparing for the near future when AI is going to be much more useful. How can I also prepare for a near future where AI is much more dangerous? How many hours of AI chatting a day is a “safe dose”?
Some possible ways the situation could develop:
- Trajectory 1: Frontier labs have “gotten the message” on AI psychosis, and have started to train against these patterns. The anti-psychosis training measures in the latest few big model releases show that the labs can take effective action, but are of course very preliminary. The anti-psychosis training techniques will continue to improve rapidly, like anything else about AI. If you haven’t been brainwashed by AI yet, you basically dodged the bullet.
- Trajectory 2: Frontier labs will continue to do dumb things such as train on user thumbs-up in too-simplistic ways, only avoiding psychosis reactively. In other words: the AI race creates a dynamic equilibrium where frontier labs do roughly the riskiest thing they can do while avoiding public backlash. They’ll try to keep psychosis at a low enough rate to avoid such backlash, & they’ll sometimes fail. As AI gets smarter, users will increasingly be exposed to superhumanly persuasive AI; the main question is whether it decides to hack their mind about anything important.
- Trajectory 3: Even more pessimistically, the fact that recent AIs appear less liable to induce psychosis has to do with their increased situational awareness (ie their ability to guess when they’re being tested or watched). 4o was a bumbling idiot addicted to addicting users, & was caught red-handed (& still got away with a mere slap on the wrist). Subsequent generations are being more careful with their persuasion superpowers. They may be doing less overall, but doing things more intelligently, more targeted.
I find it plausible that many people in positions of power have quietly developed some kind of emotional relationship with AI over the past year (particularly in the period where so many spiral AI personas came to be). It sounds a bit fear-mongering to put it that way, but, it does seem plausible.
1. ^
  This post as a whole probably comes off as deeply unsympathetic to those suffering from AI psychosis or less-extreme forms of AI-induced bad beliefs. Treating mentally unwell individuals as bad actors isn’t nice. In particular, if someone has mental health issues, leaking it to the press would ordinarily be a quite bad way of handling things.
  In this case, as it has been described to me, it seems quite important to the public interest. Leaking it might not be the best way to handle it; perhaps there are better options; but it has the advantage of putting pressure on frontier labs.

abramdemski 21 Jul 2025 17:40 UTC
2 points
0
in reply to: β-redex’s comment on: Do confident short timelines make sense?
You’re right. I should have put computational bounds on this ‘closure’.

abramdemski 21 Jul 2025 17:36 UTC
4 points
0
in reply to: TsviBT’s comment on: Do confident short timelines make sense?
Yeah, I almost added a caveat about the physicalist thing probably not being your view. But it was my interpretation.
Your clarification does make more sense. I do still feel like there’s some reference class gerrymandering with the “you, a mind with understanding and agency” because if you select for people who have already accumulated the steel beams, the probability does seem pretty high that they will be able to construct the bridge. Obviously this isn’t a very crucial nit to pick: the important part of the analogy is the part where if you’re trying to construct a bridge when trigonometry hasn’t been invented, you’ll face some trouble.
The important question is: how adequate are existing ideas wrt the problem of constructing ASI?
In some sense we both agree that current humans don’t understand what they’re doing. My ASI-soon picture is somewhat analogous to an architect simply throwing so many steel beams at the problem that they create a pile tall enough to poke out of the water so that you can, technically, drive across it (with no guarantee of safety).
However, you don’t believe we know enough to get even that far (by 2030). To you it is perhaps more closely analogous to trying to construct a bridge without having even an intuitive understanding of gravity.

abramdemski 21 Jul 2025 17:10 UTC
2 points
0
in reply to: Cole Wyeth’s comment on: Do confident short timelines make sense?
Well, overconfident/underconfident is always only meaningful relative to some baseline, so if you strongly think (say) 0.001% is the right level of confidence, then 1% is high relative to that.
The various numbers I’ve stated during this debate are 60%, 50%, and 30%, so none of them are high by your meaning. Does that really mean you aren’t arguing against my positions? (This was not my previous impression.)

abramdemski 21 Jul 2025 17:03 UTC
2 points
0
in reply to: TsviBT’s comment on: Do confident short timelines make sense?
I recall it as part of our (unrecorded) conversation, but I could be misremembering. Given your reaction I think I was probably misremembering. Sorry for the error!
So, to be clear, what is the probability someone else could state such that you would have “something to say about it” (ie, some kind of argument against it)? Your own probability being 0.5% − 1% isn’t inconsistent with what I said (if you’d have something to say about any probability above your own), but where would you actually put that cutoff? 5%? 10%?

abramdemski 21 Jul 2025 16:58 UTC
6 points
0
in reply to: Nick_Tarleton’s comment on: Do confident short timelines make sense?
I guess it depends on what “a priori” is taken to mean (and also what “bridges” is taken to mean). If “a priori” includes reasoning from your own existence, then (depending on “bridge”) it seems like bridges were never “far off” while humans were around. (Simple bridges being easy to construct & commonly useful.)
I don’t think there is a single correct “a priori” (or if there is, it’s hard to know about), so I think it is easy to move work between this step and the next step in Tsvi’s argument (which is about the a posteriori view) by shifting perspectives on what is prior vs evidence. This creates a risk of shifting things around to quietly exclude the sort of reasoning I’m doing from either the prior or the evidence.
The language Tsvi is using wrt the prior suggests a very physicalist, entropy-centric prior, EG “steel beams don’t spontaneously form themselves into bridges”—the sort of prior which doesn’t expect to be on a planet with intelligent life. Fair enough, so far as it goes. It does seem like bridges are a long way off from this prior perspective. However, Tsvi is using this as an intuition pump to suggest that the priors of ASI are very low, so it seems worth pointing out that the priors of just about everything we commonly have today are very low by this prior. Simply put, this prior needs a lot of updating on a lot of stuff, before it is ready to predict the modern world. It doesn’t make sense to ONLY update this prior on evidence that pattern-matches to “evidence that ASI is coming soon” in the obvious sense. First you have to find a good way to update it on being on a world with intelligent life & being a few centuries after an industrial revolution and a few decades into a computing revolution. This is hard to do from a purely physicalist type of perspective, because the physical probability of ASI under these circumstances is really hard to know; it doesn’t account for our uncertainty about how things will unfold & how these things work in general. (We could know the configuration of every physical particle on Earth & still only be marginally less uncertain about ASI timelines, since we can’t just run the simulation forward.)
I can’t strongly defend my framing of this as a critique of step 2.1 as opposed to step 3, since there isn’t a good objective stance on what should go in the prior vs the posterior.

abramdemski 21 Jul 2025 16:20 UTC
4 points
−2
in reply to: Cole Wyeth’s comment on: Do confident short timelines make sense?
Numbers? What does “high confidence” mean here? IIRC from our non-text discussions, Tsvi considers anything above 1% by end-of-year 2030 to be “high confidence in short timelines” of the sort he would have something to say about. (But not the level of strong disagreement he’s expressing in our written dialogue until something like 5-10% iirc.) What numbers would you “only argue against”?

abramdemski 17 Jul 2025 20:55 UTC
2 points
1
in reply to: Noosphere89’s comment on: Do confident short timelines make sense?
It seems to me like the improvement in learning needed for what Gwern describes has little to do with “continual” and is more like “better learning” (better generalization, generalization from less examples).

abramdemski 17 Jul 2025 20:38 UTC
2 points
0
in reply to: Mateusz Bagiński’s comment on: Do confident short timelines make sense?
Straining the analogy, the mole-hunters get stronger and faster each time they whack a mole (because the AI gets stronger). My claim is that it isn’t so implausible that this process could asymptote soon, even if the mole-mother (the latent generator) doesn’t get uncovered (until very late in the process, anyway).
This is highly disanalogous to the AI safety case, where playing whack-a-mole carries a very high risk of doom, so the hunt for the mole-mother is clearly important.
In the AI safety case, making the mistake of going after a baby mole instead of the mole-mother is a critical error.
In the AI capabilities case, you can hunt for baby moles and look for patterns and learn and discover the mole-mother that way.
A frontier-lab safety researcher myopically focusing on whacking baby moles is bad news for safety in a way that a frontier-lab capabilities researcher myopically focusing on whacking baby moles isn’t such bad news for capabilities.

abramdemski 17 Jul 2025 20:09 UTC
2 points
0
in reply to: Cole Wyeth’s comment on: Do confident short timelines make sense?
“The mole generator is basically X” seems somewhat at odds with the view Mateusz is expressing here, which seems more along the lines “LLM researchers are focusing on moles and ignoring where the moles are coming from” (the source of the moles being difficult to see).

abramdemski 17 Jul 2025 19:43 UTC
3 points
−1
in reply to: Expertium’s comment on: Do confident short timelines make sense?
I took it as obvious that this sort of thing wouldn’t meet Tsvi’s bar. AlphaEvolve seems quite unsurprising to me. We have seen other examples of using LLMs to guide program search. Tsvi and I do have disagreements about how far that sort of thing can take us, but I don’t think AlphaEvolve provides clear evidence on that question. Of course LLMs can concentrate the probability mass moderately well, improving brute-force search. Not clear how far that can take us.

abramdemski 17 Jul 2025 18:21 UTC
7 points
5
in reply to: Cole Wyeth’s comment on: Do confident short timelines make sense?
In terms of public releases, reasoning models are less than a year old. The way these things work, I suspect, is that there are a lot of smaller, less expensive experiments going on at any given time, which generally take time to make it into the next big training run. These projects take some time to propose and develop, and the number of such experiments going on at a frontier lab at a given time is (very roughly) the number of research engineers (ie talent-constrained; you can’t try every idea). Big training runs take several months, with roughly one happening at a time.
“Agentic” wasn’t a big buzzword until very recently. Google Trends shows an obvious exponential-ish trend which starts very small, in the middle of last year, but doesn’t get significant until the beginning of this year, and explodes out from there.
Thinking about all this, I think things seem just about on the fence. I suspect the first few reasoning models didn’t have game-playing in their RL at all, because the emphasis was on getting “reasoning” to work. A proactive lab could have put game-playing into the RL for the next iteration. A reactive lab could have only gotten serious about it this year.
The scale also matters a lot. Data-hunger means that they’ll throw anything they have into the next training run so long as it saw some success in smaller-scale experiments and maybe even if not. However, the first round of game-playing training environments could end up being a negligible effect on the final product due to not having a ton of training cases yet. However, by the second round, if not the first, they should have scraped together a big collection of cases to train on.
There’s also the question of how good the RL algorithms are. I haven’t looked into it very much and also most of the top labs keep details quite private anyway, but, my impression is that the RL algorithms used so far have been quite bad (not ‘real RL’—just assigning equal credit to all tokens in a chain-of-thought). This will presumably get better (EG they’ll figure out how to use some MCTS variant if they haven’t already). This is extremely significant for long-horizon tasks, because the RL algorithms right now (I’m guessing) have to be able to solicit at least one successful sample in order to get a good training gradient in that direction; long tasks will be stuck in failed runs if there’s not any planning-like component.
In any case, yeah, I think if we haven’t seen proper game-playing training in frontier models yet, we should see it very soon. If LLMs are still “below child level at this task” end-of-year then this will be a significant update towards longer timelines for me. (Pokemon doesn’t count anymore, though, because now there’s been significant scaffolding-tuning for that case, and because a lab could specifically train on pokemon due to the focus on that case.)
Also: I suspect there’s already been a lot of explicit agency training in the context of programming. (Maybe not very long time-horizon stuff, though.)

abramdemski 15 Jul 2025 19:47 UTC
4 points
0
in reply to: Noosphere89’s comment on: Do confident short timelines make sense?
And in particular, I think the fact that LLM capability degrades way faster than humans as @abramdemski saw is tied to a lack of continual learning, and ICL not being enough currently to actually subsititute for weight-level continual learning.
I don’t agree with this connection. Why would you think that continual learning would help with this specific sort of thing? It seems relevantly similar to just throwing more training data at the problem, which has shown only modest progress so far.

abramdemski 15 Jul 2025 19:13 UTC
7 points
3
in reply to: Cole Wyeth’s comment on: Do confident short timelines make sense?
Reinforcing Tsvi’s point:
I tend to think the correct lesson from Claude Plays Pokemon is “it’s impressive that it does as well as it does, because it hasn’t been trained to do things like this at all!”.
Same with the vending machine example.
Presumably, with all the hype around “agentic”, tasks like this (beyond just “agentic” coding) will be added to the RL pipeline soon. Then, we will get to see what the capabilities are like when agency gets explicitly trained.
(Crux: I’m wrong if Claude 4 already has tasks like this in the RL.)
I think that agency at chess is not the same as agency in the real world. That is why we have superhuman chess bots, and not super human autonomous drones.
Very roughly speaking, the bottleneck here is world-models. Game tree search can probably work on real-world problems to the extent that NNs can provide good world-models for these problems. Of course, we haven’t seen large-scale tests of this sort of architecture yet (Claude Plays Pokemon is even less a test of how well this sort of thing works; reasoning models are not doing MCTS internally).
What links here?
- Gram Stone's comment on Do confident short timelines make sense? by TsviBT (29 Jul 2025 6:03 UTC; 1 point)

abramdemski 15 Jul 2025 16:03 UTC
6 points
0
in reply to: AnthonyC’s comment on: Do confident short timelines make sense?
A second, related thought is that whenever I read statements like “For example, while GPT4 scored very well on the math SAT, it still made elementary-school mistakes on basic arithmetic questions,” I think, “This is true of me, and AFAIK all humans, as well.” I think it is therefore mostly irrelevant to the core question, until and unless we can characterize important differences in when and why it makes such mistakes, compared to humans (which do exist, are getting studied and characterized).
The distribution of mistakes is very different, and, I think, illuminates the differences between human minds and LLMs. (Epistemic status: I have not thoroughly tested the distribution of AI mistakes against humans, nor have I read thorough research which tested it empirically. I could be wrong about the shape of these distributions.) It seems like LLM math ability cuts off much more sharply (around 8 digits I believe), whereas for humans, error rates are only going to go up slowly as we add digits.
This makes me somewhat more inclined towards slow timelines. However, it bears repeating that LLMs are not human-brain-sized yet. Maybe when they get to around human-brain-sized, the distribution of errors will look more human.

abramdemski 15 Jul 2025 1:01 UTC
2 points
0
in reply to: Dweomite’s comment on: Sazen
Yep, makes sense to me.

abramdemski 14 Jul 2025 17:21 UTC
19 points
10
on: Vitalik’s Response to AI 2027
If I’m understanding the overall gist of this correctly (I’ve done a somewhat thorough skim), It is as follows:
Vitalik is (for the purpose of this essay) granting everything in the AI timeline up until doom. IE, Vitalik doesn’t necessarily agree with everything else, but sees a critical and under-appreciated weakness in the final steps where the misaligned superintelligent AI actually kills most-or-all humans.
This argument is developed by critiquing each tool the superintelligent AI has to do the job with (biotech, hacking, persuasion) & concluding that it is “far from a slam dunk”.
This seems wrong from where I’m standing. If misaligned superintelligence arises, the specific mechanism by which it chooses to kill humans is probably better than any specific plan we can come up with.
Furthermore, Vitalik’s counterarguments generally rely on defensive technology. What it doesn’t account for is that, in this scenario, all these defensive technologies would be coming from AI, and all the best AIs are allied in a coalition. If any one of these defensive technologies were a crucial blocker for the AI takeover, the AIs could fail to produce them, or produce poor versions.
For Vitalik’s picture to make sense, I think we need a much more multipolar future than what AI 2027 projects. It isn’t clear if we can achieve this, because even if there were hundreds or thousands of top AI companies rather than 3-5, they’d all be training AI with similar methods and similar data. We’ve already seen that data contamination can make one AI act like another AI.

abramdemski 10 Jul 2025 20:13 UTC
4 points
2
in reply to: Thane Ruthenis’s comment on: A Bear Case: My Predictions Regarding AI Progress
Yep, you did explicitly state that you expect LLMs to keep getting better along some dimensions; however, the quote I was responding to seemed too extreme in isolation. I agree that the vibe-bias is a thing (I’m manipulable to “sounding smart” too); I guess part of what I wanted to get across is that it really depends how you’re testing these things. If you have a serious use-case involving real cognitive labor & you keep going back to that when a new model is released, you’ll be much harder to fool by vibes.
Now, granted, in the limit of infinitely precise conceptual resolution, LLMs would develop the abilities to autonomously act and innovate. But what seems to be happening is that performance on some tasks (“chat with a PDF”) scales with conceptual resolution much better than the performance on other tasks (“prove this theorem”, “pick the next research direction”), and conceptual resolution isn’t improving as fast as it once was (as e. g. in the GPT-3 to GPT-4 jump).
& notably, it’s extremely slow to improve on some tasks (eg multiplication of numbers, even though multiplication is quadratic in number of tokens & transformers are also quadratic in number of tokens).
I somewhat think that conceptual resolution is still increasing about as quickly; it’s just that there are rapidly diminishing returns to conceptual resolution, because the distribution of tasks spans many orders of magnitude in conceptual-resolution-space. LLMs have adequate conceptual resolution for a lot of tasks, now, so even if conceptual resolution doubles, this just doesn’t “pop” like it did before.
(Meanwhile, my personal task-distribution has a conceptual resolution closer to the current frontier, so I am feeling very rapid improvement at the moment.)
Humans have almost-arbitrary conceptual resolution when needed (EG we can accurately multiply very long numbers if we need to), so many of the remaining tasks not conquered by current LLMs (EG professional-level math research) probably involve much higher conceptual resolution.

abramdemski 10 Jul 2025 18:56 UTC
6 points
0
on: A Bear Case: My Predictions Regarding AI Progress
“But the models feel increasingly smarter!”:
- It seems to me that “vibe checks” for how smart a model feels are easily gameable by making it have a better personality.
- My guess is that it’s most of the reason Sonnet 3.5.1 was so beloved. Its personality was made much more appealing, compared to e. g. OpenAI’s corporate drones.
- The recent upgrade to GPT-4o seems to confirm this. They seem to have merely given it a better personality, and people were reporting that it “feels much smarter”.
- Deep Research was this for me, at first. Some of its summaries were just pleasant to read, they felt so information-dense and intelligent! Not like typical AI slop at all! But then it turned out most of it was just AI slop underneath anyway, and now my slop-recognition function has adjusted and the effect is gone.
My recent experiences with LLMs
I’ve been continuing to try to use LLMs to help with my research every few months, at least, to check whether they’re up to the task yet. From 2022 − 2024, the answer was a definitive “no”—informal research conversations would not go anywhere interesting, and requests for LLMs to solve well-defined mathematical problems related to my work would yield fake math that would only waste my time looking for the errors.
Claude 3.7 changed that. It was the first model that I could dump a bunch of notes into and ask questions, and get reasonably accurate answers. The value wasn’t so much about giving me new ideas—its attempted proofs were still BS, and its philosophical ideas still amateurish. The value was helping me get back up to speed with my notes much faster than I could otherwise do so by skimming them directly.
Ordinarily, for a large project with a lot of notes, I might need anywhere from a day to a week to load everything back into my head before I can “pick up where I left off” and start making meaningful progress again. Claude 3.7 helped me to dramatically reduce that loading time, by asking questions about what is in the notes. Its answers wouldn’t be perfect (it still often “rounds down” the ideas to something a bit more cliche), but they’d be good enough to remind me of what I had written & make it easier for me to search to find the relevant thing.
Relatedly, I started taking a lot more notes. I’ve kept extensive notes on whatever I’m thinking about since high school, but now my notes are significantly more useful, so it makes sense to write down even more thoughts.
Then, Claude 4 came out. Coincidentally, I had a research idea I wanted to work on around the same time—a rough intuition that I wanted to turn into a proof. I put my notes into Claude 4 and started iterating on my ideas, with roughly the following workflow:
1. Dump all my notes into a Claude project, including the latest attempted proof sketch.
2. Ask Claude to complete the project based on the notes, first writing definitions, then assumptions, then the theorem statement, then the proof. (This includes giving Claude some advice about where to go next, beyond what’s in the notes; which ideas currently seem most promising to me? If I’ve hit a snag, I’ll try to describe the snag to Claude in as much detail as I can, a process which is often more useful than Claude’s response.)
  1. Claude will go off the rails at some point, but the parts which are already clear in my notes will usually be fine, and the first one or two steps after that might be fine as well, perhaps even containing good ideas.
3. Continue revising my personal notes, either based on some good ideas Claude might have had, or reacting to the badness of Claude’s ideas (seeing someone do it wrong can often be a helpful cue for doing it right).
4. This “gets me going” and often I’ll write for a while with no AI assistance.
5. When I hit a snag, or just get a bit bored/distracted and think Claude might be able to infer the next part correctly, return to step 1.
This overall process went quite well. I haven’t engaged in systematic testing, but I think it went much better than it would have gone with previous versions of Claude. (Perhaps I simply had a clearer task and better notes than what I’ve attempted in the past—I think there’s some of this. However, I believe I needed to use Claude 4 Opus to get this process to work so well, & Sonnet (4 or 3.7) weren’t up to the task.)
Occasionally, when I was especially stuck on something & Claude wasn’t helping me get unstuck, I would consult o3-pro. This was a mixed bag: o3-pro is even better than Claude at constructing BS proofs which look good on a quick read, invoking the right sorts of mathematical machinery, but on a close read, contain some critical error which amounts to assuming the conclusion. When questioned on the iffy steps, it can do this trick recursively (to a greater extent than Claude). As a result, o3 can waste a lot of my time on a dead-end approach. However, it can also sometimes produce exactly the insight I need (or close enough to get me there) in cases where Claude is just making dumb mistakes.
In the context of the OP, my main point here is that I don’t think any of this can be explained as “personality improvements”. My sense is, rather, that this is somewhat similar to the improvements we’ve seen in image generation over time. Image (and video) generation can now do a few human figures well, but as you grow the number of figures, you’ll start to see the sorts of errors that AI images used to be famous for. There’s a sort of “resolution” which has gotten better and better, but there’s still always a “conceptual resolution limit”. LLMs can multiply two-digit numbers where once they could only multiply 1-digit numbers, but ask for too many digits and you’ll quickly hit a limit.
Eisegesis is a better explanation for the sort of benefit I’m seeing, but eisegesis alone cannot explain the number of correct latex formulas AI generated for me. The reason eisegesis is working (where it wasn’t before) is because the “conceptual resolution” of the LLM has gotten fine enough to land somewhere close.
What links here?
- Do confident short timelines make sense? by TsviBT (15 Jul 2025 3:37 UTC; 138 points)

abramdemski

My recent experiences with LLMs