What public figures publish and believe, in their own words.
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Top people in LLMs
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27 May
25 May
19 May
18 May
16 May
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Their words
As reasoning models and agent workflows keep more tokens around (for longer), KV-cache size, memory traffic, and attention cost quickly become the main constraints, and LLM developers are adding a growing number of architecture tricks to reduce those costs.
15 May
24 April
22 April
From one piece Designing Data-intensive Applications with Martin Kleppmann 2 beliefs, in the piece's order there
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Their words
One is that the LLMs are getting increasingly good at writing these proofs. And if we don't have to write the proof by hand as humans, it just becomes feasible to do them in situations where previously it would have not been economical.
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korrents.com
AI-written code makes formal proof necessary, because human review of all that generated code becomes the bottleneck.Their words
But also LLMs increase the need for these formal proofs because, you know, we're live coding a bunch of stuff. If we have to manually review all of that code, then that will become the bottleneck.
18 April
4 April
2 April
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Dislikedrcmnd.app
Local Deep ResearchTheir words
Its responses are, in my view, pretty bland and not very high-quality. I did a side-by-side test of asking Local Deep Research a question, then asking pi the same question (telling it to use searxng to make as many internet searches as needed), and I fed both outputs into an LLM to ask which is better.
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Usesrcmnd.app
BubblewrapTheir words
Sandboxing To keep my LLMs in check, I do most of my LLM usage from inside of a sandbox. I use bubblewrap for this.
31 March
28 March
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Their words
The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.
25 March
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Their words
One common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity.
23 March
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korrents.com
AI language models are capable of evaluating good fiction even though they cannot yet write it themselves.Their words
An LLM may not be able to write a good story yet, but it can already evaluate them.
22 March
21 March
20 March
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Their words
a swarm of agents on the internet could collaborate to improve LLMs and could potentially even like run circles around frontier labs. Like who knows, you know? Um yeah, like maybe that's even possible. Like frontier labs have a huge amount of trusted compute but the earth is much bigger and has huge amount of untrusted compute.
19 March
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Their words
Problem is, you can solve SQL injection by parameterizing your queries. You can't do that with LLMs.
18 March
14 March
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Their words
LLMs make it easy for anyone to add new features to the product. Even before AI, teams were battling feature creep, where the product becomes increasingly complex, bloated, and cluttered.
13 March
10 March
3 March
1 March
25 February
19 February
17 February
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Their words
Both estimates make me think that the concern about global energy demand for LLMs is over-blown.
8 February
1 February
27 January
25 January
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Their words
if you think about it, it sort of makes sense because the LLM is an averaging machine, right? It's predicting the most likely that's an averaging kind of a thing. And so when what you're looking for is a kind of average, it's usually pretty good. So, summarization, topics, themes. But when you're asking for like what is interesting and not average, it's actually pretty bad at it.
15 January
1 January
31 December 2025
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Lovedrcmnd.app
The Normalization of Deviance in AITheir words
One of my favourite pieces on LLM security this year is The Normalization of Deviance in AI by security researcher Johann Rehberger.
23 December 2025
20 December 2025
19 December 2025
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korrents.com
LLM intelligence should not be understood through an animal analogy; it is a different kind of entity altogetherTheir words
2025 is where I (and I think the rest of the industry also) first started to internalize the "shape" of LLM intelligence in a more intuitive sense. We're not "evolving/growing animals", we are "summoning ghosts".
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Likedrcmnd.app
Claude CodeTheir words
Claude Code (CC) emerged as the first convincing demonstration of what an LLM Agent looks like - something that in a loopy way strings together tool use and reasoning for extended problem solving.
10 December 2025
23 November 2025
From one piece Product Evals in Three Simple Steps 2 beliefs, in the piece's order there
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Their words
The benchmark is human performance, not perfection. We sometimes get requirements for 90%+ accuracy.
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korrents.com
The main advantage of an LLM evaluator over human annotators is scalability, not higher accuracy.Their words
In my opinion, the true benefit isn't higher accuracy than human annotators-it's scalability.
22 November 2025
17 November 2025
6 November 2025
3 November 2025
17 October 2025
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Their words
the reason that I think this is kind of tricky is quite subtle. And it's the fact that anytime you use an LLM to assign a reward, those LLMs are giant things with billions of parameters and they're gameable.
5 October 2025
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Their words
You know, interestingly, LM themselves are quite bad at playing chess. Like, they hallucinate moves. They look at patterns, right? They're they're very good at pattern recognition, but not so good at going super super super deep on a specific chess thing.
1 October 2025
26 September 2025
From one piece Richard Sutton – Father of RL thinks LLMs are a dead end 3 beliefs, in the piece's order there
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Their words
We don't we don't really know what information they had prior. We are we have to guess because they've been fed so much. This is one reason why they're not a good way to do science. Uh it's just so uncontrolled, so unknown.
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Their words
The scalable method is you learn from experience. Um you uh you you try things, you see what you see what works. No one no one has to tell you. First of all, you have a goal. So without a goal, uh there's no sense of right or wrong or better or worse. So large language models are trying to get by without having a goal or a sense of better or worse. That's just, you know, it's exactly starting in the wrong place.
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Their words
reinforcement learning is about understanding your world whereas large language models are about mimicking people doing what people say you should do. They're not about figuring out what to do.
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korrents.com
Almost every major figure in AI has now arrived at the critique of large language models he began making in 2019.Their words
But one by one, almost every major thinker in AI has come around to the critique of LLMs that I began presenting in 2019.
14 September 2025
13 September 2025
12 September 2025
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Their words
uh common sense meaning the ability to make inferences about what might happen uh that are reasonable guesses but that do not require you to experience that mistake and learn and learn from it in advance that's tremendously important and that's something that we basically had no idea how to do uh about 5 years ago but now uh you we can actually use LLMs and VLMs ask them questions and they will make reasonable guesses
11 September 2025
9 September 2025
2 September 2025
From one piece Why Everyone Is Wrong About AI (Including You) | Benedict Evans 3 beliefs, in the piece's order there
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Their words
I think it's actually the opposite which is that everyone's kind of using the same data which is you need such an enormous amount of generalized text that the amount that Google has or that meta has is not actually enough to move to be a kind of fundamental difference in what you can train with.
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Their words
You see this for the music now. You can generate new music. It could generate new stuff that you wouldn't know. For an LLM variance is bad. originality is is is a lower score. So what's the feedback loop for original but good?
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korrents.com
Large language models have no network effect, and remembering a user is a switching cost rather than one.Their words
there's no apparent equivalent in LLM right now there's no reason why the LMS get better because more people use them now that may come you have that open AI and people have doing memory where it remembers what else you've asked, but that seems more like a switching cost than a network effect.
9 August 2025
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Their words
One of my favorite papers about prompt injection is Design Patterns for Securing LLM Agents against Prompt Injections
2 August 2025
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Usesrcmnd.app
MochiTheir words
I initially started making Anki (well, Mochi) cards to keep track of Google DeepMind's LLM lineage—Gopher, Chinchilla, Gato, PaLM, Sparrow, Meena, LaMDA, Bard, Gemini, etc.
30 July 2025
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Their words, before
This means that working on neural networks is NOT getting us closer to AGI, except indirectly.
Their words now
It’s become very difficult for me to maintain the belief in the stupidity of ChatGPT when every time I laugh at it, it ends up ridiculing me 6 months later.
20 July 2025
22 June 2025
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korrents.com
LLM-based evaluation methods are more reliable and nuanced than traditional automated metricsTheir words
This is why model-based evaluation is increasingly popular-it offers more reliable and nuanced evals than traditional metrics.
10 June 2025
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korrents.com
The success of large language models vindicates semantic inferentialism over symbolic, rule-based approaches to AI.Their words
The success of LLMs can thus be seen as vindicating semantic inferentialism against earlier, symbolic approaches to AI that tried and failed to explicate the rules of ordinary language using formal logic.
8 June 2025
7 June 2025
From one piece A knockout blow for LLMs? 4 beliefs, in the piece's order there
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Their words
Worse, as the latest Apple papers shows, LLMs may well work on your easy test set (like Hanoi with 4 discs) and seduce you into thinking it has built a proper, generalizable solution when it does not.
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Their words
What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms.
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Their words
But this particular approach has limits that are clearer by the day.
+ 1 more
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korrents.com
Anyone who thinks large language models are a direct route to transformative AGI is kidding themselves.Their words
But anybody who thinks LLMs are a direct route to the sort AGI that could fundamentally transform society for the good is kidding themselves.
5 June 2025
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Their words
the text embeddings across LLMs appear to largely converge on a "universal geometry" despite differing architectures, parameter counts, and training sets.
4 June 2025
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Their words, before
The problem is that the conversational interface is potent and that the AI is trained on a lot of human text input which unfortunately is probably enough to do real damage if that conversational interface is hooked up with something that has real world consequences.
Their words now
While all this is happening, I’ve found myself reflecting a lot on what AI means to the world and I am becoming increasingly optimistic about our future. It’s obvious now that we’re undergoing a tremendous shift.
2 June 2025
From one piece My AI Skeptic Friends Are All Nuts 2 beliefs · fly.io
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Their words
All progress on LLMs could halt today, and LLMs would remain the 2nd most important thing to happen over the course of my career.
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Their words
LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious. LLMs drastically reduce the number of things you’ll ever need to Google.
31 May 2025
25 May 2025
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Lovedrcmnd.app
Anthropic prompt engineering documentationTheir words
I still think Anthropic have the best prompting documentation of any LLM provider.
6 May 2025
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Their words
Unless you’re an extreme power user, asking AI questions every day is still a rounding error on your total electricity footprint.
What's the carbon footprint of using ChatGPT?hannahritchie.substack.com
1 May 2025
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korrents.com
Large language models cannot reliably self-correct their own mistakes without external feedback.Their words
this self-correction capability turns out to not exist intrinsically among LLMs and does not easily work out of the box, due to various failure modes
23 April 2025
20 April 2025
8 April 2025
31 March 2025
22 March 2025
From one piece ThePrimeagen: Programming, AI, ADHD, Productivity, Addiction, and God | Lex Fridman Podcast #461 3 beliefs, in the piece's order there
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Their words
I think there's this whole idea, I call it a denial of attention. I think there's an entire attack vector that's going to be happening. We're using LLMs to generate fake bug reports, fake all these things to just actually effectively to demotivate and hurt open source maintainers.
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korrents.com
Without hard skills of your own, your ceiling as a programmer is whatever the LLMs happen to be capable of.Their words
And so if you don't have those hard skills and you're not ultimately the driver at the end of the day, you're going to really find some hard times, and your ability to progress will be directly bound to how good the LLMs are.
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Their words
It doesn't actually care about the craft. But when you work with an intern or you work with somebody else, they care. When they factor something, they actually go over and go, "Oh yeah, this is actually kind of bad. I'm going to come back to that." They finish this, they go back over here and they make this even better. They actually care about the thing itself.
30 January 2025
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Likedrcmnd.app
llmTheir words
This is a command line tool with plenty of plugins that lets you prompt different models. Think of it as a command-line version of Open WebUI. It’s particularly useful for quick scripting and basic automation.
27 January 2025
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korrents.com
AI tooling and interface design lags behind the underlying models' actual capabilities.Their words
Overall, though, this experience reinforced my belief that the tooling and interface design around LLMs is lagging way behind the actual capabilities, and is an area of active experimentation and development, even aside from any future model improvements.
6 January 2025
From one piece How I program with LLMs 2 beliefs, in the piece's order there
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Their words
One thing humans appear to be much better than LLMs at (as of January 2025) is not getting distracted.
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Their words
So I foresee a world with far more specialized code, with fewer generalized packages, and more readable tests.
31 December 2024
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Their words
There’s still plenty to worry about with respect to the environmental impact of the great AI datacenter buildout, but a lot of the concerns over the energy cost of individual prompts are no longer credible.
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Recommendsrcmnd.app
The Rattle BagTheir words
The Rattle Bag edited by Seamus Heaney and Ted Hughes - In an age of LLM-generated poetry and machine learning curation (even coming from yours truly sometimes), it's refreshing to have real people who love poetry curate it and show you what you need to read to touch grass.
22 December 2024
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korrents.com
Large language models will not get to real agency, because a mind has to be embedded in a world through a body.Their words
But they said, "Look, in the end, actually the only way to actually get a real brain in the VAT is actually to have a brain in a body." And it could be a robot body, but you still need a brain in the body. So I don't think LLMs will get there because they can't. You really need to be embedded in a world, at least that's the E-four idea.
20 December 2024
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Their words, before
This "memorize, fetch, apply" paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.)
Their words now
OpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.
29 October 2024
31 August 2024
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korrents.com
A language model is not using language at all, because language requires an intention to communicate.Their words
But a large language model is not a writer; it’s not even a user of language. Language is, by definition, a system of communication, and it requires an intention to communicate.
29 July 2024
7 July 2024
From one piece Extrinsic Hallucinations in LLMs 2 beliefs, in the piece's order there
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korrents.com
Using supervised fine-tuning to teach a language model new knowledge risks increasing its hallucination rate.Their words
These empirical results from Gekhman et al. (2024) point out the risk of using supervised fine-tuning for updating LLMs' knowledge.
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korrents.com
A language model avoids hallucinating only by being factual and admitting when it does not know the answer.Their words
To avoid hallucination, LLMs need to be (1) factual and (2) acknowledge not knowing the answer when applicable.
19 June 2024
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Their words
In Google, even though we call it 10 blue links, you get annoyed if you don’t even have the right link in the first three or four. The eye is so tuned to getting it right. LLMs are fine. You get the right link maybe in the 10th or ninth. You feed it in the model. It can still know that that was more relevant than the first.
13 June 2024
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Their words
I think large language models are kind of like the genetic system for language in some sense, where it’s allowing an archiving that’s highly dynamic.
22 April 2024
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Their words
What a large language model is trying to do is to predict text. That’s what it does. And it is leveraging the fact that we human beings for very good evolutionary biology reasons, attribute intentionality and intelligence and agency to things that act like human beings.
15 April 2024
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korrents.com
Retrieval-augmented generation is the most practical approach for reducing LLM hallucinations.Their words
Retrieval-augmented generation (RAG; Lewis et al., 2020), which conditions on the LLM's generation on retrieved documents is the most practical paradigm IMO.
19 December 2023
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Their words
recent LLMs are reaching the limits of text data online and repeating data eventually leads to diminishing returns
14 December 2023
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Their words
It's interesting to me that large language models in their current form are not inventions, they're discoveries. The telescope was an invention, but looking through it at Jupiter, knowing that it had moons, was a discovery.
11 December 2023
5 December 2023
From one piece EMNLP 2023 Primer 3 beliefs, in the piece's order there
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Their words
In light of the increasing number of closed-source LLMs, it is important to continue to promote an open culture of sharing knowledge, data, and software, from which the NLP community has benefited greatly.
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Their words
LLMs are still limited in non-English settings and that making LLMs more multilingual is an important direction.
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Their words
Full fine-tuning of LLMs has become prohibitive and requires parameter-efficient methods instead.
3 December 2023
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Their words
Large language models are more or less generalizations of stuff that we have. There’s still breakthroughs in AI waiting to happen, and maybe they are happening and maybe they’ll be good, maybe not, but that’s not quite the same.
25 October 2023
13 September 2023
29 June 2023
From one piece George Hotz: Tiny Corp, Twitter, AI Safety, Self-Driving, GPT, AGI & God | Lex Fridman Podcast #387 2 beliefs, in the piece's order there
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Their words
I think things like MuZero and AlphaGo are so much more impressive because these things are playing beyond the highest human level. The language models are writing middle school level essays and people are like, wow, it’s a great essay.
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Their words
I think future LLMs are going to be smaller, but are going to run looping on themselves and are going to have retrieval systems. And the thing about using a retrieval system is you can cite sources, explicitly.
23 June 2023
1 May 2023
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korrents.com
Chatbots are a terrible interface for large language models, because a text box has no affordances.Their words
Hopefully I've convinced you that chatbots are a terrible interface for LLMs. Or, at the very least, that we can add controls, information, and affordances to our chatbot interfaces to make them more usable.
15 March 2023
4 March 2022
23 February 2022
Nothing matches.
What is a korrent?
A korrent is a belief a person has stated in their own words: one sentence stating the claim, backed by a quote and a source, kept at korrents.com.
Under a name here, the quoted block is what they actually said. The korrent beneath it is the claim those words support, in korrents' wording — tap it to see the record, its source, and who else holds it.
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