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embeddingsdimensiondiminishing
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16 September
8 September
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Their words
We are at a point metallurgically where video games were at the start of the last console generation-upgrades aren't necessarily noticeable based on specs alone. And noticeable improvements require a huge increase in resources.
7 September
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korrents.com
Open source wins because agents can train on it and use it efficiently, as with Blender beating its competitors.Their words
Blender beats all its competitors because it's optimal in the most important dimension: agents can train on it and use it efficiently. OSS wins.
6 September
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korrents.com
Chain-of-thought monitoring is getting less reliable, not more, as models grow more capable.Their words
However, unfortunately our evaluations indicate our ability to rely on CoT monitoring is progressively diminishing.
3 September
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korrents.com
AGI is not a finish line to be crossed but a smear with width, and we are already standing on it.Their words
some bits of AI are way across the finish line, some bits are like maybe in the middle of it. Who knew the line had dimension? The finish line is actually this sort of smear. And I think the best answer you can give is like we're on the smear.
26 August
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Their words
This is complete nonsense, a shame that The Economist has been taken in by it.
24 August
17 August
9 August
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korrents.com
New technologies raise the ceiling for skilled practitioners instead of diminishing them.Their words
What actually happened each time a new technology was introduced is that the ceiling went higher.
7 August
18 July
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korrents.com
Pushing an LLM's reasoning budget higher eventually yields diminishing returns and becomes uneconomical.Their words
This saturation can be seen more clearly for the GPT 5.6 Sol model, which also shows that increasing reasoning budgets can become uneconomical at some point.
Controlling Reasoning Effort in LLMsmagazine.sebastianraschka.com
15 July
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Their words
But it's basically this idea that like the only thing that made claude code good was reinforcement learning. And the dimension along which it got good was like we made a model. We trained the model and the harness together. And so the model got really good at calling the specific tools in that harness.
21 June
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korrents.com
Generative AI systems have no built-in drive toward truth and accuracy, unlike people or institutions.Their words
Generative AI has no internal, designed momentum towards truth and accuracy (beyond the absurdly diminishing returns of energy-hungry multi-layered LLMs), but a person or an institution can (and should).
4 June
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Their words
So, what you need is that basically demand to be bounded, like a hard bound, not even like a soft sort of like diminishing sensitivity. You need for them to eventually say, "I've had enough. I don't want to spend any more money." And for that money to not enter as investment.
2 June
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I believe that one dimension of this sadness or demoralization can be attributed to the simple fact that we are increasingly invited to outsource a class of activities that grant us a measure of satisfaction, accomplishment, and purpose.
16 May
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korrents.com
Larger language models already have enough capacity that extra per-layer embedding tricks add little benefit to them.Their words
However, larger models already have sufficient capacity where these extra embeddings may not help that much.
7 April
From one piece Michael Nielsen – Why aliens will have a different tech stack than us 2 beliefs, in the piece's order there
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Their words
if somebody, you know, is standing behind the dessert table and is replenishing, restocking the desserts and keeps kind of you know, adding adding new ones in, it may turn out that you know, a little bit later a much better desserts appear.
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Their words
If you're not changing it, if there's some kind of stagnation there, if you're not changing those external sort of circumstances, yes, like you may start to get sort of diminishing returns again. But that doesn't mean there's anything intrinsic about the situation.
8 February
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korrents.com
Local AI models will eventually catch up to frontier models once frontier progress hits diminishing returns.Their words
At some point frontier models will face diminishing returns, local models will catch up, and we will be done being beholden to frontier models.
23 January
17 January
25 November 2025
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Their words
I think I think there'll definitely be there'll be diminishing returns because you want you want people who think differently rather than the same. I think that if they were literal copies of me, I'm not sure how much more incremental value you'd get.
21 September 2025
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Their words
And I am a very big believer that every strength is its own weakness and every weakness is a strength. There's no such thing as you're going to somehow, you know, get every dimension to be 100%.
17 September 2025
11 September 2025
17 July 2025
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.
21 July 2024
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Team leadership covers a huge array of things-as you can see from how long this post is-and trying to find someone who can be great at all of them is often a unicorn hunt.
19 June 2024
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korrents.com
Vector embeddings will not solve search: a decades-old term-frequency algorithm still beats most of them at ranking.Their words
There is an algorithm called BM25 precisely for this, which is a more sophisticated version of TF-IDF. TF-IDF is term frequency times inverse document frequency, a very old-school information retrieval system that just works actually really well even today. And BM25 is a more sophisticated version of that, that is still beating most embeddings on ranking.
19 December 2023
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recent LLMs are reaching the limits of text data online and repeating data eventually leads to diminishing returns
1 September 2023
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Their words
And the way of being in the physical world today is really not in tune with the time dimension of the natural world at all, and that needs to change.
28 July 2022
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