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← Taking feedback from an LLM does not bleach out writing style the way model-generated text does.
17 connected korrents · 16 moments on record from 14 Mar 2019 to 18 Sept 2026.
Everything filed under LLMs
LLMs
Everything filed under measuring intelligence
measuring intelligence
Everything filed under open source
open source
Everything filed under scaling laws
scaling laws Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: Taking feedback from an LLM does not bleach out writing style the way model-generated text does. Taking feedback from an LLM does not bleach out writing style the way model-generated text does. Last stated yesterday
18 Sept 2026
TP
Thomas Ptacek — holds since 2026-09-18 — tap for who they are
Same subject: A computer scientist refusing to find LLMs interesting is like a geneticist ignoring Jurassic Park. — tap to centre the map on it A computer scientist refusing to find LLMs interesting is like a geneticist ignoring Jurassic Park. Last stated yesterday
18 Sept 2026
SW
Simon Willison — holds since 2026-09-18 — tap for who they are
Same subject: A language model asked to summarize its own system prompt risks that prompt's content biasing the summary it produces. — tap to centre the map on it A language model asked to summarize its own system prompt risks that prompt's content biasing the summary it produces. Last stated 2 weeks ago
2 Sept 2026
SW
Simon Willison — holds since 2026-09-02 — tap for who they are
Same subject: A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world. — tap to centre the map on it A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world. Last stated 12 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: A language model cannot produce work that is both original and good, because nothing gives it a score for originality. — tap to centre the map on it A language model cannot produce work that is both original and good, because nothing gives it a score for originality. Last stated a year ago
2 Sept 2025
BE
Benedict Evans — holds since 2025-09-02 — tap for who they are
Same subject: A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. — tap to centre the map on it A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. Last stated a year ago
7 Jun 2025
GM
Gary Marcus — holds since 2025-06-07 — tap for who they are
Same subject: A language model is not using language at all, because language requires an intention to communicate. — tap to centre the map on it A language model is not using language at all, because language requires an intention to communicate. Last stated 2 years ago
31 Aug 2024
TC
Ted Chiang — holds since 2024-08-31 — tap for who they are
Same subject: A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. — tap to centre the map on it A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. Last stated 2 years ago
22 Apr 2024
SC
Sean Carroll — holds since 2024-04-22 — tap for who they are
Same subject: A large language model is the genetic system for language: it stores the dynamics of a structure that until now lived only across human brains. — tap to centre the map on it A large language model is the genetic system for language: it stores the dynamics of a structure that until now lived only across human brains. Last stated 2 years ago
13 Jun 2024
SW
Sara Walker — holds since 2024-06-13 — tap for who they are
Same subject: A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. — tap to centre the map on it A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. Last stated 4 days ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: Every generation's test for machine intelligence is whatever machines cannot do yet, so in twenty years the question will be whether it can reproduce. — tap to centre the map on it Every generation's test for machine intelligence is whatever machines cannot do yet, so in twenty years the question will be whether it can reproduce. Last stated 3 years ago
29 Jun 2023
GH
George Hotz — holds since 2023-06-29 — tap for who they are
Same subject: Having a goal is the essence of intelligence; a system that only predicts is a behaving system, not an intelligent one. — tap to centre the map on it Having a goal is the essence of intelligence; a system that only predicts is a behaving system, not an intelligent one. Last stated 12 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: A business making hundreds of millions from an open source project owes it money or contribution, and can be refused its trademarks if it gives neither. — tap to centre the map on it A business making hundreds of millions from an open source project owes it money or contribution, and can be refused its trademarks if it gives neither. Last stated 2 years ago
26 Sept 2024
MM
Matt Mullenweg — holds since 2024-09-26 — tap for who they are
Same subject: A company that will not accept a project’s terms is free to go and use a more permissive project instead. — tap to centre the map on it A company that will not accept a project’s terms is free to go and use a more permissive project instead. Last stated 2 years ago
26 Sept 2024
MM
Matt Mullenweg — holds since 2024-09-26 — tap for who they are
Same subject: A contribution is a cost to the maintainer, not a gift: it is taking part, not merely using the code, that consumes a project’s scarcest resource. — tap to centre the map on it A contribution is a cost to the maintainer, not a gift: it is taking part, not merely using the code, that consumes a project’s scarcest resource. Last stated 5 years ago
17 Mar 2022
FV
Filippo Valsorda — holds since 2022-03-17 — tap for who they are
NA
Nadia Asparouhova — holds since 2019-03-14 — tap for who they are
Same subject: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. — tap to centre the map on it "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 10 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 3 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
At the centre
Taking feedback from an LLM does not bleach out writing style the way model-generated text does.
Holds Read this korrent →
A computer scientist refusing to find LLMs interesting is like a geneticist ignoring Jurassic Park.
Holds
A language model asked to summarize its own system prompt risks that prompt's content biasing the summary it produces.
Holds
A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world.
Holds
A language model cannot produce work that is both original and good, because nothing gives it a score for originality.
Holds
A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted.
Holds
A language model is not using language at all, because language requires an intention to communicate.
Holds
A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human.
Holds
A large language model is the genetic system for language: it stores the dynamics of a structure that until now lived only across human brains.
Holds
A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model.
Holds
Every generation's test for machine intelligence is whatever machines cannot do yet, so in twenty years the question will be whether it can reproduce.
Holds
Having a goal is the essence of intelligence; a system that only predicts is a behaving system, not an intelligent one.
Holds
A business making hundreds of millions from an open source project owes it money or contribution, and can be refused its trademarks if it gives neither.
Holds
A company that will not accept a project’s terms is free to go and use a more permissive project instead.
Holds
A contribution is a cost to the maintainer, not a gift: it is taking part, not merely using the code, that consumes a project’s scarcest resource.
Holds
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
Holds
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds