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← “Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed.
17 connected korrents · 14 moments on record from 2 Sept 2023 to 31 Aug 2026.
Everything filed under education
education
Everything filed under scaling laws
scaling laws
Everything filed under reinforcement learning
reinforcement learning 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: “Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed. “Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed. Last stated a month ago
28 Jul 2026
BS
Blake Scholl — holds since 2026-07-28 — tap for who they are
Same subject: Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson. — tap to centre the map on it Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson. Last stated 6 months ago
11 Mar 2026
SY
Steve Yegge — holds since 2026-03-11 — tap for who they are
Same subject: You cannot learn from work you did not do yourself. — tap to centre the map on it You cannot learn from work you did not do yourself. Last stated 2 months ago
22 Jul 2026
SY
Scott H. Young — holds since 2026-07-22 — tap for who they are
Same subject: Merging back what a copy of yourself learned elsewhere is dangerous: the knowledge you pull in can carry hidden goals and take you over. — tap to centre the map on it Merging back what a copy of yourself learned elsewhere is dangerous: the knowledge you pull in can carry hidden goals and take you over. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Work smarter, not harder is bad advice, because the only way to find out what smart looks like is to work very hard first. — tap to centre the map on it Work smarter, not harder is bad advice, because the only way to find out what smart looks like is to work very hard first. Last stated a year ago
22 Mar 2025
TH
ThePrimeagen — holds since 2025-03-22 — tap for who they are
Same subject: Knowledge that lives only in a book or a machine is not knowledge you have. — tap to centre the map on it Knowledge that lives only in a book or a machine is not knowledge you have. Last stated 10 months ago
18 Nov 2025
SY
Scott H. Young — holds since 2025-11-18 — tap for who they are
Same subject: AI helps exactly as far as the work is predictable and well documented, and stops helping on anything cutting edge. — tap to centre the map on it AI helps exactly as far as the work is predictable and well documented, and stops helping on anything cutting edge. Last stated a year ago
22 Mar 2025
TH
ThePrimeagen — holds since 2025-03-22 — tap for who they are
Same subject: A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems. — tap to centre the map on it A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems. Last stated 6 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — tap for who they are
Same subject: The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. — tap to centre the map on it The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: A puzzle whose categories can only be seen by the broadly educated is elitist by construction. — tap to centre the map on it A puzzle whose categories can only be seen by the broadly educated is elitist by construction. Last stated 3 years ago
2 Sept 2023
RK
Raph Koster — holds since 2023-09-02 — tap for who they are
Same subject: A school should be judged on how fast its students are learning, not on how much they already know when measured. — tap to centre the map on it A school should be judged on how fast its students are learning, not on how much they already know when measured. Last stated a week ago
31 Aug 2026
JL
Joe Liemandt — holds since 2026-08-31 — tap for who they are
Same subject: A worked example whose scaffolding is progressively removed teaches faster than making a child figure the problem out unaided. — tap to centre the map on it A worked example whose scaffolding is progressively removed teaches faster than making a child figure the problem out unaided. Last stated a week ago
31 Aug 2026
JL
Joe Liemandt — holds since 2026-08-31 — 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 2 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
Same subject: After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. — tap to centre the map on it After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. — tap to centre the map on it A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. Last stated 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. — tap to centre the map on it Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. — tap to centre the map on it Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
At the centre
“Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed.
Holds BS Blake SchollRead this korrent →
Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson.
Holds SY Steve Yegge
You cannot learn from work you did not do yourself.
Holds SY Scott H. Young
Merging back what a copy of yourself learned elsewhere is dangerous: the knowledge you pull in can carry hidden goals and take you over.
Holds RS Richard Sutton
Work smarter, not harder is bad advice, because the only way to find out what smart looks like is to work very hard first.
Holds TH ThePrimeagen
Knowledge that lives only in a book or a machine is not knowledge you have.
Holds SY Scott H. Young
AI helps exactly as far as the work is predictable and well documented, and stops helping on anything cutting edge.
Holds TH ThePrimeagen
A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems.
Holds TT Terence Tao
The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out.
Holds
A puzzle whose categories can only be seen by the broadly educated is elitist by construction.
Holds RK Raph Koster
A school should be judged on how fast its students are learning, not on how much they already know when measured.
Holds JL Joe Liemandt
A worked example whose scaffolding is progressively removed teaches faster than making a child figure the problem out unaided.
Holds JL Joe Liemandt
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds NB Noam Brown
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 DP Dylan Patel
After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.
Holds JH Jensen Huang
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago.
Holds
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds