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← Always try your stupid ideas when the experiment is cheap and…
17 connected korrents · 18 moments on record from 1 Oct 2023 to 7 Aug 2026.
Everything filed under reinforcement learning
reinforcement learning
Everything filed under robotics
robotics
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: Always try your stupid ideas when the experiment is cheap and reversible, because the one that works has no competition. Always try your stupid ideas when the experiment is cheap and reversible, because the one that works has no competition. Last stated 2 months ago
1 Jul 2026
KB
Kent Beck — holds since 2026-07-01 — tap for who they are
Same subject: Faced with a problem none of your techniques fit, try anything, the stupider the better, because the way it fails is the clue. — tap to centre the map on it Faced with a problem none of your techniques fit, try anything, the stupider the better, because the way it fails is the clue. Last stated a year ago
14 Jun 2025
TT
Terence Tao — holds since 2025-06-14 — tap for who they are
Same subject: Asking what the smallest experiment is that would settle a claim to your own satisfaction has become a thousand times more valuable in the last year. — tap to centre the map on it Asking what the smallest experiment is that would settle a claim to your own satisfaction has become a thousand times more valuable in the last year. Last stated 5 months ago
7 Apr 2026
KB
Kent Beck — holds since 2026-04-07 — tap for who they are
Same subject: The ideas that end up mattering sound stupid at first, because that is where the value is locked up; copying the best with a tweak never works. — tap to centre the map on it The ideas that end up mattering sound stupid at first, because that is where the value is locked up; copying the best with a tweak never works. Last stated a month ago
26 Jul 2026
DH
David Heinemeier Hansson — holds since 2026-07-26 — tap for who they are
Same subject: Some ideas are worth funding with fifty million dollars or with nothing, and never with five million, because underfunding buys you an ambiguous outcome instead of an answer. — tap to centre the map on it Some ideas are worth funding with fifty million dollars or with nothing, and never with five million, because underfunding buys you an ambiguous outcome instead of an answer. Last stated a month ago
7 Aug 2026
MH
Max Hodak — holds since 2026-08-07 — tap for who they are
Same subject: The value of an ambitious experiment target is not hitting it but the conversation about what would have to be true to hit it. — tap to centre the map on it The value of an ambitious experiment target is not hitting it but the conversation about what would have to be true to hit it. Last stated 11 months ago
5 Oct 2025
AC
Albert Cheng — holds since 2025-10-05 — tap for who they are
Same subject: Decisions split into reversible two-way doors and irreversible one-way doors, and only the one-way doors deserve slow, deliberate scrutiny. — tap to centre the map on it Decisions split into reversible two-way doors and irreversible one-way doors, and only the one-way doors deserve slow, deliberate scrutiny. Last stated 3 years ago
14 Dec 2023
JB
Jeff Bezos — holds since 2023-12-14 — tap for who they are
Same subject: After twenty years of doing something one way, try the opposite for a while to test whether the preferences you cemented are still valid. — tap to centre the map on it After twenty years of doing something one way, try the opposite for a while to test whether the preferences you cemented are still valid. Last stated a year ago
1 Jul 2025
DH
David Heinemeier Hansson — holds since 2025-07-01 — 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: 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: 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
Same subject: Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one. — tap to centre the map on it Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one. Last stated 2 years ago
3 Feb 2025
NL
Nathan Lambert — holds since 2025-02-03 — 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 highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas. — tap to centre the map on it A highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas. Last stated 3 years ago
1 Oct 2023
TC
Tyler Cowen — holds since 2023-10-01 — tap for who they are
Same subject: A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it. — tap to centre the map on it A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it. Last stated a year ago
6 Apr 2025
MH
Molson Hart — holds since 2025-04-06 — tap for who they are
Same subject: A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. — tap to centre the map on it A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. Last stated a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
At the centre
Always try your stupid ideas when the experiment is cheap and reversible, because the one that works has no competition.
Holds KB Kent BeckRead this korrent →
Faced with a problem none of your techniques fit, try anything, the stupider the better, because the way it fails is the clue.
Holds TT Terence Tao
Asking what the smallest experiment is that would settle a claim to your own satisfaction has become a thousand times more valuable in the last year.
Holds KB Kent Beck
The ideas that end up mattering sound stupid at first, because that is where the value is locked up; copying the best with a tweak never works.
Holds
Some ideas are worth funding with fifty million dollars or with nothing, and never with five million, because underfunding buys you an ambiguous outcome instead of an answer.
Holds MH Max Hodak
The value of an ambitious experiment target is not hitting it but the conversation about what would have to be true to hit it.
Holds AC Albert Cheng
Decisions split into reversible two-way doors and irreversible one-way doors, and only the one-way doors deserve slow, deliberate scrutiny.
Holds JB Jeff Bezos
After twenty years of doing something one way, try the opposite for a while to test whether the preferences you cemented are still valid.
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
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds
Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one.
Holds NL Nathan Lambert
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 highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas.
Holds TC Tyler Cowen
A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it.
Holds MH Molson Hart
A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases.
Holds DD Dmitri Dolgov