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← Games are particularly rich and controlled environments uniquely…
17 connected korrents · 12 moments from 17 Jul 2025 to 22 Sept 2026.
Everything filed under world models
world models
Everything filed under recursive self-improvement
recursive self-improvement
Everything filed under scaling laws
scaling laws
Everything filed under reinforcement learning
reinforcement learning
Everything filed under LLMs
LLMs 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: Games are particularly rich and controlled environments uniquely well-suited for evaluating rapid world model induction in AI Games are particularly rich and controlled environments uniquely well-suited for evaluating rapid world model induction in AI Last stated a year ago
17 Jul 2025
FC
François Chollet — holds since 2025-07-17 — tap for who they are
Same subject: An adaptive agent's world model cannot be a static representation learned once and fixed — tap to centre the map on it An adaptive agent's world model cannot be a static representation learned once and fixed Last stated a year ago
17 Jul 2025
FC
François Chollet — holds since 2025-07-17 — tap for who they are
Same subject: Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop. — tap to centre the map on it Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop. Last stated 3 months ago
10 Aug 2026
FC
François Chollet — holds since 2026-08-10 — tap for who they are
Same subject: Current AI evaluation of world models is too narrow, favoring static representations learned from massive datasets over efficient learning through interaction — tap to centre the map on it Current AI evaluation of world models is too narrow, favoring static representations learned from massive datasets over efficient learning through interaction Last stated a year ago
17 Jul 2025
FC
François Chollet — holds since 2025-07-17 — tap for who they are
Same subject: Human adaptability is fundamentally linked to the efficient construction and refinement of internal world models — tap to centre the map on it Human adaptability is fundamentally linked to the efficient construction and refinement of internal world models Last stated a year ago
17 Jul 2025
FC
François Chollet — holds since 2025-07-17 — tap for who they are
Same subject: Rapid world model induction will be critical for achieving robust, general AI that functions in complex, fast-changing real and human worlds — tap to centre the map on it Rapid world model induction will be critical for achieving robust, general AI that functions in complex, fast-changing real and human worlds Last stated a year ago
17 Jul 2025
FC
François Chollet — holds since 2025-07-17 — tap for who they are
Same subject: The companies that already control the largest troves of real-world video data will be first to build successful world models. — tap to centre the map on it The companies that already control the largest troves of real-world video data will be first to build successful world models. Last stated 3 months ago
10 Aug 2026
KK
Kevin Kelly — holds since 2026-08-10 — tap for who they are
Same subject: The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. — tap to centre the map on it The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: There is unmet demand for a single, well-integrated local AI model experience, rather than a patchwork of local AI components. — tap to centre the map on it There is unmet demand for a single, well-integrated local AI model experience, rather than a patchwork of local AI components. Last stated 5 months ago
14 May 2026
SS
Salvatore Sanfilippo — holds since 2026-05-14 — tap for who they are
Same subject: A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute. — tap to centre the map on it A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute. Last stated a month ago
22 Sept 2026
TC
Tyler Cowen — holds since 2026-09-22 — tap for who they are
Same subject: AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started. — tap to centre the map on it AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started. Last stated 3 months ago
3 Aug 2026
CP
Chamath Palihapitiya — holds since 2026-08-03 — tap for who they are
Same subject: AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033. — tap to centre the map on it AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. — tap to centre the map on it A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. Last stated 2 months ago
26 Aug 2026
HK
Henrik Karlsson — holds since 2026-08-26 — tap for who they are
Same subject: A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. — tap to centre the map on it A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — 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 11 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — 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 11 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 4 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 7 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
At the centre
Games are particularly rich and controlled environments uniquely well-suited for evaluating rapid world model induction in AI
Holds Read this korrent →
An adaptive agent's world model cannot be a static representation learned once and fixed
Holds
Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop.
Holds
Current AI evaluation of world models is too narrow, favoring static representations learned from massive datasets over efficient learning through interaction
Holds
Human adaptability is fundamentally linked to the efficient construction and refinement of internal world models
Holds
Rapid world model induction will be critical for achieving robust, general AI that functions in complex, fast-changing real and human worlds
Holds
The companies that already control the largest troves of real-world video data will be first to build successful world models.
Holds
The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture.
Holds
There is unmet demand for a single, well-integrated local AI model experience, rather than a patchwork of local AI components.
Holds
A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute.
Holds
AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started.
Holds
AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033.
Holds
A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score.
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
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