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← Orchestrating thousands of agents is a new form of test-time compute,…
8 connected korrents · 8 moments on record from 25 Nov 2025 to 26 Aug 2026. Nearly all of them are about scaling laws .
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benchmarks Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: Orchestrating thousands of agents is a new form of test-time compute, a fourth lever alongside network size, training data and training flops. Orchestrating thousands of agents is a new form of test-time compute, a fourth lever alongside network size, training data and training flops. Last stated a month ago 27 Jul 2026 · a month ago
BC
Boris Cherny — holds since 2026-07-27 — 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… Last stated 2 months ago 26 Jun 2026 · 2 months ago
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: Model comparisons understate real progress, because benchmark tables do not control for how much test-time compute each answer used. — tap to centre the map on it Model comparisons understate real progress, because benchmark tables do not control for how much test-time… Last stated 2 months ago 26 Jun 2026 · 2 months ago
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… Last stated 6 months ago 13 Mar 2026 · 6 months ago
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… Last stated 6 months ago 23 Mar 2026 · 6 months ago
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: Bet on a system that is maximally learned and minimally constrained, and add structure only where it improves the scaling laws. — tap to centre the map on it Bet on a system that is maximally learned and minimally constrained, and add structure only where it… Last stated a month ago 3 Aug 2026 · a month ago
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: Current AI techniques are four to six orders of magnitude away from optimal in data efficiency and test-time compute efficiency. — tap to centre the map on it Current AI techniques are four to six orders of magnitude away from optimal in data efficiency and test-time… Last stated a month ago 7 Aug 2026 · a month ago
FC
François Chollet — holds since 2026-08-07 — tap for who they are
Same subject: Large language models could still plateau, and that possibility should be held open even though no evidence of it has appeared. — tap to centre the map on it Large language models could still plateau, and that possibility should be held open even though no evidence… Last stated 2 weeks ago 26 Aug 2026 · 2 weeks ago
DH
David Heinemeier Hansson — holds since 2026-08-26 — tap for who they are
Same subject: Models resemble each other because pre-training is the same everywhere; what differentiates labs now is RL and post-training. — tap to centre the map on it Models resemble each other because pre-training is the same everywhere; what differentiates labs now is RL… Last stated 9 months ago 25 Nov 2025 · 9 months ago
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are