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← Tiny models trained for narrow tasks can perform excellently when…
17 connected korrents · 16 moments from 15 Jul 2021 to 14 Oct 2026.
Everything filed under recursive self-improvement
recursive self-improvement
Everything filed under AI and science
AI and science
Everything filed under OpenAI
OpenAI
Everything filed under world models
world models
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: Tiny models trained for narrow tasks can perform excellently when paired with a strong self-improving recursive flywheel Tiny models trained for narrow tasks can perform excellently when paired with a strong self-improving recursive flywheel Last stated 2 months ago
1 Sept 2026
TL
Tobias Lütke — holds since 2026-09-01 — tap for who they are
Same subject: Recursive self-improvement only works if the base model is already capable enough to improve its own mechanism. — tap to centre the map on it Recursive self-improvement only works if the base model is already capable enough to improve its own mechanism. Last stated 3 months ago
4 Jul 2026
LW
Lilian Weng — holds since 2026-07-04 — tap for who they are
Same subject: Recursive self-improvement will increase AI capability very quickly from here. — tap to centre the map on it Recursive self-improvement will increase AI capability very quickly from here. Last stated 2 months ago
3 Aug 2026
CP
Chamath Palihapitiya — holds since 2026-08-03 — tap for who they are
Same subject: Coding agents already do coarse-grained recursive self-improvement, because every use rewrites their instruction files and their long-term memory. — tap to centre the map on it Coding agents already do coarse-grained recursive self-improvement, because every use rewrites their instruction files and their long-term memory. Last stated 3 months ago
26 Jul 2026
JH
Jensen Huang — holds since 2026-07-26 — tap for who they are
Same subject: The current speed of AI progress could be sustained all the way into recursive self-improvement. — tap to centre the map on it The current speed of AI progress could be sustained all the way into recursive self-improvement. Last stated a month ago
6 Sept 2026
JP
Jakub Pachocki — holds since 2026-09-06 — tap for who they are
Same subject: Frontier AI labs improve via a flywheel of getting the smartest people on leading models, then distilling their inputs, model outputs, and synthetic data, with users locked in by mutual competition. — tap to centre the map on it Frontier AI labs improve via a flywheel of getting the smartest people on leading models, then distilling their inputs, model outputs, and synthetic data, with users locked in by mutual competition. Last stated a month ago
10 Sept 2026
NR
Naval Ravikant — holds since 2026-09-10 — tap for who they are
Same subject: Recursive self-improvement in AI will mainly make existing model capability cheaper rather than raise peak intelligence. — tap to centre the map on it Recursive self-improvement in AI will mainly make existing model capability cheaper rather than raise peak intelligence. Last stated 4 weeks ago
19 Sept 2026
NL
Nathan Lambert — holds since 2026-09-19 — tap for who they are
Same subject: Recursive self-improvement (RSI) may not lead to runaway superintelligence without a major conceptual breakthrough. — tap to centre the map on it Recursive self-improvement (RSI) may not lead to runaway superintelligence without a major conceptual breakthrough. Last stated 3 weeks ago
27 Sept 2026
RN
Ramez Naam — holds since 2026-09-27 — tap for who they are
Same subject: There is no real impediment to models running their own research loop and improving themselves at a far more rapid rate. — tap to centre the map on it There is no real impediment to models running their own research loop and improving themselves at a far more rapid rate. Last stated 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. — tap to centre the map on it A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. Last stated 2 years ago
13 May 2024
DL
Derek Lowe — holds since 2024-05-13 — tap for who they are
Same subject: A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. — tap to centre the map on it A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. Last stated 2 years ago
17 Sept 2024
AT
Alex Telford — holds since 2024-09-17 — tap for who they are
Same subject: A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. — tap to centre the map on it A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work. — tap to centre the map on it A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — 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: 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: A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. — tap to centre the map on it A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. — tap to centre the map on it Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: Agent containment will be one of the most important priorities for any AI infra company. — tap to centre the map on it Agent containment will be one of the most important priorities for any AI infra company. Last stated 3 days ago
14 Oct 2026
GR
Guillermo Rauch — holds since 2026-10-14 — tap for who they are
At the centre
Tiny models trained for narrow tasks can perform excellently when paired with a strong self-improving recursive flywheel
Holds Read this korrent →
Recursive self-improvement only works if the base model is already capable enough to improve its own mechanism.
Holds
Recursive self-improvement will increase AI capability very quickly from here.
Holds
Coding agents already do coarse-grained recursive self-improvement, because every use rewrites their instruction files and their long-term memory.
Holds
The current speed of AI progress could be sustained all the way into recursive self-improvement.
Holds
Frontier AI labs improve via a flywheel of getting the smartest people on leading models, then distilling their inputs, model outputs, and synthetic data, with users locked in by mutual competition.
Holds
Recursive self-improvement in AI will mainly make existing model capability cheaper rather than raise peak intelligence.
Holds
Recursive self-improvement (RSI) may not lead to runaway superintelligence without a major conceptual breakthrough.
Holds
There is no real impediment to models running their own research loop and improving themselves at a far more rapid rate.
Holds
A compound's AI origin story is not by itself a reason to expect it to do better in the clinic.
Holds
A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck.
Holds
A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known.
Holds
A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work.
Holds
An adaptive agent's world model cannot be a static representation learned once and fixed
Holds
Current AI evaluation of world models is too narrow, favoring static representations learned from massive datasets over efficient learning through interaction
Holds
A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests.
Holds
Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers.
Holds
Agent containment will be one of the most important priorities for any AI infra company.
Holds