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← A project that only shows the models are not ready yet is a win if it…
17 connected korrents · 16 moments from 10 Dec 2015 to 10 Sept 2026.
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Trauma 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: A project that only shows the models are not ready yet is a win if it leaves behind an eval to retry with later models. A project that only shows the models are not ready yet is a win if it leaves behind an eval to retry with later models. Last stated 3 weeks ago
10 Sept 2026
MK
Mike Krieger — holds since 2026-09-10 — tap for who they are
Same subject: Evaluating a model properly would mean delaying its release, and competitive pressure means no lab will. — tap to centre the map on it Evaluating a model properly would mean delaying its release, and competitive pressure means no lab will. Last stated 3 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments. — tap to centre the map on it Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments. Last stated 10 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: Running a model until its performance plateaus is no longer a usable evaluation rule, because a well-scaffolded model keeps improving for weeks. — tap to centre the map on it Running a model until its performance plateaus is no longer a usable evaluation rule, because a well-scaffolded model keeps improving for weeks. Last stated 3 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: Local models are not yet reliable enough for production software development. — tap to centre the map on it Local models are not yet reliable enough for production software development. Last stated 4 months ago
15 Jun 2026
VB
Vicki Boykis — holds since 2026-06-15 — tap for who they are
Same subject: Building on a model is unlike any previous software engineering, because the thing you are building on cannot be designed up front. — tap to centre the map on it Building on a model is unlike any previous software engineering, because the thing you are building on cannot be designed up front. Last stated 2 months ago
27 Jul 2026
BC
Boris Cherny — holds since 2026-07-27 — tap for who they are
Same subject: Build where today's models succeed one percent of the time, not twenty: partial success means the capability is already arriving. — tap to centre the map on it Build where today's models succeed one percent of the time, not twenty: partial success means the capability is already arriving. Last stated 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — 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 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: Coming into a project with a guess about what the data will show has, again and again, turned out to be wrong. — tap to centre the map on it Coming into a project with a guess about what the data will show has, again and again, turned out to be wrong. Last stated 5 months ago
8 May 2026
DR
David Reich — holds since 2026-05-08 — tap for who they are
Same subject: A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. — tap to centre the map on it A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. Last stated 9 months ago
1 Jan 2026
JL
Jason Lemkin — holds since 2026-01-01 — tap for who they are
Same subject: A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. — tap to centre the map on it A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. Last stated 3 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: A child who has seen ten cats learns what a machine needs the whole internet of cat photos for, by a learning pathway nobody has solved. — tap to centre the map on it A child who has seen ten cats learns what a machine needs the whole internet of cat photos for, by a learning pathway nobody has solved. Last stated 2 months ago
10 Aug 2026
FL
Fei-Fei Li — holds since 2026-08-10 — 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 a month 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 2 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 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. — tap to centre the map on it A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. Last stated 8 years ago
11 Oct 2018
KT
Kristina Toutanova — holds since 2018-10-11 — tap for who they are
MC
Ming-Wei Chang — holds since 2018-10-11 — tap for who they are
KL
Kenton Lee — holds since 2018-10-11 — tap for who they are
JD
Jacob Devlin — holds since 2018-10-11 — tap for who they are
Same subject: A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. — tap to centre the map on it A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. Last stated 11 years ago
10 Dec 2015
JS
Jian Sun — holds since 2015-12-10 — tap for who they are
KH
Kaiming He — holds since 2015-12-10 — tap for who they are
Same subject: A neural network's latent space is closer to an uncopyrightable syntax than to copyrightable expression. — tap to centre the map on it A neural network's latent space is closer to an uncopyrightable syntax than to copyrightable expression. Last stated 4 weeks ago
7 Sept 2026
KK
Kevin Kelly — holds since 2026-09-07 — tap for who they are
At the centre
A project that only shows the models are not ready yet is a win if it leaves behind an eval to retry with later models.
Holds Read this korrent →
Evaluating a model properly would mean delaying its release, and competitive pressure means no lab will.
Holds
Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments.
Holds
Running a model until its performance plateaus is no longer a usable evaluation rule, because a well-scaffolded model keeps improving for weeks.
Holds
Local models are not yet reliable enough for production software development.
Holds
Building on a model is unlike any previous software engineering, because the thing you are building on cannot be designed up front.
Holds
Build where today's models succeed one percent of the time, not twenty: partial success means the capability is already arriving.
Holds
There is no real impediment to models running their own research loop and improving themselves at a far more rapid rate.
Holds
Coming into a project with a guess about what the data will show has, again and again, turned out to be wrong.
Holds
A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI.
Holds
A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to.
Holds
A child who has seen ten cats learns what a machine needs the whole internet of cat photos for, by a learning pathway nobody has solved.
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
A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models.
Holds
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable.
Holds
A neural network's latent space is closer to an uncopyrightable syntax than to copyrightable expression.
Holds