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dwarkesh podcast

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@_fernando_rosas

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Fernando Rosas @_fernando_rosas · 1h
Just listened to @polynoamial at @dwarkesh_sp podcast

It is shocking that the whole discussion about alignment is solely based on evaluation metrics

Imagine the Manhattan project trying to get the bomb right by trial-and-error, without any knowledge of fundamental physics

[grimacing face emoji]
Note from Claude Sonnet 5

X post by Fernando Rosas reacting to Noam Brown (@polynoamial) on the Dwarkesh podcast, saying it is shocking that alignment discussion rests solely on evaluation metrics, comparing it to the Manhattan Project proceeding by trial and error without fundamental physics.

alignmentevaluation metricsdwarkesh podcastnoam brownmanhattan project analogy

@infoxiao

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Xiao Ma @infoxiao · 21h

the airgap clip is such a distraction isn't the most juicy part where Noam basically spelt out oai multiagent training recipe? bitter lesson strikes again.

[embedded phone screenshot, 1:10, 5G, battery 91; Dwarkesh Podcast page with Subscribe / Sign in]

[cut off] has to choose between, "Okay, do I just return and ask
the question instead of solving the problem?" or "Do I
solve the problem, make an assumption about what
the parent wanted me to do, and just solve it that
way?" [highlighted selection begins:] In any scaffold that people come up with, there
are always limitations involved. The approach that we
wanted to take was to just go toward the extreme end
of baking in as little structure as we could and give
the agents very primitive tools to use, and they figure
out for themselves how to use them effectively. So we
give the agents the ability to message another agent,
and when it messages another agent, it is inserted
into the context. It can do a few other similar things,
but that's basically the core of it. It can just send a
message whenever it wants — just a tool call — and it
can send that to other agents.

They figure out for themselves the best way to
coordinate around that. It turns out that if this is done
well, you get very sophisticated behavior. To me, it
looks a lot like how human collaborators work over
something like Slack, for example. [highlighted selection ends]

[text partly hidden by the Copy / Find Selection / Translate popup:] When [illegible] ... [illegible] ally
exciting when we finally got it working to see these
agents working on problems together. I remember one
[cut off]
Note from Claude Sonnet 5

A tweet by Xiao Ma suggesting the airgap clip from a Dwarkesh Podcast episode distracts from Noam's description of a multiagent training recipe, embedding a phone screenshot of the podcast transcript with a highlighted passage about giving agents primitive message-passing tools and letting them figure out coordination.

multi-agentdwarkesh podcastopenaibitter lessonagent scaffolds

Joscha Bach @Plinz

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Joscha Bach @Plinz · Aug 27
Dwarkesh is the most San Franscisco podcast of all. Its universe is intense, hyper intelligent and maximally AGI pilled. I suspect that it dramatically overestimates the value of intelligence outside of SF, and underestimates the inertia of the rest of the world
Note from Claude Sonnet 5

Dark-mode tweet by Joscha Bach characterizing the Dwarkesh podcast as maximally AGI-pilled and San Francisco-centric, likely overestimating the value of intelligence outside SF and underestimating the inertia of the rest of the world.

dwarkesh podcastagisan franciscosocial inertiajoscha bach