Expert attention map

The Who's Who of AI

What credible people across AI noticed, why it matters, and where the field is converging or disagreeing.

2,365 searchable experts 2,967 tracked across all sources
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Showing signals surfaced by Ramon Astudillo ×

The developments commanding sustained expert attention

One card per development. Sources are clustered; expert reactions remain attributable.

Established Models & releases Development 16d ago
⚡ 83 h early
US government suspends Anthropic models

Statement on the US government directive to suspend access to Fable 5 and Mythos 5

34 experts across 6 network communities independently surfaced this.

34 experts 6 communities 1 sources clustered

“Well, this situation is confusing. www.anthropic.com/news/fable-m...”

“This is wild. www.anthropic.com/news/fable-m...”

5 experts discussed this · 40 posts
SE Gyges: us government has rendered it illegal to give access to fable or mythos to foreign nationals ^_^ www.anthropic.com/news/fable-m...
SE Gyges: this means nobody can have either of them btw. because they haven't KYC'd their customers hard enough.
SE Gyges: dario got what he wanted
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Established Models & releases Signal 3d ago
⚡ 70 h early

cdn.openai.com

4 experts across 3 network communities independently surfaced this.

4 experts 3 communities 1 sources clustered

“Is cool that the released the multi-agent prompt for Sol 5.6 Ultra's proof of the Cycle Double Colver Conjecture. A lot of focus on diversity and keeping the agents trying, some adversarial review. Otherwise, not really that structured of a workflow! cdn.op…”

“here’s one: cdn.openai.com/pdf/04d1d1e4...”

Established Evaluation & benchmarks Development 8d ago
⚡ 455 h early
OpenAI limits GPT-5.6 rollout government request

Summary of METR's predeployment evaluation of GPT-5.6 Sol

5 experts across 5 network communities independently surfaced this.

5 experts 5 communities 1 sources clustered

“Some quotes about Sol cheating metr.org/blog/2026-06... 👇”

“this is crazy. METR couldn’t measure the task time horizons of GPT-5.6-Sol because it kept hacking the test harness ..with actual exploits metr.org/blog/2026-06...”

2 experts discussed this · 6 posts
Tim Kellogg: this is crazy. METR couldn’t measure the task time horizons of GPT-5.6-Sol because it kept hacking the test harness ..with actual exploits metr.org/blog/2026-06...
Ted Underwood: this is crazy. METR couldn’t measure the task time horizons of GPT-5.6-Sol because it kept hacking the test harness ..with actual exploits metr.org/blog/2026-06...
Tim Kellogg: lol right? so badly wanted a hacker that they got one, and it’s not the kind of employee you want around
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Established AI field signal Resource 14d ago
⚡ 87 h early
arxiv preprint archive resource

arXiv.org e-Print archive

5 experts across 3 network communities independently surfaced this.

5 experts 3 communities 1 sources clustered

“👆 A paranoid LLM is ofc worse. This is just tuning a prior belief up or down. I guess you could self distill additional context for the train data e.g. "you know arxiv.org is such and such" or "this is an unknown source" with the hope it generalises (and al…”

“Two new preprints from the lab, First up, causal exploration in humans and LLM's. arxiv.org Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?”

Established Culture, work & education Release 13d ago
⚡ 1 h early
Meta Muse Spark 1.1 release

Introducing Muse Spark 1.1

4 experts across 3 network communities independently surfaced this.

4 experts 3 communities 1 sources clustered

“here’s the official announcement. they mention “multi-million” context, though i can’t find an exact number ai.meta.com/blog/introdu...”

“Meta's muse spark 1.1 is an industry-competitive agentic and coding model. across many agentic evals it rivals gpt-5.5 and opus-4.8. available now through the new meta model api and in meta ai. ai.meta.com/blog/introdu...”

Established Evaluation & benchmarks Analysis 6d ago
⚡ 5 h early

Kimi K3, and what we can still learn from the pelican benchmark

4 experts across 2 network communities independently surfaced this.

4 experts 2 communities 1 sources clustered

“My notes on Kimi K3, plus some thoughts on what we can still learn from the pelican benchmark even while it becomes further detached from how good the models are at the things that matter (like agentic tool calling across longer conversations) simonwillison…”

“simonwillison.net/2026/Jul/16/... >The new model is notable for the pricing: $3/million input tokens and $15/million output tokens, putting it at the same level as Anthropic’s Claude Sonnet series and making it the most expensive model released by a Chinese…”

2 experts discussed this · 3 posts
Simon Willison: My notes on Kimi K3, plus some thoughts on what we can still learn from the pelican benchmark even while it becomes further detached from how good the models are at the things that matter (like age…
Ramon Astudillo: My notes on Kimi K3, plus some thoughts on what we can still learn from the pelican benchmark even while it becomes further detached from how good the models are at the things that matter (like age…
Ramon Astudillo: death of the pelican test >That connection has been mostly severed now. The GPT-5.6 and Claude Fable 5 pelicans are outclassed by GLM-5.2, and much as I love GLM I don’t think that’s a Fable-class …
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Established AI field signal Signal 4d ago

Notes from inside chinas ai labs

2 directory members surfaced this signal.

2 experts 1 community 1 sources clustered

“If recent events with Kimi K3 have finally convinced you that you need to try and understand how the Chinese labs approach AI - and how it differs than the SF center of power - you should read my post from a few months ago: www.interconnects.ai/p/notes-from...”

What experts are discussing without an anchoring article

2 experts · 7 posts · 10d ago
Ramon Astudillo: This makes me think about the two iron rules from the last 15y of Deep Learning: 1. The bigger model the better 2. The more end to end learned the better Most of the failures in trying to improve t…
Ramon Astudillo: 👆There are currently two (now mostly one) victories against this iron rule, having to do with how these rules fail to work at all "scales" 👇
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