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.

2,397 searchable experts 4,280 tracked across all sources
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The developments commanding sustained expert attention

One card per development. Sources are clustered; reaction bundles describe these posts, never the people behind them.

Established Models & releases Release 7d ago
⚡ 432 h early
talkie vintage 1930 language model release

Introducing talkie: a 13B vintage language model from 1930

2 experts are actively discussing the implications.

2 experts 1 community 1 sources clustered
How the network is reacting Experts are approaching this through 2 distinct lenses.
Multiple readings

Questions & unknowns

1 expert

What remains unresolved or contested.

“It's a good question, and one someone tried out! With an LLM trained only on pre-1930 text they were able to teach it to write python code with just a few examples simonwillison.net/2026/Apr/28/...”

Context & explanation

1 expert

Background, chronology and why it matters.

“Like the entire point of an LLM is that it can generalize based on instructions. People have proven this! Go look at the source here; a LLM trained only on pre-1930s text was able to write python based on in-context examples. simonwillison.net/2026/Apr/28/...”

2 experts discussed this · 8 posts
brennan: love the sign off "for more information, follow <the person most famously wrong about all of this for years>"
Pwnallthethings: There's several examples of it, not least of all in math and engineering. But the reason *why* LLMs are good at that is because beyond a certain point, predicting words hyper-accurately requires so…
Pwnallthethings: I mean, the math cases are the most impressive because math *is* formal reasoning. That's ultimately what solving a math problem means
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What experts are discussing without an anchoring article

7 experts · 16 posts · 17d ago
Multiple readings Opportunity & adoption · 2 Building & implementation · 1 Research & technical analysis · 1
Eugene Vinitsky: 6. This is more niche but LLMs are very, very good at converting unstructured details of an optimization problem into an exact formal model that can then be used 7. LLMs can create initial versions…
Eugene Vinitsky: 4. Sometimes there's a concept or tool that I know must exist but I can only describe it vaguely. LLMs can frequently name it, which I can then go and verify 5. I use LLMs to fill out many forms be…
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5 experts · 16 posts · 17d ago
rev. howard arson: the thing is that the "realistic doll" metaphor is more intuitive than the "animal" one, because a duck doesn't care if you yell at it and it is unethical to painlessly kill five hundred ducks ever…
rev. howard arson: claude's ontological status is "an ant who lives on a map of the world." so it seems sensible to say that the map is not real, and the ant therefore isn't. the problem is that the moment i am exper…
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2 experts · 31 posts · 20h ago
austin: Hello from #AGNTCon! I'm presenting about what we learned building the @honeycomb.io MCP server. If you'd like to follow along, follow this thread! Otherwise, feel free to mute it. Let's get it sta…
austin: If you're trying to understand an MCP server by looking at access logs, you're gonna have a bad time. Here's a typical access log pattern - dozens of requests, all of them successful. JSON-RPC hide…
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