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.

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Showing developments with attributable Opportunity & adoption reactions.

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See how experts are reacting—not just what they shared.

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Opportunity & adoption Research & technical analysis
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Multiple readings Introducing System One Models and Jev - TypeSafe AI Blog Research & technical analysis · 2 attributable reactions
Showing signals surfaced by Fran Litterio ×
Most important

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 13d ago
TypeSafe AI System One Jev launch

Introducing System One Models and Jev - TypeSafe AI Blog

7 experts across 4 network communities independently surfaced this.

Why this matches Opportunity & adoption reaction 1 attributable expert contribution · Sung Kim
“OMG! I just invented a classifier that is 200x faster and 400x cheaper than LLM. Heck, I don't even need a GPU. Jev typesafe.ai/blog/introdu...” evidence ↗
7 experts 4 communities 1 sources clustered
Ramon Astudillo Dylan Hadfield-Menell Tim Kellogg Sung Kim Rohan Paul Fran Litterio Amy Hoy
How the network is reacting Experts are approaching this through 2 distinct lenses.
Multiple readings

Research & technical analysis

1 expert

Evidence, methods and technical implications.

“Jev: Fable-level model that doesn’t charge for output tokens because they’re too cheap to meter it’s not general though, it only makes decisions, doesn’t generate text, but input tokens are measured by the billion ($42/btok) typesafe.ai/blog/introdu...”

Opportunity & adoption

1 expert

New capabilities, benefits and practical upside.

“OMG! I just invented a classifier that is 200x faster and 400x cheaper than LLM. Heck, I don't even need a GPU. Jev typesafe.ai/blog/introdu...”

3 experts discussed this · 21 posts
Tim Kellogg: Jev: Fable-level model that doesn’t charge for output tokens because they’re too cheap to meter it’s not general though, it only makes decisions, doesn’t generate text, but input tokens are measure…
Tim Kellogg: i feel like it’s a mistake to overlook this model, although i’m having trouble figuring out where it fits in my workflow wild new architecture, totally different approach my hunch is the main agent…
Tim Kellogg: oh interesting, some examples they give: 1. smart if-statements 2. map-reducing over huge data to extract features and insights 3. real-time applications (it’s only 100ms) hmm this seems like a swe…
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Primary source typesafe.ai ↗
Established Models & releases Signal 16d ago
⚡ 5 h early

Putting sign language AI into users’ hands — Google DeepMind

4 experts across 2 network communities independently surfaced this.

Why this matches Opportunity & adoption reaction 1 attributable expert contribution · Nando de Freitas
“Another amazing use of AI to empower people, and not leave anyone behind. https://t.co/BpJiY05Sks” evidence ↗
4 experts 2 communities 1 sources clustered
Nando de Freitas Nicole Hennig Victor Geislinger Fran Litterio

“Another amazing use of AI to empower people, and not leave anyone behind. https://t.co/BpJiY05Sks”

“Putting sign language AI into users’ hands deepmind.google/blog/putting-s… #AI #accessibility #ASL”

Primary source deepmind.google ↗