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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Showing developments with attributable Context & explanation reactions.

New Network reaction maps

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

Posts are grouped by conversation and tone. Select a lens to filter the stream; these are never permanent labels on people.

Building & implementation Context & explanation
1 live reaction map in this view Jump to the evidence ↓
Multiple readings Spark-X2.6 - a XHToken Collection Building & implementation · 2 attributable reactions
Showing signals surfaced by adinayakup.bsky.social ×
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 AI field signal Resource 12d ago
⚡ 134 h early
Spark-X2.6 XHToken collection release

Spark-X2.6 - a XHToken Collection

2 directory members surfaced this signal.

Why this matches Context & explanation reaction 1 attributable expert contribution · adinayakup.bsky.social
“After its 4B model, SparkLLM is back again with an even smaller one: Spark-X2.6-1.7B huggingface.co/collections/... - 3 thinking modes: none, low & high - Up to 1M context - Apache 2.0 + GGUF/int4/int8/FP8 - Leading decoding throughput vs similarly sized mo…” evidence ↗
2 experts 1 community 1 sources clustered
Sung Kim adinayakup.bsky.social
How the network is reacting Experts are approaching this through 2 distinct lenses.
Multiple readings

Building & implementation

1 expert

How teams are shipping and applying it.

“SparkLLM's Spark-X2.6-4B (open-weight) - 4B + Apache 2.0 - Up to 1M context - 3 thinking modes - Local deployment via GGUF Model: huggingface.co/collections/...”

Context & explanation

1 expert

Background, chronology and why it matters.

“After its 4B model, SparkLLM is back again with an even smaller one: Spark-X2.6-1.7B huggingface.co/collections/... - 3 thinking modes: none, low & high - Up to 1M context - Apache 2.0 + GGUF/int4/int8/FP8 - Leading decoding throughput vs similarly sized mo…”

Primary source huggingface.co ↗
Established Models & releases Release 18d ago
⚡ 6 h early
Sage-A1.5 InternScience model release

Sage-A1.5 - a InternScience Collection

2 directory members surfaced this signal.

Why this matches Context & explanation reaction 1 attributable expert contribution · adinayakup.bsky.social
“Sage A1.5 🔬 New agentic model built for long horizon scientific problem sloving huggingface.co/collections/... - 35B MoE - 256k context - Parallel subagents - Built in self-verification” evidence ↗
2 experts 1 community 1 sources clustered
Evangelos Kazakos adinayakup.bsky.social

“Sage A1.5 🔬 New agentic model built for long horizon scientific problem sloving huggingface.co/collections/... - 35B MoE - 256k context - Parallel subagents - Built in self-verification”

Primary source huggingface.co ↗
Established AI field signal Signal 7d ago

sensenova/SenseNova-Loop-9B-Preview · Hugging Face

1 directory member surfaced this signal.

Why this matches Context & explanation reaction 1 attributable expert contribution · adinayakup.bsky.social
“SenseNova-Loop-9B-Preview 👀 A looped multimodal model from SenseNova huggingface.co/sensenova/Se... - 9B - Apache 2.0 - 56 layer executions via weight sharing - Up to 256K context - Hybrid attention + two stream mHC” evidence ↗
1 expert 0 communities 1 sources clustered
adinayakup.bsky.social

“SenseNova-Loop-9B-Preview 👀 A looped multimodal model from SenseNova huggingface.co/sensenova/Se... - 9B - Apache 2.0 - 56 layer executions via weight sharing - Up to 256K context - Hybrid attention + two stream mHC”

Primary source huggingface.co ↗