arcagi3
for ml engineers, ai researchers
Why this scored 0
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Outlook
low confidence · estimate, not a guarantee7-day
~0
range 0–12
14-day
~0
range 0–14
30-day
~0
range 0–19
Signal history
7-day window (free)projected trajectory (estimate, not a guarantee)
The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
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OpenAI's GPT-6 Astra on ARC-AGI-3
OpenAI announced GPT-6 Astra, a new model targeting ARC-AGI-3 benchmarks. · for ml engineers, ai researchers
hackernewsSteady 3 · openaiai7d ago - 229
Nvidia AVO scores 100% on the ARC-AGI-3 interactive reasoning benchmark
Nvidia AVO hits 100% on ARC-AGI-3 reasoning benchmark, showing strong interactive AI capability. · for ai researchers, ML engineers
hackernewsSteady 2 · nvidiaai18d ago - 317
How a Strands agent took Claude Opus 5 from 30% to 99.95% on ARC-AGI-3
Strands agent boosted Claude Opus 5 performance to 99.95% on ARC-AGI-3 benchmark. · for ai researchers, ml engineers
devtoSteadyai13d ago - 46
Schema Harness Achieves ~99% on Arc‑AGI‑3 Public
AI model achievement · for ai researchers
hackernewsSteadyai55d ago