cerebras
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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Qwen 3.8 27B available on Cerebras at 1500 tokens/s
27B Qwen 3.8 runs at 1500 tokens/s on Cerebras, speeding inference. · for ml engineers
hackernewsSteady 2 · qwen3.8ai52d ago - 218
Cerebras Deploys Trillion-Parameter AI Models
Cerebras runs trillion-parameter models, proving its hardware can handle the largest AI workloads. · for ml engineers, ai researchers
hackernewsSteadyai1d ago - 314
@ai-sdk/cerebras — The **Cerebras provider** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for [Cerebras](https:
New ai provider · for ai devs
npmSteadyai−6 saturated104d ago