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aientity · one source so far

rtx 3050

for ML engineers, AI researchers

Coolingdevto
Signal score
6
Live items
2
Trajectory
↓ Cooling
7-day est. ~3

Why this scored 6

every term, weighted
Velocity+0.0 / 40

Its fastest-moving item, measured against the pace of its own source

Acceleration+0.0 / 25

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25

How many independent communities its own items come from

Recency+6.6 / 10

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty−0.6 / 30

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite6.0

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

medium confidence · estimate, not a guarantee

7-day

~3

range 0–15

14-day

~0

range 0–14

30-day

~0

range 0–19

Signal history

7-day window (free)
375

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.

  1. 1
    22

    Can a 26B MoE Run on an 8 GB GPU? Gemma 4 26B A4B on an RTX 3050

    Gemma 26B runs on an RTX 3050 via llama.cpp and CUDA, showing consumer-grade inference is feasible. · for ML engineers, AI researchers

    devtoEmergingai23m ago
  2. 2
    18

    Gemma 4 on a Used 8 GB RTX 3050: 116 Tokens/s for One User, 618 for 64

    Gemma 4 achieves 116 tokens/s on RTX 3050, scaling to 618 tokens/s with 64 GPUs. · for ml engineers, ai researchers

    devtoSteadyai4d ago
  3. 3
    10

    How Much Context Fits Beside a 12B on an 8 GB GPU? Gemma 4 12B's Context Cache on an RTX 3050

    Gemma 4 12B model fits 8 GB GPU context cache using llama.cpp and CUDA on RTX 3050. · for ml engineers

    devtoCoolingai1h ago