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aientity · one source so far

jax

for ML engineers, research scientists

Steadystackoverflow
Signal score
9
as of 5d ago
Live items
1
Trajectory
Accelerating
7-day est. ~14

Why this scored 9

every term, weighted
Velocity+0.0 / 40

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

Acceleration+12.0 / 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+0.0 / 10

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

Saturation penalty2.6 / 30

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

Composite9.4

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 guarantee

7-day

~14

range 026

14-day

~14

range 029

30-day

~14

range 034

Signal history

7-day window (free)
931

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
    28

    Deploying a QAT Checkpoint Your Serving Stack Can't Load: Gemma 4 E2B in Pure JAX on One TPU

    Runs quantized Gemma-4 E2B on JAX using a single TPU, enabling efficient LLM serving. · for ml engineers

    devtoSteady 2 · jaxai22d ago
  2. 2
    19

    Gemma 4 in Pure JAX: What Changes Between Turing and Ada, and What Doesn't

    Gemma 4 on JAX highlights differences between Turing and Ada GPUs. · for ml engineers

    devtoSteadyai11d ago
  3. 3
    18

    g5g vs g6 for LLM Serving: the Same Code, and 3.7x the Throughput

    g5g vs g6 comparison shows 3.7x throughput boost for LLM serving on same code. · for ml engineers

    devtoSteadyai11d ago
  4. 4
    18

    Gemma 4 in Pure JAX: What Ports from TPU to GPU, and What Doesn't

    Porting Gemma 4 from TPU to GPU shows performance trade-offs for JAX users. · for ml engineers, jax developers

    devtoSteadyai13d ago
  5. 5
    16

    How to visualize a Jax neural network?

    Provides methods to visualize JAX neural networks for debugging and model insight. · for ML engineers, research scientists

    stackoverflowSteadyai7d ago
  6. 6
    15

    Reproduce n-ary `vmap` from unary

    Shows how to build n-ary vmap in JAX, expanding vectorized mapping capabilities. · for ml engineers

    stackoverflowSteadyai23d ago
  7. 7
    12

    One TPU Chip, Eight Agents: Serving Small Agent Workloads with Raw JAX

    Efficient agent workloads · for ai engineers

    devtoSteady 2 · jaxai43d ago
  8. 8
    6

    openai/whisper-large-v3 (automatic-speech-recognition)

    New speech recognition · for ai devs

    huggingfaceSteady 3 · openaiai23 saturated31d ago
  9. 9
    0

    open-dreamer — Open-source Dreamer world-model implementation in JAX

    World-model implementation · for ai researchers

    githubSteadyai14 saturated45d ago
  10. 10
    0

    nanocodex — Blazing-fast, minimal, library-first reimplementation of Codex

    Fast codex reimplementation · for ai devs

    githubSteadyai13 saturated45d ago
  11. 11
    0

    Cactus-Compute/needle

    New tool for function calling · for ai devs, researchers

    huggingfaceSteadyai15 saturated57d ago