jax
for ML engineers, research scientists
Why this scored 9
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
~14
range 0–26
14-day
~14
range 0–29
30-day
~14
range 0–34
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.
- 128
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 - 219
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 - 318
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 - 418
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 - 516
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 - 615
Reproduce n-ary `vmap` from unary
Shows how to build n-ary vmap in JAX, expanding vectorized mapping capabilities. · for ml engineers
stackoverflowSteadyai23d ago - 712
One TPU Chip, Eight Agents: Serving Small Agent Workloads with Raw JAX
Efficient agent workloads · for ai engineers
devtoSteady 2 · jaxai43d ago - 86
openai/whisper-large-v3 (automatic-speech-recognition)
New speech recognition · for ai devs
huggingfaceSteady 3 · openaiai−23 saturated31d ago - 90
open-dreamer — Open-source Dreamer world-model implementation in JAX
World-model implementation · for ai researchers
githubSteadyai−14 saturated45d ago - 100
nanocodex — Blazing-fast, minimal, library-first reimplementation of Codex
Fast codex reimplementation · for ai devs
githubSteadyai−13 saturated45d ago - 110
Cactus-Compute/needle
New tool for function calling · for ai devs, researchers
huggingfaceSteadyai−15 saturated57d ago