Skip to content
Signalcrest
Get alerts
Back to entity feed
aientity · one source so far

gemma

for ml engineers

Emergingdevto
Signal score
15
as of 7h ago
Live items
1
Trajectory
→ Steady
7-day est. ~9

Why this scored 15

every term, weighted
Velocity+2.9 / 40

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

Acceleration+19.1 / 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 penalty−6.5 / 30

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

Composite15.4

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

~9

range 0–21

14-day

~10

range 0–24

30-day

~10

range 0–29

Signal history

7-day window (free)
054

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
    44

    Gemma 4 on an 2021 4 GB Laptop GPU: QAT Takes It From 9.5 GiB to 1.6

    Quantization-aware training shrinks Gemma 4 memory to run on 4 GB laptops. · for ml engineers

    devtoEmerging 2 · cudaai17h ago
  2. 2
    30

    Self-hosting a lite agent backend on one TPU: Gemma 4 E2B + vLLM on a v5e-1

    Run lite agent backend on one TPU · for devtools founders

    devtoSteady 2 · vllmhardware32d ago
  3. 3
    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
  4. 4
    22

    Serving Gemma 4 E2B on a TPU v6e-1: what Trillium buys, and what it doesn't

    Trillium buys TPU · for ai founders

    devtoSteadyai31d ago
  5. 5
    22

    Pure JAX on G5g: Serving Gemma 4 on Graviton and a T4G

    Shows how to serve Gemma 4 on ARM Graviton and T4G GPUs using AWS G5g instances. · for cloud ml engineers

    devtoSteadyai13d ago
  6. 6
    21

    tpu-management: a Claude Code skill for running Gemma 4 on Cloud TPUs

    Run Gemma 4 on Cloud TPUs · for ai devs

    devtoSteady 6 · claudeai52d ago
  7. 7
    20

    Running Gemma 4 on EC2 G5g: Graviton2 AMD with NVIDIA GPU

    Instructions for deploying Gemma 4 on AWS EC2 G5g using Graviton2 CPUs and NVIDIA GPUs. · for ml ops, cloud engineers

    devtoSteadyai28d ago
  8. 8
    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
  9. 9
    18

    TPU Deployments with Gemma 31B, v6e-8, and Antigravity CLI

    TPU deployments with Gemma · for ai engineers

    devtoCooling 2 · gemmaai57d ago
  10. 10
    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
  11. 11
    18

    Three Gemma 4 Deployments on One T4G for Under $3: What the Runtime Changes, and What It Doesn't

    Deploying three Gemma 4 models on a single AWS T4g costs under $3, showing cheap runtime options. · for ml engineers, cloud ops

    devtoSteadyai9d ago
  12. 12
    18

    Running Gemma 4 on EC2 G5g: Graviton2 AMD with NVIDIA GPU

    Guide to running Gemma 4 on AWS EC2 G5g instances with Graviton2 and NVIDIA GPUs. · for ml ops, cloud engineers

    devtoSteadyai28d ago
  13. 13
    18

    Latency vs. Tokens: What I Learned Optimizing an Agent with Gemma (and What Didn't Work)

    Optimizing agent performance · for ai engineers

    devtoSteadyai30d ago
  14. 14
    15

    Serving Gemma 4 2B on a Single TPU v5e Chip with MCP and Antigravity CLI

    Gemma 4 optimized on TPU · for ai engineers

    devtoSteadyai36d ago
  15. 15
    13

    Gemma 4 E2B on a Single TPU v6e Chip: A Serving Deep Dive

    Gemma 4 E2B on TPU v6e · for ai researchers

    devtoSteady 3 · gemmaai52d ago
  16. 16
    13

    Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac

    Run large models on low RAM · for ai devs

    hackernewsSteadyai43d ago
  17. 17
    13

    Self-Hosted Gemma 4 on TPU v6e: Deployment & SRE with Antigravity

    Gemma deployment on TPU · for ai devs

    devtoSteady 2 · gemmaai47d ago
  18. 18
    10

    Smash Stories: The Bug That Whispered for Two Weeks Before I Heard It

    whispering bug found · for ai engineers

    devtoSteady 2 · gemmaai52d ago
  19. 19
    10

    Porting a 128-expert MoE (Gemma-4 26B-A4B) to AWS Inferentia2 — where every rank weighted the wrong experts

    Porting gemma to aws · for ai engineers

    devtoSteady 2 · gemmaai55d ago
  20. 20
    10

    Master Local Fine-Tuning with "gemma-trainer"

    devtoSteady66d ago