apple silicon
for mac developers
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
Signal history
7-day window (free)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.
- 117
Apple Silicon and macOS VMs: Faster LLM Inference with llama.cpp
Faster LLM inference · for ai devs
hackernewsSteadyai34d ago - 216
We rebuilt the Linux MicroVM stack on Apple Silicon
Rebuilt Linux microVM stack to run natively on Apple Silicon hardware. · for platform engineers, devops
hackernewsSteadyhardware24d ago - 316
H3-metal – Native MiniMax-H3 inference for Apple Silicon
Native inference for Apple Silicon · for hardware devs
hackernewsSteadyhardware34d ago - 40
dji-4g-vohive-mac — 在 Mac(Apple Silicon / Intel)上用 UTM 跑 Linux 虚拟机,把大疆 4G 模块(EG25-G)伪装成移远 Quectel EC25 并部署 vohive 平台的完整步骤
githubSteady 2 · linux72d ago - 50
turbo-fieldfare — Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook
AI model runs on macbooks · for ai devs, mac users
githubSteady 2 · gemmaai−19 saturated47d ago