soybelli connected Pac-Man to laya-mlx on Apple Silicon. The live view shows action probabilities, latency, tactical planning and safety interventions, with 0 output tokens.
No cloud API, no text generation.
介绍比Jev快50倍，在你设备上跑的laya-mlx! 只在你的设备上占用最高1G内存 Laya是一个开源的类似于Jev的,基于文本输出概率的分类系统 我将其移植到MLX,并且做了一些性能优化! 视频中就是这个模型在我的本地M3Max上玩贪吃蛇 这个模型能够以每秒决策60次的速度玩贪吃蛇！ https://t.co/31KGUiNunb
@mizorewww · Tools & apps · free, local · 60 decisions/s
@mizorewww
X post
Tools & apps
free, local
60 decisions/s
AXERA-TECH's conversion of all three Laya checkpoints for its edge AI chips, reported at latency under 70 ms per decision on AX650/AX8850.
AXERA-TECH · Tools & apps · free, on-device · < 70 ms on AX650 / AX8850
AXERA-TECH
GitHub
Tools & apps
free, on-device
< 70 ms on AX650 / AX8850
MCP server plus HTTP sidecar: token-budget preflight that reports what would be cut, a persisted calibration store, structured errors, and a noul fix (P(true) 0.5 → 1.0).
@PerryLink · Tools & apps · free, local
@PerryLink
GitHub
Tools & apps
free, local
-
Local 15x15 Gomoku on Apple Silicon. Rules in Python, board in the browser, every AI move one real Laya choice over up to six candidates, probabilities shown. No Gomoku training.
smile-magic · Tools & apps · free, local
smile-magic
GitHub
Tools & apps
free, local
-
I connected Pac-Man to Laya-MLX 👻 Every move is a real typed decision running locally on Apple Silicon—no cloud API, no text generation, 0 output tokens. The live view shows action probabilities, latency, tactical planning, and safety interventions. { "steps": 351, Show more
介绍比Jev快50倍，在你设备上跑的laya-mlx! 只在你的设备上占用最高1G内存 Laya是一个开源的类似于Jev的,基于文本输出概率的分类系统 我将其移植到MLX,并且做了一些性能优化! 视频中就是这个模型在我的本地M3Max上玩贪吃蛇 这个模型能够以每秒决策60次的速度玩贪吃蛇！ github.com/mizorewww/laya…
