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laya-mps

Run Jev-style typed decisions locally on your Mac with low RAM usage and fast responses

Details

External ID
1379229326
Source
GITHUB
Company
—
Product
laya-rs
Website domain
github.com
Launched
Sept. 21, 2026
Cohort
—
Upvotes
22
Upvotes percentile
0.6369459390212657
Tags
ai, apple-silicon, decision-model, jev, laya, local-ai, local-llm, python, pytorch
Fetched at
Sept. 25, 2026, 5:02 p.m.
Updated at
Sept. 25, 2026, 5:02 p.m.

Enrichment

Theme
decision model runtimes and tools
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
local decision engine for mac developers
Manually corrected
False

Could you build this?

Partial Creating a local runner with an API wrapper is vibe-codeable, but running low-RAM, high-performance typed decision models locally on Apple Silicon requires Metal/MPS optimization and ML systems tuning.

What it would actually take: The project requires an Apple Silicon execution runtime leveraging Metal Performance Shaders (MPS) or MLX/llama.cpp-style quantization and memory-mapped model weights. The tricky part is optimizing memory footprint while maintaining fast inference latency and strictly enforcing schema/typed outputs via grammars (e.g., GBNF or outlines). It requires engineers familiar with local LLM runtimes, Metal compute kernels, and structured generation engines.

Competitors

Other products that read as similar to this one — 1235 launches clear the similarity bar, closest 8 shown.

Attention rank: #514 of 1236 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 325 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a dev tools tool for Sales yet.