LLM routing & guardrails

yibie

@yibie

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Independent Jev vs Laya measurements plus a cascade: Laya decides first, high confidence executes, low confidence escalates to Jev. At a 0.60 threshold it matched Jev's 78%.

Islem Maboud

@ipenywis

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A port of the jev-ultrafast browser agent that decides with laya-mlx instead of the hosted Jev API. DOM snapshot, executor, safety checks and inspector are the originals.

Gleb Lukicov

@glukicov

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Two routers behind one endpoint, measured on 180 labelled requests. Laya answers small/medium/powerful routing in one forward pass, against a GPT-5 nano router.

ChenneyZhuang

@ChenneyZhuang

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Local decider for the browser-agent loop: hand it a numbered table of page controls and it picks the operation and element. Playwright/CDP driver, speaks /v1/systemone.

Openlayer

github.com

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Openlayer's library packs every eval for a trace into one decision request: tool choice, grounding, scope, injection, PHI. Runs on Jev, or on Kev or Laya locally on a Mac.

Thomas Brüggemann

@thomasbrueggemann

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The decision model never writes code; each step it picks the next tool, scores progress, estimates risk and says if the goal is reached. Any OpenAI-compatible LLM executes.

Gopalji Gaur

@gopaljigaur

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Choice, Score and Noul over any System One backend with a confidence policy: if a backend errors or is unsure, the next is tried. Ships a TypeSafe-compatible server.

cdepuy

@cdepuy

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Hermes plugin that classifies each task against a ~300-skill index with Laya, injects the top skill's SKILL.md as per-turn context, and fails open. Cache-safe by design.

seedling

@wangmiaozero

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Cross-platform Agent Skill plus MCP tool that gives coding agents a local Laya decision capability. Verified on Apple Silicon with MLX and PyTorch backends.

yankis

@yankis0x

Seven open source Jev models tested in 21 minutes. Laya gets the last 3:41 0% - 0:00 - intro 17% - 3:46 - JevBench, the thing they all get scored on 33% - 7:02 - Nimble 51% - 11:03 - Decider 62% - 13:20 - OpenJev 83% - 17:50 - Laya The order is the story. Laya did 2M views this

Sai Dutta Abhishek Dash

@AbhishekDash69

I benchmarked Jev 1.13.0 vs open-weights Laya on 751 identical questions. Jev sweeps triage/guardrails/moderation, goes 1.000 on 5-language intent. Laya wins agnews + mnli at $0. Full data:

Aaron Edell

@aaronedell

Benchmarked @mizorewww’s Laya-MLX against @FeatherlessAI Simple Jev on my M4 Pro inside @OpenClaw: Laya: 85% @ 27.8ms Jev/Qwen3-4B: 90% @ 1,167.6ms The stack is rules → Laya → Jev → @ollama 27B → cloud. 14/20 decisions resolved before Jev. @typesafeai was right about System

Andrea Bruno

@baronunread

I've added Laya and https://t.co/flwFG40TX4 (@michael_chomsky) support to leanest, Jev is good but having other options to try out seems nice. Also new og image (I hate og image caching so bad) https://t.co/OgVzZg8CVE

Akihito Koriyama

@koriym

Built a hypermedia browser on Laya-MLX — a small model that, like Jev, doesn’t generate text; it just returns a probability for each option you give it. The next link is chosen from HAL’s _links and what ALPS declares those links mean. No routes defined up front. cc/ @mamund

//s̶̀pò̶of.sh

@spoofydude

I'm publishing this workflow! a 3-model fully local pipeline (prompt enhancement + Laya decision-making) that generates an image in ~37s (fast af) Should run great on any RTX 3080+ https://t.co/i11GcPJbIm

pythongiant

@pyth0ngiant

Built laya-drift to visualise and monitor agentic-drift today. It continuously measures how far an OpenCode session has drifted from its original plan using Laya + Jensen-Shannon divergence https://t.co/eB9DNlQksp

ShaunSpark

huggingface.co

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A browser-use agent that picks the next action with Laya, trained and evaluated on the Mind2Web web-task benchmark. Weights on Hugging Face.