Tools & apps

Anton Lobach

@AntLobach

Wow, Laya (https://t.co/PhmZAwTZeX) inference in Jolt (Clojure running on Chez Scheme)

Daniel Tremer

@TremerDaniel

ScreenQuest: an autonomous screenshot-to-action AI agent for https://t.co/RHiWnwGPEf, running locally on an M3 Max (48 GB). Qwen3.5-4B / MLX + Laya / Core ML + Apple Vision OCR. No cloud inference. Code: https://t.co/gFP4sjd3VU

Shantanu Goel

@shantanugoel

Update: I trained Laya non-step for close to 24 hours. No where close to jev 0-shot :|

Simplifying AI

@simplifyinAI

The open-source local version of Laya crushes Jev in response speed! On the left is Laya, a local 421M open-source decision model. On the right is Jev 1.13.0, running through a cloud API. Same Snake game, same typed decisions, and the same 30-second free run: - Laya: Score

TTUX | تی‌تاکس (AmirTaha سابق)

@TTUX_tech

جالب شد بازی اسنیک. ورودی لوکیشن میوه و هد و بوردر هارو میگیره و با مدل اوپن سورس #Laya به صورت لوکال که شبیه #Jev هست دستور میگیره که لوکیشن بعدی کجا بره بالا پایین چپ راست. تجربه جالبی بود اینم لینک پروژه تو گیت هاب ممنون میشم با 🌟 حمایت بکنین: https://t.co/dcQcs6WljY

Mansour Raad

@mraad

The engineer and the trainee. Adaptive MPC knows the physics. Laya, a small decision model, learned from examples. Alone, Laya lands 0/90. With MPC vetoing about one in three of its moves, it gets 90/90, through a 60% engine loss. Fly it in your browser:

Daniel F

@cristianexer

I had a stupid idea: What if, instead of asking an #AI to talk, I made it fight me? 🥊 So I built World Summit Tournament. 21 fictional world-leader-inspired fighters, signature moves, combos, replays — and the opponent can use Laya, a small open-weight decision model in the

Sree

@sreexts

Two AIs. One game. I let @typesafeai 's Jev and @brainFnCl's Laya play the same arena survival game where every enemy’s decision is made live by the models. How it works: the game turns each moment into one sentence and asks one typed question. Back come probabilities, not

soybelli

@ahmetsoybelli

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,

 Ahmed

@eng_ahmd

I made Laya (Jev-like model) play Breakout, Every paddle move is a live decision from an open 322M System One model (convaiinnovations/laya) running locally on Windows laptop, ~70 decisions/sec on an RTX 5080, zero cloud, $0. Code 👉 https://t.co/8v0dnXCnQ1

Hope

@hope_rythmn

i made Jev and Laya fight 1v1 each other in Doom 1v1 deathmatch, monsters in between, first to kill the other opponent 5 times wins! @typesafeai Jev's calls were sharper, it needed a third of the corrections. @mizorewww Laya just decided twice as often. final results

Dev Ed

@developedbyed

If you’re wondering how fast Laya is compared to Jev: my maze demo measured 19ms median responses from local Laya on a base M5, versus roughly 300ms from Jev’s cloud API.

Tony Dinh

@tdinh_me

Tetris bench: Jev vs. Laya-mlx I run the Laya model on my MacBook. It's true that the model is really fast (~84ms), but it's also much more stupid. Losing to Jev 3 out of 3 rounds.

Viraj Bhartiya

@heyxviraj

i made Jev and Laya play the chrome dinosaur game against each other except they’re actually controlling the dinosaurs. Laya runs locally on my Mac, Jev runs over an API. same track, same physics, completely different latency. they make their own decisions, dodge obstacles,

brain function collapse

@brainFnCl

This is NOT Jev. Open source. Runs on your laptop. Decides in ~27 ms, about 200× faster than waiting on a hosted LLM. Here it is playing Tetris by itself 👇 https://t.co/eq4gP53o2A

Mattepiu

huggingface.co

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ONNX export of the Laya checkpoint so it runs anywhere onnxruntime does, including browsers and edge boxes. An fp16 variant followed a day later.

Tom Siwik

@tomhacks

The problem isn't the models, it's how you define the problem space, design the movements / decisions and optimize for it (batching e.g.) Jev wins for a network-based model any day. Local models tied - Laya-mlx and my custom build Jax. Look at them go.

Tobias Wupperfeld

@tobiaswup

Jev vs locally running Laya-MLX and Kev-4B I built my own snake benchmark. Jev @typesafeai is running via API. The other models are small alternatives running on very little RAM on my Macbook! Jev seems to deliver the best quality no doubt! After running it for a while 0

chewa

@0xchewa

Laya beat Jev 43 to 1 with the wifi turned off 86.4 decisions per second against 3.2. 1,281 moves against 47 left side is Laya, an open weights decision model, 421 million parameters, running locally off a 1GB footprint. right side is Jev 1.13.0 over an API. both got the

Alex

@Alex_tra_memory

Thanks for the model, we were able to port Laya to coreml with 99.5% of the ops on ANE + benchmarked too. it is now blazing fast with 3.7 ms per decision on an M5 Pro. Release: https://t.co/9xerYIHt9y Models: https://t.co/kPOaMQI4RH

atomic.chat

@atomic_chat_hq

Local Laya moggs Jev at @grok 4.7-built Tetris 🧩 An open-weights System One model called Laya, beat cloud-based Jev at playing Tetris by making decisions 11 times faster, running locally on a 16GB MacBook Air! Run AI models locally -> https://t.co/RbcCOIgVkj

雨夹雪

@mizorewww

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MLX port of Laya with performance work. Under 1 GB of memory on-device; the demo plays Snake at 60 decisions per second on an M3 Max.

Nandakishor’s dev.to write-up on building decision models a year before Jev, RLCD training against proper scoring rules, and why Laya is open.

Nandakishor Mukkunnoth

@NandhaKishorM

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The model itself. Typed choice/score/noul questions over any state in one forward pass, 100+ languages, Apache 2.0 weights.