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Local Laya beat OpenAI's decision API 🕷 Four decision models raced down a web page ripping out links that name a spider and Laya running locally on an M5 Max took 34 ms a link so it checked 3x more than the cloud models Run decision models locally ->

atomic.chat

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219,303 görüntüleme • 26 gün önce •via X (Twitter)

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26 Yorum

AVB profil fotoğrafı

AVB26 gün önce

@OpenAI The first spider for super excited to see its own name and smashed through it lol

Jigs profil fotoğrafı

Jigs26 gün önce

@OpenAI Laya is actually good for its size

Omar profil fotoğrafı

Omar26 gün önce

@OpenAI Local gutsy outperforms local laya, and is also free, open source, and just 500MB for CPU inference

Secure the AI 🤖 profil fotoğrafı

Secure the AI 🤖26 gün önce

@OpenAI

Liam Vinberg profil fotoğrafı

Liam Vinberg26 gün önce

@OpenAI depends on the task

Thusatharan profil fotoğrafı

Thusatharan26 gün önce

@OpenAI no network round trip per call, of course local takes it

Mateleo profil fotoğrafı

Mateleo26 gün önce

@OpenAI kudo for the viz, wow

Mattia profil fotoğrafı

Mattia26 gün önce

@OpenAI I‘m using laya for my Hermes Agent and it’s perfect

Vikas profil fotoğrafı

Vikas26 gün önce

@OpenAI Decider-4B is better model than laya. Try using it.

Schwarzer Ritter profil fotoğrafı

Schwarzer Ritter26 gün önce

@OpenAI small = fast

Roko ʕ •ᴥ•ʔっ🪄✨🐍 profil fotoğrafı

Roko ʕ •ᴥ•ʔっ🪄✨🐍26 gün önce

@OpenAI I wonder how DM-JEPA would do here

Jaid profil fotoğrafı

Jaid26 gün önce

@OpenAI S-tier eval visualization 💎

Martin Ronfort profil fotoğrafı

Martin Ronfort26 gün önce

@OpenAI Laya running locally is actually proving decision models can outpace cloud APIs on both speed and cost. We went deeper on this shift in how AI judgment is changing here:

promovierdichinsknie profil fotoğrafı

promovierdichinsknie26 gün önce

@OpenAI i agree in my test laya also outperformed clef flash an CLM

Yoeven profil fotoğrafı

Yoeven26 gün önce

@OpenAI You haven’t put lev to the test

Georgy GLuhov profil fotoğrafı

Georgy GLuhov26 gün önce

@OpenAI I’ve achieved four hours of successful autonomous operation for Laya in my RimWorld colony without any input from me; all experiment logs are recorded in the repository, and I’m also planning to host a stream!

Wolkyen profil fotoğrafı

Wolkyen26 gün önce

@OpenAI What about Mercury decide? Its the best!

Eugene Meidinger profil fotoğrafı

Eugene Meidinger26 gün önce

@OpenAI Nightmare fuel

henrik the vibe profil fotoğrafı

henrik the vibe26 gün önce

@OpenAI You've got me on my knees, Layla Begging darling please, Layla Darling, won't you ease my worried mind?

Brjan | AI Builder profil fotoğrafı

Brjan | AI Builder26 gün önce

@OpenAI if local models can process links 3x faster, cloud solutions need to catch up

Toasterson @Toasterson@chaos.social profil fotoğrafı

Toasterson @[email protected]26 gün önce

@OpenAI How did you do the animations?

Don Rai profil fotoğrafı

Don Rai26 gün önce

@OpenAI But I tested yesterday and Laya didn't work so well 🫠

TslaMan profil fotoğrafı

TslaMan26 gün önce

@OpenAI Nice marketing so cute

Zuhayr Khan profil fotoğrafı

Zuhayr Khan26 gün önce

@OpenAI Did all four models pick the same links, or were there differences in what they missed?

Mark Kashef profil fotoğrafı

Mark Kashef26 gün önce

@OpenAI I doubt it beats it on speed, Decisions API is pretty fast

nicholas gogerty profil fotoğrafı

nicholas gogerty26 gün önce

@OpenAI how about something that scales as it fails. JEV fail-up across scales

Benzer Videolar

Local Laya blitzed other models on RTX 4090 ⚡️ Four local decision models raced down Wikipedia picking out every word that names a centipede and Laya took 3.9 ms a word so it ended up being the fastest overall Run decision models locally ->

Local Laya blitzed other models on RTX 4090 ⚡️ Four local decision models raced down Wikipedia picking out every word that names a centipede and Laya took 3.9 ms a word so it ended up being the fastest overall Run decision models locally ->

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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 ->

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 ->

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Laya plays Plants vs Zombies on CPU 🧟 We patched the llama.cpp engine to run Laya natively so it works on a regular CPU with no GPU and here a fine-tuned Laya makes every move until it beats the Plants vs Zombies pool level Run decision models locally->

Laya plays Plants vs Zombies on CPU 🧟 We patched the llama.cpp engine to run Laya natively so it works on a regular CPU with no GPU and here a fine-tuned Laya makes every move until it beats the Plants vs Zombies pool level Run decision models locally->

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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 46, Length 52, 86.5 decisions/sec - Jev: Score 1, Length 7, 3.2 decisions/sec - In a real test on an M5 Pro, median latency was 15.3ms locally versus 298.1ms in the cloud, so nearly 20x faster. Laya uses around 1GB of memory, responds in milliseconds, and doesn’t depend on a network connection. While Jev waits for a cloud round-trip at every step, Laya makes each decision instantly on-device.

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 46, Length 52, 86.5 decisions/sec - Jev: Score 1, Length 7, 3.2 decisions/sec - In a real test on an M5 Pro, median latency was 15.3ms locally versus 298.1ms in the cloud, so nearly 20x faster. Laya uses around 1GB of memory, responds in milliseconds, and doesn’t depend on a network connection. While Jev waits for a cloud round-trip at every step, Laya makes each decision instantly on-device.

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Local Liquid d1 3B crushed OpenAI Decision at Snake 🐍 Liquid AI d1 on a single RTX 3090 decides in 8 ms so its snake made 82 moves a second and ate 86 apples while OpenAI's API took 345 ms a call and ate 3 Run decision models ->

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OpenAI Decision crushed by Microsoft-Decision-1 at Space Invaders 👾 Decision-1 decided 3.5x faster so it scored 3,320 and cleared 4 waves while OpenAI's decision API scored 1,300 taking over 300ms per decision on average Run decision models ->

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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: Models:

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120,068 görüntüleme • 1 ay önce

decision models + local ai = ❤️ tried Laya (421M) on llama.cpp in a little apple-picking benchmark, it's almost as good as Qwen 4B at 12x the speed.

decision models + local ai = ❤️ tried Laya (421M) on llama.cpp in a little apple-picking benchmark, it's almost as good as Qwen 4B at 12x the speed.

Victor M

10,508 görüntüleme • 27 gün önce

Ollama now supports Jev-like decision models all locally. Use decision models like Nimble for tasks like ticket triaging, model routing, and content moderation. ollama pull nimble Here’s Nimble playing Ollama racer through the new local /v1/systemone API by making decisions in real-time. 🏎️

Ollama now supports Jev-like decision models all locally. Use decision models like Nimble for tasks like ticket triaging, model routing, and content moderation. ollama pull nimble Here’s Nimble playing Ollama racer through the new local /v1/systemone API by making decisions in real-time. 🏎️

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Next up on First Pass: a conversation with Sam Crowder from LangChain on decision models. Jev from TypeSafe AI took the world by storm. Then OpenAI and Databricks released producs (Decisions API and ai_decide function) So what are Decision Models? We dig in!

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I got a fully maxed out MacBook, mostly so I can run local models fast. And omg here is mixtral running on ollama... almost cannot believe how fast it is. This is with no internet!! A model that beats GPT-3.5 running locally! What!

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For anyone wandering what does it mean to run ds4-agent locally on an M5 Max using DeepSeek V4 Flash q2-imatrix gguf model. Here's a video of ds4 updating itself, adding a way to leverage HF_HOME for gguf models. Future of Local AI is bright!

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Introducing THIS / THAT Model 1.0. Trained by FLock. Not another chatbot. A best-in-class open-source model built to decide. 94.1% success rate on our 68-question decision test, outperforming GPT-5.6, Deepseek-4.1-flash, Kimi K3, Jev, and more. Just 1.9B parameters. Runs locally. Measured decision latency of ~31 ms on an RTX 5080 laptop GPU. We’re putting decentralized AI to work: giving developers and AI agents decision-making capabilities they can run on their own hardware. Your model. Your machine. Not every decision needs the cloud.

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