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Deploying DiffusionGemma-Jev (djev) just got a lot easier. You can now spin up a Jev API-compatible endpoint on Google Cloud Run using a single command. Performance is solid: ~35-60 ms for single step latency and batch@32 is ~100-123 requests/sec. It's a straightforward way to experiment without needing your own... show more

Google Gemma

101,747 subscribers

627,376 Aufrufe • vor 2 Tagen •via X (Twitter)

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31 Kommentare

Profilbild von Google Gemma

Google Gemmavor 2 Tagen

Credits to @mmastrac and @dylayed. Original post:

Profilbild von Jonathan Ouyang

Jonathan Ouyangvor 2 Tagen

@bodonoghue85 Fastest bookmark of my life

Profilbild von Viren Khandal

Viren Khandalvor 2 Tagen

jev on blackwell in the big 2026 🥀 what are we doing

Profilbild von ORC KILLER

ORC KILLERvor 2 Tagen

but why ? jev is cheaper than $3/h and way better at it

Profilbild von Everlier

Everliervor 2 Tagen

Infinite Jev unlocked

Profilbild von hey

heyvor 2 Tagen

Put some pressure on Gemini team so you guys can distill for Gemma 5 soon 😌

Profilbild von iMuffin

iMuffinvor 2 Tagen

this is exactly, what i asked for. thanks!

Profilbild von Vincent Myshinkye

Vincent Myshinkyevor 2 Tagen

This is the kind of deployment UX that makes experimentation actually accessible. Spin it up when you need it, pay while it runs, and let it disappear when you’re done. 🔥

Profilbild von Ibrahima Sarr

Ibrahima Sarrvor 2 Tagen

When I was seeing Jev explanation I was asking myself, isn't what DiffusionGemma already doing. Is it the similar, is it that Jev just has more hype?

Profilbild von Red Wizard 🔮 (e/acc)

Red Wizard 🔮 (e/acc)vor 2 Tagen

Coming to the Gemini API/AI Studio?

Profilbild von Sadnan

Sadnanvor 2 Tagen

lmao, didnt see this coming

Profilbild von The AI Therapist

The AI Therapistvor 2 Tagen

Single-ste latency is the real flex. 35ms means you can feel the stream before your coffee cools, smooth workflow vibes that actually respect a mom’s attention span. Efficient code looks good on everyone. 😊

Profilbild von TensorQuay

TensorQuayvor 2 Tagen

One practical limit in the repo: the Cloud Run recipe is set to one GPU instance and 32 concurrent requests. The 100–123 req/s figure is a warm batch result; it says little about the wait a new caller sees when that GPU is busy.

Profilbild von pysolin

pysolinvor 2 Tagen

google's like you want ML, we got ML

Profilbild von 𓂀♎︎𓂀

𓂀♎︎𓂀vor 2 Tagen

Oh ok Google.

Profilbild von Bruno B. Sant' Anna

Bruno B. Sant' Annavor 2 Tagen

Interesting! How does this compare to running similar models on other serverless platforms like AWS Lambda or Azure Functions in terms of cost and cold start times?

Profilbild von Kyle Kelley

Kyle Kelleyvor 2 Tagen

@mmastrac you mad lad

Profilbild von John Rood

John Roodvor 2 Tagen

Don't add --cpu-boost by reflex here. The repo reports 73-75s cold starts with it versus 47.5s without it on the 20-vCPU GPU setup.

Profilbild von Jhon Dennis

Jhon Dennisvor 2 Tagen

一条命令就能把实验跑起来这种少折腾的升级最有用普通人不用先学一堆黑话

Profilbild von 刘朝 Zhao Liu

刘朝 Zhao Liuvor 2 Tagen

The interesting part is the deployment contract: Jev-compatible API, scale-to-zero, and a single Cloud Run command. The next useful artifact is a cold-start and tail-latency trace under real bursty traffic, because “$0 when idle” matters only when wake-up cost stays predictable.

Profilbild von Y11

Y11vor 2 Tagen

@grok 这个有什么业务意义呢？

Profilbild von Karios

Kariosvor 2 Tagen

Gemini 4 pro gelene kadar tüm yan görevleri yapıyorlar

Profilbild von gab

gabvor 2 Tagen

OMG GEMMA I LOVE YOU

Profilbild von Fajar M Reza

Fajar M Rezavor 2 Tagen

Single-command deployment is the kind of boring improvement engineers quietly celebrate.

Profilbild von Hidde

Hiddevor 2 Tagen

how smart is this though?

Profilbild von extracsw

extracswvor 2 Tagen

Local?

Profilbild von Noble Savage

Noble Savagevor 2 Tagen

AI is getting so advance its going backwards to 2017. 😂

Profilbild von 🇺🇸🏴‍☠️

🇺🇸🏴‍☠️vor 2 Tagen

Heh heh

Profilbild von Jigs

Jigsvor 2 Tagen

Need to check if laya is better or djev? 🤔

Profilbild von Chase McDougall

Chase McDougallvor 2 Tagen

Is there a blueprint like this for an openai compatible version? Can already get structured output that way. Jev format just seems like nerfing Gemma

Profilbild von Sonath

Sonathvor 2 Tagen

google made deploying its diffusiongemma image model a single cloud run command, $3 an hour and zero when idle. the gpu shortage is just a pricing strategy now.

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194,422 Aufrufe • vor 4 Tagen

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40,204 Aufrufe • vor 6 Tagen

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Simplifying AI

24,406 Aufrufe • vor 4 Tagen

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140,922 Aufrufe • vor 9 Monaten

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55,100 Aufrufe • vor 1 Jahr

I got a rubik's cube to solve itself with TypeSafe AI 's Jev and it solves it like a person does, 94 moves, not the 22 move optimal solution. Jev isn't an LLM, it just answers one question in ~250ms with a probability. so I put the beginner method in code (the one you'd learn on youtube: white cross, corners, middle layer, yellow layer) and at every step Jev just looks at the cube and picks which case it's in. code checks every pick. ~4 seconds of model time total. video is slowed down so you can read it!

I got a rubik's cube to solve itself with TypeSafe AI 's Jev and it solves it like a person does, 94 moves, not the 22 move optimal solution. Jev isn't an LLM, it just answers one question in ~250ms with a probability. so I put the beginner method in code (the one you'd learn on youtube: white cross, corners, middle layer, yellow layer) and at every step Jev just looks at the cube and picks which case it's in. code checks every pick. ~4 seconds of model time total. video is slowed down so you can read it!

Paolo Rosson

207,238 Aufrufe • vor 8 Tagen

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DigitalOcean

3,722,162 Aufrufe • vor 3 Tagen

GLM 5.3 Flash vs Hybrid (GLM 5.3 Flash + Jev) on a turn-based strategy game (Halite 2) The hybrid approach is 13x faster, 56% of the pure GLM 5.3 Flash API cost, and gains a slight edge in performance. In the hybrid approach: -GLM 5.3 Flash handles strategic judgment of which planets matter, who expands, who fights, when to re-plan. -Jev handles the ship-by-ship execution. GLM 5.3 Flash alone defeats Jev alone 82% of the time. This is obviously just a single example and some of the first things I've tried to make use of Jev as an augmentation to an LLM. Could potentially make even more gains in cost, speed, and performance, but using what I've figured out here, I am compelled to try some ideas with robotics next.

GLM 5.3 Flash vs Hybrid (GLM 5.3 Flash + Jev) on a turn-based strategy game (Halite 2) The hybrid approach is 13x faster, 56% of the pure GLM 5.3 Flash API cost, and gains a slight edge in performance. In the hybrid approach: -GLM 5.3 Flash handles strategic judgment of which planets matter, who expands, who fights, when to re-plan. -Jev handles the ship-by-ship execution. GLM 5.3 Flash alone defeats Jev alone 82% of the time. This is obviously just a single example and some of the first things I've tried to make use of Jev as an augmentation to an LLM. Could potentially make even more gains in cost, speed, and performance, but using what I've figured out here, I am compelled to try some ideas with robotics next.

Harrison Kinsley

51,595 Aufrufe • vor 4 Tagen

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536,864 Aufrufe • vor 6 Tagen

UCONN HC Dan Hurley - Building a System/Identity - "Our system is how hard we play. How hard we get after people every single possession at both ends of the court & on the backboard." - "The number one thing you got to understand if you want to have a program that can sustainably be successful and have a chance to win every single night... It's hard to coach that way because it's fu**ing exhausting. To coach every single possession, every single day and when the losing team runs the line because every single drill we do there is a winner & a loser, when they get this close and miss that line, you better be there to call them out and make the whole team now run with them." - "If you want to start the game 6-0, like I start most games just because my team is ready to go because we go so hard every day."

UCONN HC Dan Hurley - Building a System/Identity - "Our system is how hard we play. How hard we get after people every single possession at both ends of the court & on the backboard." - "The number one thing you got to understand if you want to have a program that can sustainably be successful and have a chance to win every single night... It's hard to coach that way because it's fu**ing exhausting. To coach every single possession, every single day and when the losing team runs the line because every single drill we do there is a winner & a loser, when they get this close and miss that line, you better be there to call them out and make the whole team now run with them." - "If you want to start the game 6-0, like I start most games just because my team is ready to go because we go so hard every day."

James Light

162,675 Aufrufe • vor 5 Monaten

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Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Roan

119,605 Aufrufe • vor 5 Tagen

Trying Gemini today It feels like Google is trying to scare customers into NOT using it with legal popups literally every step of the way Admittedly it's gotten better and now supports CURL requests without all the Google libraries to install But still no OpenAI-compatible queries yet Took 9 clicks to get an API key, and the legal popups are just way too crazy If Logan Kilpatrick can just get all the lawyers of his back, it could be great

Trying Gemini today It feels like Google is trying to scare customers into NOT using it with legal popups literally every step of the way Admittedly it's gotten better and now supports CURL requests without all the Google libraries to install But still no OpenAI-compatible queries yet Took 9 clicks to get an API key, and the legal popups are just way too crazy If Logan Kilpatrick can just get all the lawyers of his back, it could be great

@levelsio

413,444 Aufrufe • vor 1 Jahr

If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

Ruben Hassid

167,306 Aufrufe • vor 4 Tagen

fun weekend project: AutoJev. i was curious to see if i could train a competitive Jev-like model completely autonomously with a swarm of agents using our internal system. turns out you can get quite far! some details: - gave the swarm a devbox with an h200 gpu - the swarm is a mix of astra and fable; used sol and luna for synthetic data and filtering; ran for 20 hours - spent $3.1k in total ($1.9k on agents, $1.2k on data) - used qwen3.8-27b as the backbone. it underperforms Jev initially, but after training it is competitive/better, and it also supports 260k context and multimodal input - the agents tried RL, but it hasn't worked out so far, so they fell back to SFT on high-quality synthetic data - both accuracy and calibration are better - jev-compatible api, playground, and inference. on an h200 it gets around 120ms p95. that will be higher under contention, but still quite fast for an unoptimized version code: model: going to let it run longer, and will also bring up inference so folks can try it out.

fun weekend project: AutoJev. i was curious to see if i could train a competitive Jev-like model completely autonomously with a swarm of agents using our internal system. turns out you can get quite far! some details: - gave the swarm a devbox with an h200 gpu - the swarm is a mix of astra and fable; used sol and luna for synthetic data and filtering; ran for 20 hours - spent $3.1k in total ($1.9k on agents, $1.2k on data) - used qwen3.8-27b as the backbone. it underperforms Jev initially, but after training it is competitive/better, and it also supports 260k context and multimodal input - the agents tried RL, but it hasn't worked out so far, so they fell back to SFT on high-quality synthetic data - both accuracy and calibration are better - jev-compatible api, playground, and inference. on an h200 it gets around 120ms p95. that will be higher under contention, but still quite fast for an unoptimized version code: model: going to let it run longer, and will also bring up inference so folks can try it out.

Denis Yarats

27,046 Aufrufe • vor 3 Tagen

Jev is a new model from the co-inventor of ChatGPT that can use computer tools in realtime right now it's playing Minecraft on its own computer in the cloud on Orgo this is what it looks like with just one Jev, but imagine: what about a thousand? or what about a billion of Jevs? each working together on their own computers in the cloud, discovering new science, running experiments, and designing new drugs 24/7. this is going to be bigger than ChatGPT

Jev is a new model from the co-inventor of ChatGPT that can use computer tools in realtime right now it's playing Minecraft on its own computer in the cloud on Orgo this is what it looks like with just one Jev, but imagine: what about a thousand? or what about a billion of Jevs? each working together on their own computers in the cloud, discovering new science, running experiments, and designing new drugs 24/7. this is going to be bigger than ChatGPT

Nick Vasilescu

16,613 Aufrufe • vor 8 Tagen