JEV + Grok Bot combo is a gamechanger... I simply can't comprehend why everyone isn't building with JEV and Grok Bot yet. In my workflow, this cut costs and time by ~80%. I think it's one of the best ways to use it: → Pick relevant project notes before loading the context → Route suitable tasks to a faster worker → Choose a recovery path when a tool fails → Run focused checks before the full test suite Grok Bot handles the hard reasoning. JEV just picks from options the harness prepares and validates. I explain how to build a Grok Bot agency in the article below 👇

JEV + GROK BOT is insane... my inbox now sorts itself overnight Grok Bot reads a shared mailbox on its own cloud computer while i sleep. Jev asks four questions about every message in one call By morning there's a short list and drafts waiting, and nothing has been sent. [copy this into Grok Bot:] "Every night at 1am, open the shared mailbox and go through everything that arrived since the last run. For each message call TypeSafe System One once with four questions: which team owns it (choice: ops, finance, legal, nobody), does it need a reply today (noul), does it only look urgent (noul), and tone on a 0 to 3 scale (score). Anything under 0.5 confidence goes to a review pile instead of a queue. Write a draft reply only for messages that need one today. Do not send anything, do not archive anything, and leave every draft for my approval. In the morning give me one summary: counts per bucket, the drafts, and what you filtered out with the reason." Give it to your Grok Bot and check out the full guide in the article below ↓

Jev vs. LLMs, clearly explained The key difference is not that Jev generates faster. Jev does not generate text at all. A traditional LLM receives context and produces an answer one token at a time. Even when the output is a small JSON object, every token depends on those generated before it. Jev receives the same context but evaluates predefined decisions directly. When those decisions are independent, it can evaluate all of them in parallel. Consider an agent handling a failed deployment. It may need to determine: → Whether the incident is urgent → Which team should handle it → Whether the proposed command is risky → Whether the task is complete An LLM generates a response containing these answers sequentially. The application then parses and validates it. With Jev, you define the questions and expected answer types upfront. It evaluates them together and returns typed answers with probabilities. LLMs generate new language when the answer space is open. Jev evaluates known paths when the answer space is bounded. The article below is a full breakdown explaining Jev and where it fits. Check it out ↓

Jev + Grok Bot is the best agent setup I've built so far it's cheaper and faster than 95% of agent stacks i've seen, and the setup takes just 5 minutes: your prompt → Grok Bot → Jev decides → Grok Bot acts → result step 1 → go to @typesafeai and create an API key. don't paste it into any chat step 2 → ask Grok Bot to save it as TYPESAFE_API_KEY in the secret field step 3 → have Grok Bot install typesafe-sdk on its Agent Computer and run a quick system_one test with one Choice question step 4 → ask Grok Bot to build a small usage lab: router, dry-run mode, config and logs. or just clone my repo below step 5 → add a skill called jev-usage-router: before opening a browser, starting research, retrying a task or spinning up another bot, it asks the router first and follows the answer step 6 → run it in shadow mode first and read the logs. switch to active only once you trust the calls. keep a kill switch: bypass Jev or set enabled: false step 7 → go active. Jev picks the route, Grok Bot carries it out, and anything irreversible still waits for a human i've been running it on routine tasks and the difference is hard to ignore there are a hundred ways to use this pair, so the real advice is to set it up early and start learning where it helps grab the setup, then read the full article below ↓

SpaceXAI Engineer Lauren Tan: "99% of people will keep using Grok Bot wrong" Grok Bot is turning engineers into managers of autonomous agent teams. Now a single engineer can orchestrate an entire team of AI agents. In a 1-hour guide, a SpaceXAI engineer breaks down how to unlock the full potential of any agentic tool. These practical insights worth more than any $500 online course. Watch the video today and check out Lauren's guide below

Jev Founder, Diogo Almeida (ex-OpenAI): "I am the one who made ChatGPT and who also hates on ChatGPT. Claude Code and ChatGPT are still part of the assistance era - JEV is the next step for AI x200 faster, x400 cheaper, 0 hallucination, no human in the loop - that's JEV, this is how LLMs will look like" in a 35-minute tech talk, the Jev Founder explained why current LLMs aren't a thing anymore and what's coming next for AI this is worth more than a Stanford Machine Learning course watch today no matter what, then learn how to start using Jev in the article below

Jev is for the parts of your agent that just decide something. which tool to use next. is this worth keeping. does a human need to see this. those aren't writing jobs, and you're paying a big model to do them anyway. four good places to put it: 1/ picking the cheap path before the expensive one runs 2/ checking a step before it actually happens 3/ checking the result before it goes out 4/ trimming a long run down to what still matters start with the last one. easiest to add and you'll notice it on the bill. Check out the full guide on Jev it the article below ↓

every Grok Bot on your account can reach every login you've ever used on it. isolation is per user, one shared cloud computer, and the separate screens are work surfaces rather than trust boundaries. the research Bot reaches what the ops Bot logged into, the throwaway Bot reaches both. people are connecting production credentials to this on the assumption that separate Bots mean separate access. check out this documentation, then read the full guide on Grok Bots below ↓

20 GROKBOT TIPS from SpaceXAI team SpaceXAI team just released an official guide on “How to Use Grok Bot at 101% of Its power” Save this tips list. Then read how to build a team of GrokBot agents from scratch in the article below. Here are my top 20 notes from this guide:

holy sh*t. turns out every GrokBot on your account can see the same logins and files. they don't get separate accounts. one shared computer, one shared set of credentials, and anything one Bot can reach, all of them can reach. that's from a community list someone put together, and it has a whole section on what goes wrong: → "Always allow" doesn't always allow. commands still get blocked → the cloud computer trips site security. X will lock the login → local MCP servers don't work at all, confirmed by staff → delete your Cursor account and the link breaks in a way you can't undo the rest of the list is people showing what they actually got done with it, and some of it is genuinely impressive. read the full article on Grok Bot before you hand it anything you care about ↓

OpenAI Co-founder: "You can build 10 assistants in one afternoon with GPT-6 Astra. Each handles a task you've been doing manually every day.” In 48 minutes, he explains how one person now does the work an entire team used to spend a month on. He says just keep throwing harder tasks at it, software, science, whole simulations, it handles more than you'd ask for. Watch it, then read the guide below on full Astra setup

SpaceXAI engineer (ex-Cursor): "99% of people use GrokBot like Google and only 1% are building teams with Grok cloud agents I'm running 20+ GrokBot cloud agents in a loop. I have a Chief of Staff, a PM agent and 12 workers" in a 20-minute workshop, a SpaceXAI engineer revealed how to build a team of cloud agents that work 24/7 from scratch this is worth more than a $500 agentic engineering course skip Netflix today and watch this, then read how to build a team of agents in article below

Sam Altman (CEO of OpenAI): "Every night I have a few thousand agents running with GPT-6 Astra" a few thousand of them, working overnight, watched from his phone in 45 minutes he shows what one person can build now, and how long that used to take a whole team the part nobody copies is that he is not watching most of them at any given moment watch it today, then save the full breakdown below

OpenAI Mathematician: "10,000 agents running in parallel solved a Millennium Prize problem" OpenAI launched them on 1 September after 88 hours they had a resolution to Navier-Stokes. the setup, from their own writeup: > agents subdivided into groups that could talk within the group > tools included code execution and a cached copy of the internet > the group that produced the result ran on the order of 10,000 concurrent agents this is what models like GPT-6 Astra make possible once you stop using them one prompt at a time and start building agent teams. watch the video, then read the guide below on full Astra setup for free📷

OpenAI engineer just released a 1-hour course on building agentic systems with Codex from scratch: • 00:42 - deep dive into Codex: agents, subagents • 11:34 - Codex agentic prompting patterns • 20:06 - long running Codex agents with /goal • 30:03 - building skills for Codex agents • 41:28 - self-improving loops with /automations this 1-hour workshop can replace a $500 AI engineering course watch today, then read how to build self-improving agentic systems in the article below