12 Grok Bots without Jev are just 12 separate workers running in parallel. Add Jev as the decision layer - and they start operating like one AI company. I mapped the complete setup into a 12-step blueprint you can copy: step 1 → define the outcome before building the team: start with one measurable result, not twelve Bots searching for something to do step 2 → create the smallest useful roster: one coordinator, one specialist for each stage and one independent reviewer. Add new Bots only when a real bottleneck appears step 3 → give every Bot a clear contract: define its inputs, tools, expected artifact, forbidden actions, approval boundary and failure behavior step 4 → create one project ledger outside the chats: objective, owner, current stage, artifacts, evidence, gaps, risks and definition of done become the shared source of truth step 5 → put Jev between project state and execution: Grok Bots perform the work, Jev decides which transition should happen next and code enforces the decision step 6 → route tasks through the live team: Jev can only select Bots that currently exist, are available, have the required tools and are allowed to access the project data step 7 → turn every assignment into a bounded packet: one owner, one task, one expected output, one deadline and one approval boundary step 8 → standardize every handoff: the artifact, claim map, evidence, action log, known gaps and proposed next step move together instead of disappearing inside chat history step 9 → separate research from approval: every important claim becomes accept, verify_more or reject before it can reach the final artifact step 10 → replace fake “done” with a completion gate: objective coverage, exact checks, evidence quality, unresolved gaps and approval state decide whether the workflow stops or continues step 11 → divide autonomy by consequence: reading, research and drafts can run automatically; sending, publishing, spending, deleting and production changes still require you step 12 → automate only proven loops: complete the workflow once, repair its failure modes, save it as a skill, test it and only then turn it into a repeatable routine the result: Grok Bots keep the work moving, Jev decides what should happen next, evidence survives every handoff and you only step in when the decision carries real consequences Save this, then send the complete 12-page Jev × Grok Bot blueprint to your Grok Bot coordinator and coding agent ↓

Whoever leaked this AI system has zero survival instinct. It made $14,300 last month by doing one thing: Finding what people already watch → recreating the pattern → publishing at scale This 17-second clip is a perfect example: A house cat walks toward an empty frame. A lion appears behind the glass. The magician covers it with a blue curtain. Seconds later, the lion walks onto the stage while the cat is still sitting beside him. But the video itself is only one piece. The real system looks like this: Research → Claude → CapCut → Make → GeeLark → Published Step 1 → Research finds video formats already performing way above normal Step 2 → Claude turns the winning format into a hook, five-shot script, narration, and visual plan Step 3 → CapCut builds the finished vertical video with motion, captions, voice, and music Step 4 → Make publishes it wherever automated uploads are supported Step 5 → GeeLark handles platforms that require the real mobile app, giving each account its own virtual phone, login, proxy, and posting task The workflow was tested across 12 profiles. Each virtual phone can open the real TikTok app and publish from its assigned account. Runtime starts at $0.007/min after included minutes and caps at $1.20 per device per day. The real advantage isn't making one viral-looking video. It's turning: Proven Idea → Script → Video → Publishing Into one repeatable system. The complete Research-to-GeeLark workflow is mapped out in the article below ↓

I gave Space Bunny Alpha one prompt: build an interactive deep ocean experience from scratch. A few seconds later it was already writing the whole thing in OpenCode. It built ABYSS with the UI, animations, depth-based scrolling and interactions, then ran the build and fixed everything itself. What stood out most was the speed. It feels very much like a Flash-level model, but still capable enough to take a vague creative idea and turn it into a working frontend without much hand-holding. Definitely one of the more interesting models I've tried for fast coding workflows.

222 guaranteed Snurps spots were gone in around 20 minutes. And because each spot required $100K in volume, that means at least ~$22.2M in combined trading volume behind that list. Gated mints can definitely be more hype than substance, but 20 minutes is still 20 minutes. To me, the more interesting signal is that people are actually putting volume through @letsCatapult to qualify, not just signing up and disappearing. Mint is September 30. If you missed the guaranteed list, the WL form is still open and you can trade through my link: catapult.trade/r/LUNAR

#DreaminaCaughtTheVibe I like where @Dreamina_ai is going with this update. Dreamina AI updated its web experience and canvas, and it’s live now. The more interesting part for me is the new AI video workflows and skills for film, brand ads, viral social content, and more. You can start from a real creative workflow and adapt it to whatever you’re making. Less time figuring out how to get there, more time actually making something. Dreamina Basic is also 90% OFF for your first month until October 9th. I’d probably start by testing one of the workflows on a real project.

ex-Apple engineer gave Grok 4.6 two real jobs inside Cursor, went to sleep, and opened the results live the next morning no babysitting, no checking every generation, just an agent running on real work for hours 0% → 00:42 - reveal the website Grok redesigned overnight 25% → 23:21 - go from one voice prompt to a full software stack 50% → 45:43 - inspect the generated code and architecture 75% → 1:21:26 - compare Grok 4.6 vs Opus 5 100% → 1:46:31 - run a live PR + Cloud Agent workflow Most coding demos test an AI for 5 minutes This tests what actually matters for agents: can the model keep working when you stop watching it? The endgame isn't prompting faster It's giving an agent a job at night and reviewing finished work in the morning Worth watching before you choose which model runs your overnight agent loops

i’m building the harness layer that makes AI agents dependable: clear tasks, relevant context, safe tools, durable memory, verifiable results, and recovery when things fail. We’ll use the funds to build it properly - robust, secure, and ready for real-world use. Thank u for all guys!

someone launch token for my harness engineering looks so interesting, really thank u guys for supporting, i’m collecting fees for update my project!

SpaceXAI engineer: "We built GrokBot from zero in about a month Now I have one agent orchestrating a team of 100+ GrokBot agents at once" GrokBot → Templates → Chief Agent → Agent Teams → Autonomous Systems In this 30-minute workshop, a SpaceXAI engineer shows how to use templates and build better teams of agents from scratch One agent coordinates the entire system while the rest execute the work Worth more than most 3-hour lectures on agentic engineering Watch it today Then read how to build a chain of GrokBot agents from scratch below

Going from $0 to $140M ARR in 90 days is a crazy number. But at Deel's scale, even something as basic as payment reconciliation can become a huge operational problem. Money comes in, but figuring out who sent it, what it's for and which invoice to close can mean jumping between a bank portal, billing records, sender history and internal tools. And some of those systems don't even have APIs. That's why I like the approach @get_akai is taking. You show it the workflow once. It watches how you actually do the job, figures out which systems it needs, builds the connections and turns the whole process into an agent. Even the weird edge cases become part of the workflow as it keeps running. So instead of spending weeks explaining an automation how your process should work, you teach it by doing the work. That's a much more interesting way to think about ops automation.

SpaceXAI engineer Lauren Tan: "99% of people are using GrokBot with less than 1% of its actual power They're running a single agent without loops or graphs" "I'm running a fully autonomous team of 20+ GrokBot agents One Chief of Staff, one PM, and 20+ workers That's the new engineering stack" GrokBot → Chief of Staff → PM → Workers → Autonomous Fleet In this 1-hour session, a SpaceXAI engineer shows how to build a complete AI agent team from scratch This workshop is worth more than most $1,000 agent engineering courses Bookmark it and watch today Then read the full article below

Andrej Karpathy spent 8 years working at OpenAI and Tesla Last week, he condensed everything he knows into one free 2-hour lecture People spend $15K on bootcamps that teach less than half of this You probably don't have two hours to watch it right now Just don't let it disappear in your feed Save it, watch it later, then read the guide below and build your first loop