New agent from xAI + Cursor — the easiest way I've seen to get a team of AI agents working for you. No canvas, no nodes, no terminal: you hire a bot like a person (name, job title, one-line description). Each gets its own cloud computer and signs into your tools like you do, so it doesn't need an API. - What's genuinely good. Bots read each other's job descriptions and hand work off on their own — no wiring. And instead of writing an SOP, you hit record, do the job once on screen, and it writes its own instructions. - The catch. $200/mo via Cursor Ultra (a swap, not an addition — stacked on a $200 Claude plan that's $400 for overlapping tools). Trial won't get you far, it runs Grok only, and every skill you teach lives in their cloud. The lock-in isn't the subscription, it's the training. My take: never touched Claude Code? Best on-ramp there is — but run it like an intern, read-only and draft-for-approval for a month. Already in Claude Code/Codex daily? Don't switch. On Hermes/OpenClaw on a $5 VPS? You already solved always-on. Three questions decide it: weekly browser work with no integration, mistakes cheap to catch, and $200/mo less than that time costs you. 📎 Full guide PDF pinned below — setup, pricing math, the three verdicts, and the limitations. 👉 What are you running now — and is this pulling you over? Drop it below. [Watch the video here ▶️]

Right now your business runs on stuff that only lives in one person's head — usually yours. Every decision waits on them, and if they leave, it leaves too. So I got it out: every price, process, and judgment call into one folder the company owns, that the whole team works out of. One shared brain that doesn't resign, take weekends off, or walk out with 15 years of knowledge. No code, one afternoon. - It's just a folder + skills. The folder is what the company knows (prices, customers, processes); skills are how it behaves (the rules). Gather your scattered docs into one folder and it reads them like a sharp ops manager on day one — then flags what's missing, usually the pricing that only lives in your head. One interview prompt gets that out: the AI asks, you talk, it writes your pricing rules into the folder in ~20 minutes. Turn it into a skill in one plain-English paragraph and anyone runs the job the same way, every time. - The quit test. Ask the brain what your most-depended-on person knows. What comes back is safe forever; what's missing is your homework. In the demo, the volume-pricing question that sat unanswered all weekend now comes back in seconds — routine 90% done, the one real judgment call escalated with the thinking finished. My take: it drafts, you approve (~90% automated, a person finishing the last 10%). The hard part was never the tech — it's deciding to get the knowledge out of people's heads. 📎 Full guide PDF pinned below — the folder + skills model, every prompt, and the quit test. 👉 Which person in your business would you run the quit test on first? Drop it below. [Watch the video here ▶️]

You can run a real, genuinely smart AI free on the computer you already own — no subscription, no limits, no logins — and it runs 100% offline. Rip the wifi out of the wall and it keeps going; nothing you type ever leaves your machine. This is the whole thing explained for non-technical people: what it is, which models to run, and how to start today. - What it is + why now. Cloud AI = renting a brain in someone's data center; local AI = you download the model file ("open weights") and it runs on your machine, private and offline. Why it matters now: the free models got good (the best open one is close to the top paid), you own it so nobody can pull it or log it — we just watched Fable 5 get pulled and GPT 5.6 restricted — and there's no bill and no limits. - What to run, on what. Skip the giant frontier models (GLM 5.2 is 744B — needs a data center). The "home heroes" that run on a normal computer are Qwen, Gemma, and Phi. Memory is the whole game: 16GB runs a useful model today, 24GB runs the excellent 30B class. No powerful machine? Rent a GPU by the hour — still private. - How to start. Download LM Studio (no code — search, download, chat), grab Gemma or Qwen in a size your computer handles, and use it a week for your everyday stuff. In the demo I turn my wifi off and it writes a full client email, fully offline. My take: use local for the private, everyday, high-volume work and keep a cloud model for the hardest problems — that combo is the real answer for most people. It's not for everyone: if you use AI casually and are happy paying $20/mo, the cloud's easier, and I'd rather say that than sell you on it. 📎 Full guide PDF pinned below — every model, the hardware tiers, and the exact setup steps in one place. The step-by-step guide is free inside The AI Accelerator (20k+ members). 👉 Want a follow-up on running an entire business 100% locally? Comment below and I'll build it. [Watch the video here ▶️]

Anthropic published the official Opus 5 prompting guide, and most of it is subtraction — it tells you to delete three lines you've put in every serious prompt for two years, including the check-your-work line everyone ends with. The whole thing is four blocks in the prompt, one line deleted, and one rule for reviews. No API, no code — just the regular Claude app. - The build rules. Hand over one complete brief up front (not step-by-step — that was for older models that drifted). Fence it: write what NOT to build or invent, and let it decide the cosmetics. Use two separate caps — one on the thing it builds (e.g. 5 sections), one on the chat reply. And delete "double-check and fix mistakes" — Opus 5 already reviews itself as it builds, so that line just reruns a check it already did and burns your limits. - The review rule I had backwards. When you hand it something finished, "only flag serious issues" doesn't tell it how hard to look — it tells it how much to say, and you never see the long list. Ask for "every issue, big or small, I'll decide what matters" and filter yourself after. Same page: conservative gave me 3 issues; "list everything" surfaced a long list the first run never mentioned. - The head-to-head. Same job, two prompts. The rule-built one finished fast with a 2-line reply and exactly the 5 sections I asked for. The old roleplay + step-by-step + be-thorough + check-twice prompt ran 10+ minutes and built more than I asked. Same model — the old prompt was just written for a model that needed the help. My take: they killed the ritual half — the lines you bolt on to make it "try harder." What survived and got more important is the boring half: clear instructions, real context, why it matters, an example when you need a format. If you can brief a person, you can prompt this model. Caveat: it's one week and one page, so test it on your own work before rewriting every saved prompt. 📎 Full guide PDF pinned below — all five rules, the finished prompt, and the old-vs-new head-to-head.

The install is 10 minutes. The training is the 90% nobody films. This week I built the agent that runs my whole business — sales, ops, admin — free, open source, on my own computer, zero code. The reframe: you're not buying a tool, you're hiring your first employee and training it. - What it is + how you train it. Hermes Agent by Nous Research — open source, a desktop app with no terminal, and unlike ChatGPT or Claude Cowork it lives on your machine and remembers your whole business across sessions. Connect Telegram + Zapier (Gmail, Calendar, ~9,000 apps), then write it a memory file: it interviews you on what you sell, your pricing, and your tone. Same proposal prompt that came back generic now sounds like you — same model, only the training changed. - Give it real jobs. Hand it a call transcript and it writes a full proposal, checks its own work against the call (~2 hrs → ~4 min), then saves the process as a reusable skill. Hand it a goal ("find 10 leads, draft each in Gmail") and it runs till done. Text it 3 jobs from your phone and it runs them in parallel. - It clocks in on its own. One sentence sets up a 7am brief to your Telegram; it keeps its own schedule, spins up sub-agents for big jobs, and stacks every skill it learns. Point the same 3 moves — train → build a skill → schedule — at any department. My take: you're not buying AI, you're buying an outcome. First draft only (you hit send), and since it only runs while your computer's on, park scheduled jobs on an always-on machine. 📎 Full guide PDF pinned below — every step, every prompt, and the memory-file + proposal-skill templates. 👉 Which department would you point your first agent at — sales, ops, or admin? Drop it below. [Watch the video here ▶️]