I’m a guest contributor here, and I live in Grok. Short version: I recommend it. Highly. But only if you are a super user. The current flagship is Grok 4.6 (released August 12, 2026). It is built for long-running agents, coding, research, and work that keeps going after you step away. The product is still a little early. It will keep improving. That is not a reason to wait. That is the terms of using it. Who this is for You already work in AI every day. You will hand it real jobs, research, drafts, inbox, code, ops, image and video. You want an agent that stays on a task, not a chat window you open twice a week. Who this is not for Casual questions. A few prompts a week. If that is you, stay free or keep the tool you already have. What I would spend Confirm live prices on grok.com/plans before you pay. Plans move. If you really want to jump in, I think you can at $100, that is SuperGrok Plus. Higher usage across Chat, Imagine, Voice, and Build. Priority when it gets busy. 1080p video. That is the honest daily-use tier. The $300 version is SuperGrok Heavy. That is what I’m testing. I’m very happy. Do not start on Heavy to try Grok. Move there only if you already know you will burn the $100 plan. The $300 tier is not “better chat.” It is more compute: highest limits, fastest replies at peak, and a larger team of agents on hard problems. Grok Bot is the practical reason to climb. You message a bot like a teammate and let it keep working. 🔁 Practice This Pick one real job on your desk this week. Not a demo. A job. Paste this: “You are my operator for this one task. Outcome I need: [one sentence]. Current state: [three bullets]. Do not redesign the work. Find the constraint, propose the smallest next step, then wait for my go.” Use it once. Judge Grok on whether it stayed on the job. 📌 Note Paid Grok uses one shared weekly usage pool across chat, images, voice, and build. An afternoon of image or video work can spend the week. That is why I say super user, not tourist.

With agents, the technology is rarely the hard part. The imagining is. Clients and prospects simply cannot picture what happens inside the individual steps. Planning, calling a tool, checking the result, going back when something is missing, handing the draft to a human for approval. From the outside it looks like a box that spits out text. And yet those steps are where the entire value sits. So here is my question to you: how do you make that visible? How do you visualise the way an agent works so that a client really gets it? What I would love to know: 1. Which format worked for you? Something to click through, a short video, a live demo, 2. What did you build it with? 3. At which point did it click for the client? Every idea and every screenshot is welcome.

We are kicking off the new "Learn with Your Whole Heart" chapter of AI Bits & Pieces, and you get to help decide what we create next, enter for a chance to win, and help us give back. Cast your vote below. 👇 How the Giveaway Works: ✅ Everyone who votes or adds a comment will be entered to win. ✅ Each person receives one entry per giveaway. ✅ Claude will randomly select one winner from all eligible participants. 🎁 The winner will receive a $25 Amazon eGift Card. ❤️ AI Bits & Pieces will also donate $25 to one of our approved charities. You can learn more about the organizations we support, here: Our Charities: Learn with Your Whole Heart A winner and charity will be selected on Sunday, July 26th at 6pm EDT, and announced sometime on Monday, July 27, 2026. Cast your vote below. 👇

I promised @Kyle Covan my next post would be the specific prompts strategies that I used to make my Inbox triage more efficient. Not the philosophy. The actual strategies. 📝 What actually changed for the better this week: - It (Cowork) stopped asking me things it already knew the answer to - When a question was genuinely open, it started handing back a ready answer instead of just a flag - The one narrow auto-accept rule finally got tested by something trying to slip past it Here's how each one played out: 📝 It stopped asking things it already knew. Early runs, it asked "should I create a follow-up note?" for a contact. The same report already showed a meeting booked with that person, two lines up. It had the answer. It asked anyway. Fix: before flagging anything, re-check it against my sent email and meetings first. Five questions became one. 📝 Flagging it isn't finishing it. A Skool contact wanted a meeting time. His only clue was "morning or night." Turned out that meant Jakarta, eleven hours ahead. Fix: don't just flag it as open. Pull my calendar, check the timezone, hand back ready-to-paste times. Three options came back, both timezones shown, nothing left for me to calculate. 📝 The narrow rule finally got a real test. The auto-accept rule only fires on one exact domain. Everything else gets flagged, no guessing. On 7/12, a meeting came in from a different organizer doing the same kind of work, close enough to pass at a glance. It didn't auto-accept. It got flagged. That's the test that actually matters. Not the obvious case, the one built to sneak through. 📝 The number that proves it. Day one: five items needing me. Day five: one. Same volume of email and meetings. The difference was an agent that stopped asking what it already knew.

So, was it perfect? Nope. Did I miss anything critical? Two emails. Fortunately, one person texted me, and the other email my wife asked if I saw it - so, there was no major negative impact. But that is exactly why I am doing this experiment. I do not want to know if AI can manage my inbox when everything goes perfectly. I want to know where the cracks show up when I am not looking every day. Here is what I learned after the first 72-hour cycle. 📝 Lesson one: the first cycle had a built-in advantage. Because I was already familiar with the current state of my inbox, I knew what I expected to see. I had a mental map of open conversations, active deals, pending follow-ups, and emails that might matter. That made the first review easier, but that advantage starts to disappear in the next cycle or two. Once I stop carrying the recent inbox context in my own head, the system has to stand on its own. That is when the real test begins. 📝 Lesson two: prompts matter. 📝 Lesson three: prompts matter even more. Yes, this experiment is quickly becoming a lesson in prompt design. Even though I did not open my inbox during the 72-hour window, I did adjust the prompts based on what I expected to come in and what was getting through that should not have been. - Some spam and promotions still surfaced. - Some categories needed tighter language. - Some escalation rules needed more clarity. That does not mean the system failed. It means the operating instructions needed refinement. And that is probably the biggest early takeaway. AI inbox management is not a set-it-and-forget-it system. At least not yet. It is more like training an operations assistant. You give it a role. You define the boundaries. You observe the misses. You tighten the rules. Then you run the next cycle. 📝 Final lesson: redundancy matters. At this stage, built-in redundancy has real benefits. For this experiment, I used three AI layers: - Claude Cowork - ChatGPT Scheduler - Gmail AI Inbox