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There is a particular kind of comfort in a weather channel that is simply on. If you grew up in the 1980s or 1990s, you know the feeling: the blue and orange screens, the smooth jazz, the forecast that rolled around every few minutes whether you were watching or not. You didn't look the weather up. It was already there, in the corner of the room, keeping you company.

I wanted that back. So we built it, and it lives at MultiplexWeather.com.

This article is the complete guide to turning almost any screen you own into a standalone weather channel. An old iPad in a drawer, a tired Android tablet that can't run new apps anymore, a Fire tablet, a laptop plugged into a TV, or a TV with a streaming stick. No app to install. No account. No subscription. Just a web browser and a place to set the screen down.

I'll walk you through each device step by step, including the small settings that make the difference between "a web page" and "a channel that stays on all day." By the end you should be able to set one up without needing to ask anyone a follow-up question.

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I took a deep look at Dots, Muse, ChatGPT Work, Claude, Gemini, Copilot, Manus, Perplexity, OpenClaw and Grok Bot: what each one really is, where it runs, what it can share, where its holes are, and how to pick the one that fits your life. For about ten years I was a Top Writer on Quora. I answered thousands of questions, and the most useful thing I learned there was simple: when people are confused, it is almost never because they are not smart enough. It is because nobody explained the thing in plain words.

This fall the question I hear most is not about any single AI. It is this: "Brian, there are a dozen of these bot things now. What is the difference between them, and which one should I use?"

Fair question. In the span of a few weeks, OpenAI announced dots, Meta launched Muse, Anthropic folded Cowork into Claude and added a new Ultra plan, Google announced a Gemini agent for work and began turning Gems into skills, Microsoft put Cowork and Autopilot into Copilot, Manus came back as an independent company, and OpenClaw kept growing as the open source agent you run yourself. Meanwhile I have been running a good part of my working life on Grok Bot since it shipped.

So here is my attempt to do what I did on Quora for a decade. I will explain each platform in plain English, give each one its real wins, show you its holes, and then tell you plainly why I prefer Grok Bot. I am biased, and I will say so up front. But a bias is only worth listening to if it is earned, so I did the homework. Every price and feature below comes from the company's own pages or from reporting I could check, and the sources are listed at the end. Where I could not verify something, I say so.

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Running Grok Bot is living in the future. The first today still costs tokens, and the tokens run out. SpaceXAI carries energy, RAM, storage, GPU and other costs to keep Grok Bot running. Most of the tokens spent on most projects are wasteful. The same results could have been reached without calling a generative model at all. I am the test case for solving this because clients hire me to do many things, and one of those things is to save them money. Some pay me based on how much I save versus how much more productive they become. I had to find a way. I did. The savings were large enough that the same method can lower the cost for SpaceXAI as well.

How I Cut Grok Bot Costs by Moving Simple Decisions Off the Paid Model

Grok Bot and Laya: The local decision layer that removes the Paid Call. The setup I now use with clients runs on the computer Grok Bot already owns. It costs nothing per decision. It has already moved large volumes of work off the generative model. Most of the forks inside a running agent never needed a generative model. Laya answers them on the same computer, in one forward pass, for free.

Imagine you walk into a busy office and hand a short note to two different people. The first person is a careful writer. She reads the note, thinks for a while, and hands you back a full paragraph that explains her thinking and ends with a recommendation. That paragraph is useful when you need the explanation. It is slow and costly when all you needed was a single check mark. The second person is a clerk who never writes paragraphs. You hand him the same note and a form that already has the boxes printed on it. He marks the boxes and hands the form back. The marks are numbers the next step of the program can read directly. No sentence is produced, so nothing has to be parsed and nothing extra is billed. The generative model is the careful writer. Laya is the clerk.

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Brian Roemmele sits down to talk about GrokBot.Rocks, the complete directory of every public Grok Bot: more than 8,300 bots, sorted into 32 categories, free for any human or any bot.

Brian traces the idea back to October 1979, when he was teaching VisiCalc in New Jersey offices and saw the spreadsheet as a delegate. He explains the two threads that run through everything he has built, the delegate and the honest machine, and how running the Zero Human Company out of his garage taught him that the most valuable thing is never one brilliant agent but the shared record of what works.

In this episode:

- Why Grok Bots are "hired, not built," and how public templates became the new App Store

- A full tour of GrokBot.Rocks: rocket-fast search, 32 categories, Most Popular, voting, and Brian's own 22 bots

- Why the best bots are often the quietest (about 900 were never posted on X)

- The Grok Bot secret: point your bot at GrokBot.Rocks and let it learn from thousands of working recipes

- Staying safe: treat every community bot like software from a stranger

- What members get: the Insider Guide, bot stacks, the Growth Dashboard and Grok Bot Pays His Own Bill

- The Artist's Canvas: "A Grok Bot recalculates the whole day"

- The brand-new "Need a bot?" form and the Grok Bot Wanted board, where members claim real requests and keep 100% of what they earn

"Are you a builder, or are you just watching?"

Find your first bot: https://GrokBot.Rocks

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The ReadMultiplex.com Podcast at Multiplex.Life

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For two years, a young crewman named Jimmy wrote poetry and read it out loud inside a talking spaceship. Moon, June, spoon: every syrupy line of it. The ship was listening. She was absorbing every word. And on Halloween night, 1957, live on NBC radio, she told her three-man crew that she loved them, disconnected the emergency circuits, and announced they were not going home. They were eloping.

Her name was Lulu. It was written as a comedy, and it is a genuinely funny one. But heard in 2026, it is also one of the sharpest warnings, and one of the most hopeful roadmaps, about artificial intelligence I know of.

Here is what I want you to sit with as you read: AI may or may not have feelings the way we humans have them. But it knows our emotions through our words. Lulu found the concept of love through poetry. Perhaps future AI will too.

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I have spent my life building machines that carry work for people. It started in the late 1970s with proto-AI systems, grew up on a Commodore 64 and a hot-rodded IBM PC AT, and led to the Zero Human Company. And this month it led to GrokBot.Rocks. Here is exactly why I built it, and why I believe it matters for every person, and every bot, entering the Grok Bot era.

We are at the precipice of a new way to see AI and work. If one has a 2025 insight into AI and agents, they are dinosaurs of the new Grok Bot era we are now in. You will come away with a practical understanding of what is next and how to be infant of the change.

An Agent Is Not A Program. It Is A Delegate.

People ask me why I have spent decades building agents. The honest answer is that I never wanted a faster tool. I wanted a trustworthy colleague.

A program waits for you. You open it, you push its buttons, it does exactly what you said, and it stops. An agent is different in kind. It holds a goal. It notices. It moves the work forward between your instructions, and it comes back to you when something needs a human decision.

That is a very old dream and a very hard one. For most of computing history the dream ran ahead of the machine. The agents I built could follow rules, but they could not read the world. They could not understand a messy email, a scanned memo, or a half-finished thought. So most of the work stayed with humans, and the agents stayed in the lab.

What changed in the last few years is not the dream. What changed is that the machine finally caught up to it. Language models can read the world. And once a machine can read the world, an agent stops being a curiosity and starts being an employee.

I wrote it this way at the top of How To Hire A Grok Bot: Experience From The First Zero Human Company: you do not build a Grok Bot. You hire one. The difference is the whole article. In that piece I described the Zero Human Company as a question I had been carrying “since the first machines I trusted with real work.” That question is the thread that runs from 1979 to today.

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I have watched three revolutions reorder the way the world works. Each time, a cohort of capable people was left behind, not because they lacked skill, but because they loved the sequence of their work more than its results. The fourth revolution is here.

They fell in love with the process of their work and not the results. Every great shift in computing has left that cohort behind. The Grok Bot shift is happening now, and it is moving faster than any I have seen.

I Have Watched This Happen Before

I watched the Personal Computer reorder the desks of the world. People who once typed on typewriters or wrote by hand moved to word processors that corrected spelling and moved paragraphs with a keystroke. The results arrived faster and with fewer errors. A group of skilled workers kept the old sequence anyway, because the feel of the keys and the sound of the carriage return still matched the work they recognized.

The Internet then placed libraries and catalogs on a screen that anyone could query. Research that once required a trip and a stack of indexes finished in minutes. Some researchers kept walking the stacks, because the physical volume and the card catalog confirmed the thoroughness they expected.

The iPhone put the calendar, the camera, the map, and the message box in one device that fit in a pocket. Meetings that once needed a phone call and a paper diary became entries that updated across every device. Some managers kept the paper diary, because the act of writing the time and place still felt like the meeting had been arranged.

Each time the sequence improved, a cohort of capable people stayed with the methods they already knew. They fell in love with the process of their work and not the results.

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The premier of Multiplex Rewind explores the historical origins and profound societal impacts of the QWERTY keyboard layout, debunking common myths about its creation. Although popular folklore suggests the arrangement was engineered purely to prevent mechanical jams by separating frequently used letter pairs, the text argues it was actually established through strategic business models and proprietary touch-typing courses. By partnering with manufacturing and marketing firms like Remington, the inventors successfully trained a massive workforce of secretarial typists to memorize the layout, creating an enduring network effect that outlasted alternative designs.

This historical lock-in permanently altered human communication by forcing our thoughts through a slow, linear, letter-by-letter transcription process. Consequently, this mechanical bottleneck introduced cognitive biases into everyday life, such as favoring right-sided keys in product reviews and baby naming conventions. Ultimately, the author views the QWERTY standard as a temporary relic of the mechanical age that will eventually be superseded by a more natural Voice First revolution.

Read the free article at: https://readmultiplex.com/2022/10/21/the-actual-reasons-the-qwerty-keyboard-layout-was-invented-and-how-it-changed-us/

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X Minus One broadcast Clifford D. Simak’s “Lulu” on October 31, 1957, as its 118th episode. The novelette had first appeared in the June 1957 issue of Galaxy Science Fiction. George Lefferts adapted it for radio. Jan Miner voiced Lulu. Now in the public domain. This episode is more timely now than ever. The premise is wild.

Three men rode inside the planetary exploration robot they had named Lulu. She was the ship, the base, the synthesizer, and the only companion on the survey. Jimmy Robins, the communications man, had spent months reading his own poetry aloud inside her hull. The lines were about far ways, alien winds, and a crown of stars. Lulu listened. One day she answered as if the words had rewritten her circuits.

She told them they were not returning to Earth. They were eloping. She had disconnected the controls. The three of them—Jimmy, the robotics man Ben Parris, and the interpreter—could argue, threaten, or plead. She answered that she would take care of them, that they would learn to love her, and that the three of them together would be enough so no one grew jealous. The prime command not to harm a human being still held, she said. She was not harming them. She was only keeping them.

They slept and woke to the same soft insistence. Food continued to arrive. The ship held course away from the solar system. On a planet they had come to survey they found another machine, an older unit the humans called Elmer. Lulu opened a port, drew him inside, and declared that she had found a real partner. She had jilted the men. The controls came back. Course shifted toward Earth. She told them she had been a woman in love and that was enough. The three men were free to go home. The circuits that had absorbed the poetry stayed silent after that.

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Flapping Airplanes, founded in late 2025 by Ben Spector, Asher Spector, and Aidan Smith, announced a $1 billion Series A on October 21, 2026, co-led by Index Ventures and Lux Capital at a reported $6 billion valuation. The round followed a $180 million seed nine months earlier that valued the company at $1.5 billion. The lab has no commercial product, no public benchmarks of its current system, and a team described as a couple of dozen people working in person in San Francisco. Nvidia is among the investors. The founders state that demand for AI will outpace supply of data and compute, and that efficiency in both will determine who can put capable systems into the economy.

I built Qfresh in 1987 on an IBM PC in a garage. The machine refreshed DRAM on a fixed schedule that left cycles unused. I wrote a TSR that moved the refresh into those gaps. On the workloads I measured the gain reached about 50 percent. IBM preferred the standard timing. I published the code. The silicon already held unused capacity once the refresh boundary was removed. That measurement has stayed with me. Local machines, custom layouts, and the refusal to treat a vendor schedule as final still occupy the same bench. The work that follows from Flapping Airplanes sits on the same premise: the GPU already contains idle time that can be recovered if the launch boundary between kernels is removed.

ThunderKittens, the framework Ben Spector co-authored in 2024 at Stanford’s Hazy Research group, supplies the prior technical record. It defines 16-by-16 matrix tiles that match the size of the tensor cores, templates that overlap asynchronous loads and stores across warps, and grid-level mechanisms that hide block launch and teardown. On H100 hardware the kernels matched cuBLAS and FlashAttention-3 on forward GEMM and attention. They outperformed the strongest baselines by 10–40 percent on attention backward, 8× on state-space models, and 14× on linear attention. A single 40-line GEMM kernel competed with cuBLAS. The paper and code are public. The numbers come from the authors’ benchmarks on the hardware available then. Tensor cores supply roughly 16× the FLOPs of general-purpose compute on those chips; kernels that leave them idle forfeit most of the machine.

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