X creator payouts just changed — and not everyone gets a say

If you make money on X through subscriptions or the platform’s content rewards, your next check isn’t coming from Stripe anymore. As of Wednesday, all U.S. creator payouts are being routed through X Money, the company’s in-app payments service.

The shift was announced in a post from the official @XCreators account, which confirmed that both the Original Content Rewards Program and creator subscriptions now pay out exclusively via X Money for U.S.-based users.

Instant payouts sound great — but there’s a catch

The upside is speed. X says creators get their money the moment it’s sent. No more waiting for a two-week billing cycle to close. No minimum threshold blocking access to your earnings. Previously, X’s own documentation noted payouts ran every two weeks with a $30 minimum.

But here’s the thing: the announcement doesn’t frame X Money as an option. It’s the only way U.S. creators get paid now. If you preferred the old Stripe-powered system, that preference doesn’t matter anymore.

A representative for X confirmed the change is mandatory for U.S. creators. Those outside the U.S. will stay on Stripe for now.

What’s happening to X’s other creator programs?

This payout change lands right as X reshuffles its broader creator economy. On September 7, the platform will retire its Creator Revenue Sharing Program — the one that stopped taking new members last month. Creators are being pushed toward the Original Content Rewards Program instead, which leans harder into original posts rather than engagement-based revenue sharing.

It’s a meaningful pivot. The old program rewarded virality. The new one rewards originality, at least according to X’s framing. Whether that translates into better paydays for creators remains to be seen.

X Money is bigger than creator payouts

X Money is part of Elon Musk’s long-touted “everything app” vision. The service includes a bank card with 3% cash back, instant payments, and free ATM withdrawals. But it’s worth clarifying: X Money is not a bank. Accounts sit with FDIC-insured Cross River Bank.

Here’s what creators should know about the new setup:

• Payouts are instant once sent — no waiting period.
• Direct deposit through X Money could boost your APY. X Premium users get a 6% rate; standard users get 4%.
• Individuals receiving creator payouts will get a 1099-NEC form.
• LLCs must provide W-9 information so X can issue accurate 1099s.

Tax implications and fine print

That tax bit matters. If you’re an individual creator, expect a 1099-NEC from X. If you operate as an LLC, X will collect your organization’s W-9 details to keep the paperwork straight. It’s the kind of administrative detail that’s easy to overlook until tax season rolls around.

The move also means creator payouts count toward X Money’s direct deposit requirements for that better APY rate. So if you were already banking with X Money, your creator earnings just became more valuable.

What creators should do next

If you’re already using X Money, you don’t need to do anything. Your next payout will land there automatically. If you haven’t set up an X Money account yet, you’ll need to open one to keep receiving payments in the U.S.

The bigger question is whether this signals more consolidation to come. X has been steadily folding its creator tools into its payments ecosystem. For those watching Musk’s everything-app ambitions, this is another brick in the wall. For creators, it’s a reminder that platform changes come fast — and you don’t always get to vote.

Why I Spent a Week Testing Linux Distros for Windows Apps

Let me be blunt. Most people don’t switch to Linux because they love the penguin. They switch because they’re tired of Windows—the updates, the bloat, the ads in the Start menu. But there’s one thing that keeps them chained: their software.

So when a handful of distros claim they can run your Windows apps out of the box, that’s a big promise. I decided to put five of them through real-world testing. Not just installation, but actual daily use. Opening documents, running utilities, even playing a game or two.

One distro stood out so clearly that I’m almost embarrassed it took me this long to try it properly.

What I Tested and How

I used a spare laptop with an Intel Core i5, 8GB of RAM, and a 256GB SSD. Nothing fancy—a typical machine someone might repurpose. For each distro, I did a fresh install, then tried to run a set of common Windows programs:

• Microsoft Office (via the web installer)
• Adobe Acrobat Reader
• 7-Zip
• Spotify
• Notepad++
• Steam with a lightweight game

I gave each distro a fair shot. Updated everything, installed the recommended tools, and followed the official guides. Here’s what happened.

Zorin OS: The Pretty One That Almost Made It

Zorin OS is often recommended to Windows refugees. Its interface mimics Windows closely, and it ships with a tool called Zorin Connect that helps with phone integration. The setup process for Windows apps is straightforward—you install Wine, and you’re off.

In practice, it worked—but with friction. Office installed, but the ribbon felt sluggish. Notepad++ ran fine, which is expected. Spotify worked, but only after I installed an older version. The game? It stuttered enough to be annoying.

Zorin is a decent introduction, but it’s not the best Linux distro for Windows apps. It’s more of a gentle on-ramp for people who want a Windows-like desktop, not a powerhouse for running Windows software.

Linux Mint: The Reliable Workhorse

Linux Mint is beloved for its stability. And it didn’t disappoint—everything installed without crashing. But “without crashing” isn’t the same as “running well.”

Adobe Reader worked, though the interface looked dated. 7-Zip was flawless. But Office was a different story. The installer took forever, and once it was done, the apps opened slowly. Saving a document occasionally caused a delay that made me nervous.

Mint is a great daily driver for Linux-native software. For Windows compatibility, it’s merely acceptable. You’ll spend time tweaking settings and hunting for workarounds that other distros handle automatically.

Pop!_OS: Built for Gamers, Not for Windows Users

Pop!_OS has a reputation for gaming, thanks to its NVIDIA driver integration. I hoped that would translate to better Windows app support. It didn’t.

Steam worked well—no surprise there. But the rest was a mess. Office wouldn’t install at all. Spotify’s Snap version kept crashing. I spent more time in terminal troubleshooting than actually using the system.

Pop!_OS is excellent for gamers who want a Linux-native experience. But if your priority is running Windows software, you’re better off elsewhere.

Ubuntu: The Default Choice That’s Just Okay

Ubuntu is the distro everyone recommends to beginners. It’s polished, well-documented, and has the largest community. That means when you run into problems, you’ll find solutions quickly.

And you’ll need them. Ubuntu’s default Wine setup is fine for basic apps, but anything complex requires additional configuration. I got Notepad++ and 7-Zip running easily. Adobe Reader took some work. Office was a no-go without a virtual machine.

Ubuntu is a safe choice for general use, but it doesn’t offer anything special for Windows app compatibility. You’re essentially on your own, relying on community guides and trial-and-error.

Bottles: The Unexpected Champion

Here’s where things get interesting. Bottles isn’t a full distro—it’s a compatibility layer that runs on top of Linux. But it’s so good at what it does that it deserves a place in this comparison.

Bottles simplifies the entire Wine process. Instead of manually configuring prefixes and dependencies, you create a “bottle”—a virtual environment for your Windows apps. Each bottle can have its own settings, runners, and dependencies. It’s like having a dedicated Windows machine for each program.

I installed Bottles on Ubuntu, and the difference was night and day. Office installed without a hitch. Adobe Reader worked perfectly. Even the game ran smoothly, with minimal tweaking.

The secret is Bottles’ dependency management. It automatically installs the libraries and components each app needs, so you don’t have to figure out why something isn’t working. It’s the closest thing to a plug-and-play experience for Windows software on Linux.

Which Distro Should You Choose?

If you want the absolute best Linux distro for Windows apps, pair a stable base like Ubuntu or Linux Mint with Bottles. That combination gave me the smoothest experience, with the least amount of headache.

Zorin OS is a good starting point for beginners, but you’ll eventually want Bottles there too. Pop!_OS is great for gaming, but don’t expect miracles with Windows software. And while Ubuntu and Mint are reliable, they need Bottles to truly shine.

My final setup: Linux Mint + Bottles. It’s stable, fast, and handles Windows apps better than any distro I tested on its own. If you’re considering making the switch, that’s where I’d start.

And if you’re curious about other ways to bridge the gap, check out our guide on using Wine for Windows apps and our comparison of Linux distros for beginners.

Why Your AI Output Still Sounds Like Everyone Else’s

You’ve noticed it, right? You ask ChatGPT or Claude for a blog post, a strategy doc, or a client report, and what comes back is… fine. Technically correct. Grammatically clean. But utterly forgettable.

The problem isn’t the model. It’s the way you’re prompting it. Most people treat AI like a vending machine: type a request, get a generic result. That’s a waste of the tool’s real power.

The fix? Two techniques that professional AI users swear by: building detailed AI personas and creating structured feedback loops. Together, they can transform your output from bland to genuinely impressive.

What Is an AI Persona and Why It Matters

An AI persona is a detailed role you assign to the model before giving it a task. Instead of saying “Write a marketing report,” you say “You are a senior marketing analyst with 15 years of experience in B2B SaaS, known for concise, data-driven insights.”

That single shift changes everything. The AI doesn’t just generate words — it generates words filtered through a specific lens. It adjusts tone, vocabulary, and structure to match the persona you’ve defined.

Here’s a concrete example. Ask for “a product description” and you’ll get something generic. But ask for “a product description written by a skeptical tech reviewer who’s seen hundreds of similar tools,” and suddenly you get critical thinking, nuance, and specificity.

How to Build a Powerful Persona

Don’t just say “you’re an expert.” That’s too vague. Instead, give the AI a full backstory. Include:

• Role and seniority: “You are a chief financial officer” vs. “You are a junior accountant.”
• Industry context: “You work in healthcare” vs. “You work in e-commerce.”
• Communication style: “You write in short, punchy sentences” or “You prefer formal, academic language.”
• Audience awareness: “You’re writing for C-level executives who hate jargon.”
• A specific point of view: “You believe traditional marketing is dead and say so.”

The more specific you get, the better the output. It’s like giving a method actor a character sheet — the performance becomes believable.

Feedback Loops: The Secret to Consistently Better AI Output

Here’s the thing about AI: it doesn’t remember your preferences unless you tell it. And it doesn’t learn from its mistakes unless you correct them. That’s where feedback loops come in.

A feedback loop is a structured process where you evaluate the AI’s output, identify what’s wrong, and feed that criticism back into the next prompt. Over multiple iterations, the quality compounds.

Think of it like editing a junior writer. You wouldn’t just fire them after one bad draft. You’d point out the issues, explain what you want, and have them try again. AI works the same way.

Build a Simple Feedback Loop in 4 Steps

1. Generate a first draft with your persona prompt.
2. Critique it honestly. What’s missing? What’s too generic? What’s factually shaky?
3. Feed that critique back. Say: “You’re on the right track, but the introduction is weak. Make it more specific. Also, you used the word ‘leveraging’ three times — cut that.”
4. Repeat. Usually, two or three rounds are enough to get something great.

This isn’t just a hack — it’s a workflow. And once you internalize it, you’ll stop accepting that first mediocre draft.

How to Use Feedback Loops for Different Deliverables

The technique adapts to whatever you’re producing. Here are a few examples:

For Blog Posts and Articles

Use a persona like “You are a veteran journalist at a major publication who writes for a smart, busy audience.” After the first draft, ask: “Where is this article weakest? Rewrite the middle section with more concrete examples and shorter paragraphs.”

For Client Reports

Create a persona that matches your client’s industry and expectations. Then, in your feedback, push for clarity: “Simplify the executive summary. The client doesn’t care about methodology — they care about results.”

For Social Media Content

Give the AI a brand voice persona. Then iterate on hooks: “Write 10 different opening lines. I’ll pick the best.” That’s a feedback loop in itself — you’re using the AI to generate options, then curating.

Common Mistakes That Sabotage AI Quality

Even with personas and feedback loops, people still mess up. Here’s what to avoid:

• Being too vague: “Make it better” isn’t feedback. Be specific.
• Ignoring context: If the AI doesn’t know your audience, it can’t tailor the output.
• Not iterating: One round of feedback isn’t enough. Expect to go back and forth.
• Forgetting to save good prompts: When you find a persona that works, save it. Build a library of effective prompts.

Another common trap: expecting AI to be perfect on the first try. It won’t be. But with a solid feedback loop, you’ll get to excellent in a fraction of the time it would take to write from scratch.

Putting It All Together: A Simple Workflow

Here’s a practical workflow you can start using today:

1. Define the task and the audience.
2. Write a detailed persona prompt.
3. Generate the first draft.
4. Critique it against your standards.
5. Feed the critique back and regenerate.
6. Repeat until the quality meets your bar.

This isn’t a one-time trick. It’s a habit. And once you start using AI personas and feedback loops consistently, you’ll notice a dramatic improvement in the quality of your content, reports, and deliverables.

For more on getting the most out of AI in your marketing, check out our guides on AI content strategy and prompt engineering for marketers. The tools are only as good as the way you use them.

So next time you’re about to accept a mediocre AI draft, stop. Ask yourself: did I give it a persona? Did I give it feedback? If not, you’re leaving quality on the table.