In this post, I'm sharing only the most practical insights, focusing on three things: Where exactly is the bottleneck that's killing AI bloggers? How can you let an AI Agent scroll X, Xiaohongshu, and Reddit 24/7 to find topics for you? I'm open-sourcing the entire set of Prompts and threshold tables for 5 platforms that I've been running for two weeks—just copy them! At the end of the article, there's also a reality check and a full data review from the past two weeks. If you're trapped in the information flow, take this and use it!
This might sound a bit like humblebragging, but I need to be honest first—
I've been immersed in the Chinese AI community on X for half a year, and I've recently realized one thing: the biggest bottleneck for AI bloggers isn't being unable to write, but not knowing what to write.
I used to spend 4 hours every day scrolling through X, Xiaohongshu, and Reddit to find topics until my eyes were blurry. The result? I was still just following trends that others had written about three days prior.
That was until I completely handed this task over to a cloud phone. Now, I spend zero time scrolling through feeds. At 8:00 AM, a topic list is waiting on my desktop, and my hit rate has jumped from 15% to over 60%.
The mindset, the Prompts, and the 5-platform extensions—I'm open-sourcing everything in this post.
Without further ado, let me first explain exactly where I was stuck.
1. An AI Blogger's Day Starts with "Scrolling"
If you're an AI blogger, your day probably looks like this:
Open X to see what Sam Altman posted, what Karpathy retweeted, or which new skill is going viral. Switch to Xiaohongshu to see AI reviews, prompt sharing, or new workflows. Switch to Reddit to check the latest high-upvote discussions in r/LocalLLaMA or r/ClaudeAI. Then switch to Bilibili to see which creator released a new tutorial.
After one round of scrolling, 3-4 hours are gone.
What's even more frustrating is that by the time you see those "hot topics," others have often already finished writing and publishing them.
When you strip it down, what you're doing is essentially manual labor—using human eyes to monitor numbers, keywords, and popularity.
It doesn't require your judgment, your taste, or even your presence.
I've always felt that the essence of "topic selection" is a data filtering problem. It's not about "having inspiration"; it's about "scanning the right signals at the right time."
Can this kind of work be done by AI?
To be honest, I tried before. I used RSS, various aggregation tools, and even painstakingly wrote a few crawlers myself. They all failed for the same reason—apps like X and Xiaohongshu don't have decent APIs. The "recommendation stream" data you want only lives inside the apps.
2. The Turning Point: Letting a Cloud Phone Scroll for You
Until recently, I started using Airtap.
Let me clarify what it is—an AI Agent that can operate mobile apps. Give it a cloud phone (an Android running in the cloud), write a Prompt, and it will scroll through the phone for you.
The key point is this: it's not an API call; it's actually "scrolling the phone."
Therefore, it can read X's "For You" feed, Xiaohongshu's discovery page, and Reddit's "Hot" section—things that don't have official APIs.
You might think, how is this different from me opening my phone and scrolling?
The difference is huge.
First, it doesn't sleep.
Second, it runs on a phone with a "blank personality." It doesn't log into any account, so the recommendation stream is a pure algorithmic baseline, uncontaminated by my personal interests. It sees what the platform is actually promoting.
Third, write the Prompt once, and it runs on a schedule every day.
You can think of it like this:
When you scroll X yourself, it's like eating at a restaurant that has been completely "spoiled" by your usual tastes; the menu is always just the few things it thinks you like.
But if you want to know what the restaurant's true signature dishes are, you need a completely fresh face to go in and order again. That's what the cloud phone is doing.
3. My Specific Strategy in Three Steps
Step 1: Define Your "Signal Threshold"
In some reference samples, a blogger used "1 million+ views" as a threshold. That's for general traffic bloggers, but the AI circle is different.
The signals in the AI circle aren't in "view counts" but in "retweets + comments + author weight."
The thresholds I set for X are:
- Retweets ≥ 500
- Or Likes ≥ 2000
- Content must hit keywords: Claude / GPT / Cursor / Skill / MCP / Agent / Prompt
Why these numbers?
Because the AI community on X is an order of magnitude smaller than general entertainment. 500 retweets in AI Twitter is equivalent to 1 million views in general traffic—it's the threshold where something is "just verified but not yet saturated."
Anything below this level is noise; no one will read it if you write it. Anything above 10,000 retweets has already been written to death; you'd just be producing me-too content.
The 100-500 range is the golden zone: "verified interest + not yet exhausted by the general market."
Simply put, the most counterintuitive part of signal thresholds is that higher isn't always better. You want to pick the temperature where the food is "fresh out of the oven but no one has eaten it yet."
Step 2: Write a Working Prompt
This is the version I've been running for two weeks and iterated four or five times. You can copy it directly:
Drag it into Airtap, set it as a daily routine, and have it start running at 7:00 AM. By 8:00 AM, when you open your computer, the table is ready.
It looks like this:
This is your topic pool for the week.
Step 3: Parallel Apps—The Best Part
Reuse 90% of the Prompt above, only changing the App and the thresholds:
Run each app in parallel on a cloud phone. I currently have 4 cloud phones running simultaneously, getting 4 topic tables every morning at 8:00 AM.
You'll notice a very satisfying phenomenon: when the same "signal" appears on 3 platforms simultaneously, it's basically a must-write.
This is the multiplier effect.
Once the most exhausting task of "finding topics" is automated, the effort required to cover 5 platforms is almost the same as covering 1.
To use an analogy, it's like you used to drive one truck on one delivery route. Now, you've hired 4 tireless drivers to run 4 routes simultaneously. The fuel cost (cloud phone cost) is almost the same, but your order volume has quadrupled.
This is the compound interest of a workflow.
4. Two-Week Data Review: The Numbers are Real
I did a rough comparison.
Before (Manual Scrolling):
- Daily feed scrolling for topics: 3-4 hours
- 5 days a week ≈ 20 hours
- Per year ≈ 1000 hours
- Topic-to-article hit rate: approx. 15%
Only 1-2 out of 10 ideas could actually be turned into an article.
Now (Running Airtap):
- Daily manual scrolling: 0
- Morning review + secondary screening: 20 minutes
- Per week ≈ 2 hours
- Topic-to-article hit rate: 60%+
20 minutes versus 20 hours.
Over a year, what's saved isn't just 998 hours; it's 998 hours that used to be spent staring at a screen in a grind.
I didn't use that time to slack off; I used it for deep writing and hands-on testing.
Because I've always believed that deep writing and testing are the parts AI still can't do.
5. But I Have to Give Myself a Reality Check
I can't hype this up as a savior; that wouldn't be honest.
Airtap helps you complete "signal filtering," not "judgment."
Out of the 20 items in the table, maybe only 3-5 can be converted into articles.
Why?
Because AI doesn't know:
- What your followers care about
- What angles suit your style
- Which topics haven't been dug into deeply by others
- Which topics might offend people if written about
The work of judgment still has to be done by you.
And frankly, Airtap isn't perfect right now.
Occasionally it gets stuck on a popup, occasionally it misreads a number, and occasionally it skips a post it should have caught.
I have to adjust the Prompt about once a week—tweaking thresholds, keywords, and adding new edge cases.
I feel like I can't sugarcoat this.
Airtap isn't a savior; it's the first stage of the assembly line.
But that one stage alone has transformed me from a "manual laborer scrolling feeds for 4 hours a day" into a "content person making judgments in 20 minutes a day."
The identity has changed, and the rest is easy.
6. What I Really Want to Say at the End
The core of what I want to say is just one sentence—
The real bottleneck for AI bloggers has never been that "AI isn't strong enough," but that "your workflow hasn't put AI in the right place."
If you use AI to "help you write," you'll find it doesn't write as well as you do.
If you use AI to "help you filter," you'll find your own productivity instantly triples.
In the coming year, my judgment is that the gap between solo AI bloggers and teams will increasingly come from "workflow maturity," not "who is smarter."
As I write this, I'm still iterating.
I might change this Prompt next month, and I might adjust these thresholds again.
But I can never go back from the underlying action of "letting AI filter signals for me."
It's like someone who has driven an electric car; if you ask them to go back to a shared bicycle, they can ride it, but they won't choose to anymore.
If you are also a fellow traveler in the Chinese AI community, feel free to take this Prompt and try it. If it works, tell me the data, and I'll help you iterate the next version. Let's figure it out together.
⚡️ Airtap Official Website: airtap.ai
🌅 Follow @airtap_ai for more Routine demos
📌 If you found this useful, please give me a like / retweet so more brothers trapped in the information flow can see it.
(The Airtap mentioned in the text is just the Agent tool I use personally and a reference case mentioned in the article; it does not constitute any recommendation.)
