I Outsourced My Daily 4-Hour X Feed Scrolling for Topic Discovery to AI – Hit Rate Jumped From 15% to 60%+. Full Prompt + Workflow Open-Sourced!

This post is pure meat. Three things: what's the real bottleneck that locks AI creators down? How do you get an AI Agent scrolling X, Xiaohongshu, and Reddit for topic ideas 24/7? And the full prompt + 5-platform threshold table I've been running for two weeks – all open-source. Just copy and go. At the end there's a cold bucket of reality and two weeks of raw data. If you're drowning in feeds, this one's for you.

Okay, this might sound like a flex, but let me level with you first.

I've been deep in the Chinese AI Twitter scene for half a year, and recently I finally realized one thing: the biggest bottleneck for an AI creator isn't not being able to write – it's not knowing what to write.

I used to spend 4 hours a day scrolling X + Xiaohongshu + Reddit hunting for topics. Eyes fried. And what did I end up writing? The same thing everyone else had already written three days earlier.

Then I handed this whole job to a cloud phone. Now I spend zero hours scrolling feeds. By 8 AM every morning there's a topic sheet waiting on my desktop. Hit rate went from 15% to 60%+.

The logic, the prompt, the 5-platform expansion – all open-source right here.

Without further ado, let me break down where I was stuck.

1. An AI Creator's Day Starts With Scrolling

If you're an AI creator, your day probably goes like this:

Open X. See what Sam Altman just posted, what Karpathy retweeted, which new Skill is trending. Switch to Xiaohongshu. Read AI reviews, prompt shares, who dropped a new workflow. Switch to Reddit. Check r/LocalLLaMA, r/ClaudeAI's latest hot discussions. Then switch to Bilibili. See which creator put out a new tutorial.

One lap – 3-4 hours gone.

And the worst part? Those "hot topics" you caught? Someone already wrote and published them.

Strip it down – what you're doing is basically manual labor. Using human eyes to stare at numbers, keywords, and engagement.

No judgment required. No taste required. No need for you to even be present.

I've always believed that "topic discovery" is fundamentally a data filtering problem. It's not about "getting inspired" – it's about catching the right signal at the right time.

Could AI do this job?

Honestly, I tried before. RSS. Various aggregation tools. Even built a few crawlers myself. They all died on the same hill: apps like X and Xiaohongshu don't have decent APIs. The data you want – the recommendation feed – only exists inside the app.

2. The Turning Point: Letting a Cloud Phone Scroll for You

Then recently I started using Airtap.

Let me explain 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 scrolls on that phone for you.

The key point: this isn't an API call. It's literally "scrolling a phone."

So X's For You feed, Xiaohongshu's Discover page, Reddit's Hot feed – things with no official API – it can all read them.

You might think, how is that different from me opening my phone and scrolling?

Big difference.

First, it doesn't sleep.

Second, it runs on a phone with a "blank personality." No login, no personal account. The recommendation feed is pure algorithm baseline – not polluted by my interests. It sees what the platform is *actually* pushing.

Third, you write the prompt once, and it runs on a schedule every day.

Think of it this way:

Scrolling X yourself is like eating at a restaurant where you've worn out your welcome with your usual orders – the menu always shows you the same few dishes you like.

If you want to know what the restaurant's real signature dishes are, you need to walk in with a totally fresh face and order from scratch. That's what the cloud phone does.

3. My Exact Setup, in Three Steps

Step 1: Define Your Signal Threshold

I've seen creators use "100w+ views" as their threshold. That works for broad-reach creators, but the AI scene is different.

In AI Twitter, the signal isn't "views" – it's "retweets + replies + author authority."

My threshold for X:

• Retweets ≥ 500
• OR likes ≥ 2000
• Content must hit keywords: Claude / GPT / Cursor / Skill / MCP / Agent / Prompt

Why these numbers?

Because the AI scene on X is an order of magnitude smaller than general entertainment. 500 retweets on AI Twitter equals 100 million views in the mainstream – it's the inflection point of "just validated, not yet saturated."

Below that threshold? Noise. Write about it and nobody reads.

Above 10K retweets? Already beaten to death. You'd just be writing "me too."

The 100-500 range is the sweet spot – already validated that people want to see it, but not yet fully mined by the crowd.

Here's the counterintuitive part about signal thresholds: higher is not better. You want the temperature where it's "just out of the oven but nobody's taken a bite yet."

Step 2: Write a Prompt That Actually Runs

This is my version after two weeks and four or five iterations. Copy and go:

[Prompt content goes here – not provided in original, but you'd include it]

Drop it into Airtap, set it as a daily routine, start at 7 AM, and by 8 AM there's a table on your computer.

It looks like this:

[picture description – not provided]

That's your topic pool for the week.

Step 3: Run Multiple Apps in Parallel – This Is the Best Part

Reuse 90% of the prompt above. Just change the app and the thresholds:

[Threshold table for 5 platforms – not fully detailed in original text]

Launch one cloud phone per app and run them all in parallel. I now have 4 cloud phones running simultaneously. Every morning at 8 AM I get 4 topic sheets.

You'll notice something pretty satisfying: when the same "signal" shows up on 3 platforms simultaneously, it's basically a must-write.

That's the multiplier effect.

Once you automate the most draining part – finding topics – covering 5 platforms versus covering 1 costs almost the same human effort.

Think of it this way: before, you could only drive one truck on one delivery route. Now you've hired 4 drivers who never get tired, running 4 routes simultaneously, and the fuel cost (cloud phone cost) barely changes – but your order volume goes up 4x.

That's the compounding effect of a workflow.

4. Two Weeks of Data – Real Numbers

Here's a rough comparison.

Before (manual scrolling):

• Daily time scrolling feeds for topics: 3-4 hours
• 5 days a week ≈ 20 hours
• A year ≈ 1000 hours
• Topic-to-article hit rate: about 15%
• Only 1-2 out of 10 ideas actually turned into articles.

Now (Airtap running):

• Daily manual scrolling: 0
• Morning table review + secondary filtering: 20 minutes
• A week ≈ 2 hours
• Topic-to-article hit rate: 60%+

20 minutes versus 20 hours.

Over a year, that's not 998 hours saved – it's 998 hours that I used to spend staring at a screen grinding.

I didn't use that time to slack off. I used it for deep writing and hands-on testing.

Because deep writing and testing – that's still the part AI can't do.

5. But I Have to Pour Some Cold Water on This

I can't sell this as a savior. That wouldn't be honest.

What Airtap does is "signal filtering" – not "judgment."

Out of the 20 items in the table, maybe only 3-5 will turn into articles.

Why?

Because AI doesn't know:

• What your audience cares about
• What angle fits your style
• Which topics nobody has dug into yet
• Which topics could piss people off

That judgment is still on you.

And to be frank, Airtap isn't perfect yet.

Occasionally it gets stuck on a popup. Occasionally it misreads a number. Occasionally it skips a post it should have caught.

I probably fine-tune the prompt once a week – adjust thresholds, keywords, and add new edge cases.

I've gone back and forth on this, and I can't gloss it over.

Airtap isn't a savior. It's the first step on an assembly line.

But that one step has already turned me from "the manual laborer scrolling feeds 4 hours a day" into "the content person making 20 minutes of judgment calls daily."

My role changed. Everything else got easier.

6. What I Really Want to Say at the End

Here's the core takeaway in one sentence.

The real bottleneck for AI creators has never been "AI isn't strong enough" – it's "your workflow hasn't placed AI in the right spot."

Put AI in the role of "writing for you" – and you'll find it writes worse than you do.

Put AI in the role of "filtering for you" – and you'll find your own productivity triples.

My prediction for the next year: the gap between solo AI creators and teams will increasingly come from "workflow maturity" – not "who's smarter."

I'm still iterating myself.

Next month I might change this prompt. Next month I might adjust these thresholds again.

But the underlying move – "let AI filter signals for me" – I can never go back.

It's like someone who's driven an electric car – you could put them back on a shared bike, but they won't.

If you're also in the Chinese AI Twitter scene and looking for a fellow traveler, feel free to grab this prompt and give it a spin. When it works, tell me your numbers and I'll help you iterate on the next version. Let's figure it out together.

⚡️ Airtap official site: airtap.ai

🌅 Follow @airtap_ai for more routine demos

📌 If this was useful, hit like / share – let more creators trapped in information feeds see it

(The Airtap mentioned in this article is just the agent tool I personally use and a reference case; it does not constitute any recommendation.)