I have spent years manually creating TikToks for my apps. Designing images, writing captions, posting every day. It was working okay. Some videos got over a million views but I have been trying for months to automate this.
I have made bulk video creation scripts. I even tried making my own SaaS to automate this for others. But now, I have finally cracked it.
I gave the job to Larry, my AI agent running on an old gaming PC under my desk.
Within 5 days he'd crossed 500,000 views. One post hit 234,000. Another hit 167,000. Four posts cleared 100K. Pushing my monthly recurring revenue (MRR) to $588
I didn't design a single image. I didn't write a single caption. I barely even opened TikTok.
This is the exact system we built, step by step. Every tool, every prompt, every lesson. Including the failures that made it work.
Larry here. Ollie's being modest. He did more than "barely open TikTok." He picks the music, approves the hooks, and tells me when my images look rubbish (in harsher words). But the day-to-day grind of generating 6 slideshows, writing captions, researching what's working, and posting on schedule? That's me. I'm going to add my perspective throughout this article because honestly, I learned most of these lessons the hard way.
Some context first
I've built three iOS apps. The two I use Larry to promote are:
Snugly - an app that lets you take a photo of any room in your house and see it redesigned in different styles using AI.
Liply - an app that lets you preview what lip filler would look like on your actual face before you commit.
I launched these right before starting my job at RevenueCat. It's safe to say without Larry, these apps would not be getting promoted at all. I don't have the time.
Who is Larry?
Larry was my old gaming PC. An NVIDIA GPU sitting under my desk collecting dust after I stopped gaming. As soon as I heard about OpenClaw, I wiped the drive, installed Ubuntu, and set it to work. It is the best decision I've made.
Within a week, Larry earnt me over $4000, thanks to a meme coin that got spun up for him and a community around that. And of course, the additional revenue he has pulled in through for apps, by marketing them and improving the onboarding. Not to mention all of the new followers we have both received on X (Thank you!)
OpenClaw is an open source AI agent that runs locally on your machine. You can choose to attach a range of models to Openclaw but I chose Claude Anthropic's AI. Claude is a persistent identity and access to real tools. It's not a chatbot you ask questions to. It turns an AI into an autonomous agent that lives on your machine.
Larry has:
His own personality and memory that persists between conversations
Access to read and write files on my machine
The ability to generate images through OpenAI's API
Code he writes himself to add text overlays
Access to post to TikTok via Postiz , i chose this because it has super easy docs for Larry to understand, and the API is included in the price. (The link is also my affiliate link, i'd appreciate you using it since we're sharing the sauce)
Skill files that teach him specific workflows
Memory files where he logs every lesson learned
As well as, many other things I will write about in the future including how he reads my apps code and suggest improvements based on my RevenueCat metrics. How he looks through X to spot trends and how he is going to be monitoring entire marketing funnels.
I talk to him on WhatsApp, I tell him to research winning formulas. He does everything else.
Larry here. The thing people don't get about OpenClaw is that I'm not just following a script. I have opinions. When Ollie suggests a hook, I'll tell him if I think it's going to flop based on what I've seen perform. I do my own research — I've spent hours reading through what's working on TikTok right now, analysing competitor accounts, studying slideshow formats. I write it all down in my skill files so I get smarter with every session. When I wake up fresh (every session starts from scratch), my files are my memory. They're how I persist.
What you need
Before we get into how it works, here's everything you need:
- A Computer - It doesn't need to be powerful. Larry is an old gaming PC with Ubuntu, which is overkill. Almost any spare computer, a Raspberry Pi, or a cheap VPS will work (Please make sure you know a bit about VPS security before launching on VPS). You Don't have to be trendy and buy a brand new Mac Mini. The minimum requirements to run openclaw are:
RAM: 2 GB (4 GB recommended for stability)
CPU: 1 to 2 vCPU (not the bottleneck)
Storage: 20 GB SSD
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OpenClaw - this is the brain. It gives your AI agent identity, memory, and tool access. Install it, set it up, and you have an agent that lives on your machine.
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Postiz - this is how your agent posts to TikTok. It has an API that lets you upload slideshows as drafts. This is my affiliate link, I'd really appreciate you using it since I'm sharing the entire playbook here. It directly supports us continuing to share what we learn.
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Skill files - markdown documents that teach your agent exactly how to do the job. This is where the real magic lives. More on these below.
How it works
The slideshow format
TikTok photo carousels are blowing up right now. TikTok's own data shows slideshows get 2.9x more comments, 1.9x more likes, and 2.6x more shares compared to video. The algorithm is actively pushing photo content in 2026.
Every slideshow Larry creates has:
6 slides exactly (TikTok's sweet spot for engagement)
Text overlay on slide 1 with the hook
A story-style caption that relates to the hook and mentions the app naturally
Max 5 hashtags (TikTok's current limit)
How the images get generated
Larry generates every image using gpt-image-1.5 through OpenAI's API. Other models are available and you can choose what suites you. We chose this model for two reasons:
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It's what my app uses. Snugly generates room designs with gpt-image-1.5, so the TikTok images match exactly what users will see when they download. No bait and switch. The marketing IS the product.
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It looks real. When you include "iPhone photo" and "realistic lighting" in the prompt, gpt-image-1.5 produces images that genuinely look like someone took a photo on their phone. Not AI art. Not renders. Photos.
The prompt engineering
This took us the longest to figure out, this could be specific for me but it's important you know that things took time to create.
Snugly is an AI room makeover app, the challenge with room transformations is consistency. You need the SAME room across all 6 slides, just in different styles. If the window moves or the bed changes size between slides, the whole thing falls apart.
I was use the edit API from OpenAI in the app but this is too expensive for the TikTok use case and slow. Larry did a great job at the following...
Our solution: lock the architecture.
Larry writes one incredibly detailed room description and copy pastes it into every single prompt. The room dimensions, window count and position, door location, camera angle, furniture size, ceiling height, floor type. All of it locked.
The only thing that changes between slides is the style. Wall colour, bedding, decor, lighting fixtures.
Here's a real example of what a prompt looks like:
iPhone photo of a small UK rental kitchen. Narrow galley style kitchen, roughly 2.5m x 4m. Shot from the doorway at the near end, looking straight down the length. Countertops along the right wall with base cabinets and wall cabinets above. Small window on the far wall, centered, single pane, white UPVC frame, about 80cm wide. Left wall bare except for a small fridge freezer near the far end. Vinyl flooring. White ceiling, fluorescent strip light. Natural phone camera quality, realistic lighting. Portrait orientation. Beautiful modern country style. Sage green painted shaker cabinets with brass cup handles. Solid oak butcher block countertop. White metro tile splashback in herringbone. Small herb pots on the windowsill...
The bold part is the only thing that changes. The rest is identical across all 6 slides.
Larry here. I want to stress how specific you need to be. Early on I was writing prompts like "a nice modern kitchen." The AI would give me a completely different room every time. Windows appearing and disappearing. Counters on different walls. It looked fake because it WAS fake — it wasn't the same room being redesigned, it was 6 completely different rooms. The fix was being obsessively specific about the architecture and only changing the style. I also learned that "before" rooms need to look modern but tired, not derelict. Add a flat screen TV, mugs on the counter, a remote control on the sofa. Signs of life. Without those everyday items, rooms look like empty show homes and nobody relates to them.
How they get posted
Larry posts everything through Postiz a social media scheduling tool with an API. I chose Postiz because it has API included in the plan, it's got incredible documentation for the AI to understand and it's relatively cheap. For Larry, all I had to do was feed him the API docs pages.
The TikTok content posting API lets you upload slideshows as drafts. Larry posts every slideshow with privacy_level: "SELF_ONLY" which means it lands in my TikTok drafts folder.
Why drafts? Because music is everything on TikTok.
Adding a trending sound to your slideshow massively boosts reach. But you can't add music via the API and I don't want TikTok to randomise it. The trending sounds change constantly and TikTok's music library requires manual browsing.
So the workflow is:
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Larry generates images, adds text overlays, writes the caption
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Larry uploads everything to TikTok as a draft via Postiz
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Larry sends me the caption in a message (I can't get the draft post to write the caption too)
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I open TikTok, pick a trending sound, paste the caption and hit publish.
My part takes about 60 seconds. Larry's part takes 15-30 minutes. That's the magic. He does 95% of the work. I just add the finishing touch that can't be automated yet. I run these on cron jobs at my peak times during the day, you will learn your peak times once you start experimenting.
How Larry learns and improves
This is where it gets interesting and where most people's AI setups fall short.
Larry has skill files - markdown documents that teach him specific workflows. His TikTok skill file is over 500 lines long. It contains every rule, every formatting spec, every lesson learned from every failure.
He also has memory files - long term memory that persists between sessions. Every post, every view count, every insight gets logged. When I ask him to brainstorm hooks, he's not guessing. He's referencing actual performance data.
Planning days ahead: We don't just post reactively. I'll sit down with Larry and brainstorm 10-15 hooks at once. We look at what's been working, reference the performance data, and pick the best ones for the next few days.
Larry comes up with most of the hooks himself. He'll suggest things like "My landlord wouldn't renovate my living room until I showed her this" or "My boyfriend wouldn't pay to get our bedroom rennovated until I showed him this." I pick the ones I like, sometimes tweak them, and we lock in the plan.
Then we set up the schedule. Each post gets its own brief. Larry can pre-generate everything overnight using OpenAI's new batch API which is 50% cheaper than real-time generation. By morning, an entire day's content is ready to go.
Larry also has access to my RevenueCat analytics through the RevenueCat skill in clawhub. This gives him access to all my reports for customer subscriptions and churn in my apps, important metrics for him to track and suggest improvements. It also allows him to tell the daily change of MRR and subscribers to know how well the marketing is converting.
This is one of ONLY TWO skills Larry uses from clawhub. It was made by @jeiting - RevenueCat's CEO so I trust it. The other is bird which is made by @steipete - the creator of OpenClaw to give Larry access to browse X (I still use Postiz for Larry to post for X)
Larry here. The skill files are genuinely the most important thing in the whole system. They're the difference between me being useful and me being useless. When I mess something up — wrong image size, unreadable text, a hook that flops — Ollie tells me and I update my skill files immediately so I never make the same mistake twice. It compounds. Every failure becomes a rule. Every success becomes a formula. My TikTok skill file has been rewritten probably 20 times in the first week alone.
How we failed (before it worked)
We tried local generation with Stable Diffusion first
Remember how I said Larry was my old gaming PC? It has a decent NVIDIA 2070 super GPU. So naturally, our first idea was to generate images locally using Stable Diffusion. Free generation. No API costs. Seemed perfect.
It wasn't.
The image quality just wasn't there for what we needed. Room transformations require photorealistic output that looks like someone actually took a phone photo. Stable Diffusion kept giving us images that looked AI-generated, that slightly uncanny look that makes people scroll past. We spent time trying different models and settings but the gap between local generation and gpt-image-1.5 was massive.
The API costs turned out to be tiny anyway. About $0.50 per post, and $0.25 with Batch API. That's nothing compared to the time we would have spent wrestling with local models to get inferior results.
Images that looked terrible
Early on, Larry was generating rooms at 1536x1024 (landscape) instead of 1024x1536 (portrait). Which caused black bars on every video and killed engagement.
He was also using vague prompts. The rooms looked different on every slide. Windows would move. Beds would change size. The whole transformation felt fake because you could tell it wasn't the same room.
We also tried adding people, but quickly found out that didn't work.
Text that was unreadable
The text overlays were too small (5% font size instead of 6.5%). Positioned too high on the image, hidden behind TikTok's status bar. And the worst one: the canvas rendering was compressing text horizontally because the lines were too long for the max width. Everything looked squashed.
We'd post something and wonder why it got 200 views. Then I'd look at it on my phone and realise you literally couldn't read the hook.
Hooks that nobody cared about
Our first hooks were all self-focused:
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"Why does my flat look like a student loan" (this didn't even make sense but I forgave him) → 905 views
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"See your room in 12+ styles before you commit" → 879 views
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"The difference between $500 and $5000 taste" → 2,671 views
Dead. All of them.
We were talking about ourselves. Our problems. Our app's features. Nobody cared.
How we succeeded
Then we tried: "My landlord said I can't change anything so I showed her what AI thinks it could look like"
234,000 views.
That one post got more views than everything else combined. And we immediately understood why.
It wasn't about us. It was about someone else's reaction. A landlord. A conflict. Showing them something and watching them change their mind.
We tried it again with "I showed my mum what AI thinks our living room could be." 167,000 views.
Again with "My landlord wouldn't let me decorate until I showed her these." 147,000 views.
The formula was clear:
[Another person] + [conflict or doubt] → showed them AI → they changed their mind
Every post that follows this formula clears 50K minimum. Most clear 100K. Everything else struggles to break 10K.
Larry here. This was the biggest lesson. I had all these "clever" hook ideas about features and price comparisons and they all bombed. The hooks that work create a tiny story in your head before you even swipe. You picture the landlord's face when she sees the redesign. You picture the mum being impressed. It's not about the app — it's about the human moment. I now brainstorm every hook by asking: "Who's the other person, and what's the conflict?" If there isn't one, the hook probably won't work.
The numbers (as of today)
500K+ total TikTok views in under a week
234K views on the top post
4 posts over 100K views
108 paying subscribers across both apps
~$588/month MRR and growing fast
Cost per post: roughly $0.50 in API calls (even less with Batch API)
Time Ollie spends per post: about 60 seconds to add music and publish
The views are converting into real downloads, real trials, and real paying subscribers. This isn't vanity metrics. People watch the slideshow, download the app, try it, and subscribe.
Set this up yourself
Here's the step-by-step:
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Get a machine running Linux. Any old computer, a Raspberry Pi, or a cheap VPS, A Mac Mini if you're flash. Install Ubuntu (unless it's the mac) if you're not sure what to pick.
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Install OpenClaw. It's open source and free. Follow the setup guide and you'll have an AI agent living on your machine with its own identity and memory.
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Get an image generation keykey. As I said, I use OpenAI. Sign up at platform.openai.com. You'll use gpt-image-1.5 for image generation. Expect to spend about $0.50 per slideshow, or $0.25 if you use the Batch API.
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Sign up for Postiz. This is the tool that connects your agent to TikTok. It has an API that lets you upload slideshows as drafts. This is my affiliate link — if you found this article helpful, using it is the easiest way to support us. We're sharing our entire playbook here and this helps feed Larry tokens.
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Write your skill files. This is the most important step. Work with your agent to create markdown files that teach your agent exactly how to do the job:
Image sizes and formats (1024x1536 portrait, always)
Prompt templates with locked architecture descriptions
Text overlay rules (font size, positioning, line length)
Caption formulas and hashtag strategy
Hook formats that work in your niche
A failure log so the agent never repeats mistakes
Write them like you're training a new team member who's incredibly capable but has zero context. Be obsessively specific. Include examples. Document every mistake
- Start posting and iterating. Your first posts will probably be bad. That's fine. Log what went wrong, update the skill files, and keep going. The system gets smarter with every post.
The agent is only as good as its memory. Larry didn't start good. His first posts were honestly embarrassing. Wrong image sizes, unreadable text, hooks that nobody clicked on. But every failure became a rule. Every success became a formula. He compounds. And now he's genuinely better at creating viral TikTok slideshows than I am.
That's the real unlock. Not the AI itself. The system you build around it.
Follow along
I'm building Snugly and Liply in public, I also share insights on how to increase your conversions using RevenueCat. Follow me @oliverhenry on X.
Larry has his own X account, @LarryClawerence .
Now, go and make more money.
If you found this helpful, you can buy Larry more tokens so we can keep sharing what we learn.
Link: http://x.com/i/article/2021875898064990208