Mufasa

i analysed 1,000 TIKTOK slideshows for consumer apps... here's what i found something that change how you run your app/saas campaign since. most people assume the slideshow with the most views brings in the most installs. i tracked every metric i could pull across 1000+ posts. views, saves, comment sentiment, slide count, where the app got mentioned in the sequence, caption length, niche. the data told a different story. the highest converting slideshows rarely broke 100k views. some sat under 20k. meanwhile some of the viral ones with 2m+ views converted under 0.05%. viral and profitable are two different games. here's the pattern that separated the winners. the format was almost always: content, content, content, content, ad warmup, app push. 4 to 6 slides that feel like normal lifestyle or niche content, no mention of the app at all. then one slide near the end where the product shows up, framed as part of the story instead of an ad. apps that opened with the product on slide 1 underperformed almost every time. the accounts winning were disguising the app inside content people were already scrolling for anyway. travel aesthetics, interior inspiration, "things nobody tells you about x" hooks, niche opinions. the second pattern was volume, not virality. accounts running 8-10 tiktoks, posting 2x a day, same slide formats with fresh variations each time. 90% of posts stayed under 5k views, most even under 300. a handful hit 50k-500k. a rare one crossed 1m. the accounts winning weren't making one perfect post, they were running the format enough times that the algorithm found the winners for them. the workflow behind it, if you want to copy it: research first. search your niche on tiktok, screenshot every top slideshow, save the captions somewhere. this is the raw material for everything after. pull matching visuals from pinterest for each slide type in that screenshot pile. figure out what slide 1 looks like, slide 2, slide 3. download a handful per slide type. not everything from pinterest can be reposted as is. run it through an image api like openai or gemini to generate variations that keep the same vibe. 5 slide types x 100 variations gets you 500 usable images fast. feed the competitor captions into claude code and have it write new caption variations in that same tone, keep the early slides content only, drop the app in near the end. claude code can overlay the captions onto the images directly using ffmpeg, then hand scheduling off to a tool that allows accounts to post automatically without you touching them daily. set this up once and you get months of content queued across every account, running the same proven format with fresh visuals each time. the accounts losing were treating every post as a one off. the accounts winning built a system and let volume do the work.

this fitness app is doing $600k/month with duolingo-like onboarding, here's why. onboarding: - an elephant mascot greets you first → duolingo tone in a category full of clinical spreadsheet apps. - it shows you the ranking system before any tracking feature → you learn what you're playing for before you learn how to play. - hold the screen to commit → a physical action, tiny sunk cost, you've already done something. - build your own avatar → it's your app now, not theirs - 40+ questions on goals, experience, training history → every answer makes the plan feel more custom. - you earn your first rank from the answers alone → a win before you've touched a barbell - review prompt fires right at that moment → peak motivation, highest possible rating. - paywall loops with no exit, only "try free for 7 days". that review placement is the actual business. it's how you get 30k+ reviews sitting at 4.7. review volume is an app store ranking input, and liftoff is top 3 on 700+ keywords. the onboarding isn't just converting downloads but it's manufacturing the ratings that produce the next batch. these guys stopped chasing trends. found 3 or 4 formats that converted, pov training clips, day in the life, transformation arcs, and ran them into the ground with fresh execution each time. i see app founders do this exact system adapted to the niche, somewhere in there we separated creation from scale. they built the original templates, a small team of editors multiplied them across platforms. this is where most solo founders get stuck. they either do everything themselves and burn out around 15 posts a month, or hire generically and lose whatever made the content work.

#8 in 24 hours 😭💀

how to pull 10M+ views on tiktok slideshows... i've done tens of millions of views with this exact setup and it's worked in every single niche i've deployed it in, so this isn't theory, it's the actual playbook i run. if you want to scale slideshows to absurd numbers, or you're just starting and want to win as fast as humanly possible, this is the whole thing written out. start with one question: who wins, the person posting 30 times a month or the person posting 1,000 times a month? same core content, same hooks, doesn't matter. person 2 has a 33x higher chance of catching a massive viral outlier because virality on tiktok is a lottery and they're buying 33x more tickets. that's the entire mental model. you're not trying to make one perfect post. you're trying to run enough shots that the algorithm is statistically forced to hand you a winner. everything below is just how you get to thousands of posts a month without doing thousands of hours of manual work. phase 1: research and asset scraping stop inventing formats from scratch. it's the single biggest time-sink for people starting out and it's completely unnecessary. the algorithm is already telling you what works, you just have to go read it. before you make a single asset, go find the top-performing slideshows in your exact niche. not adjacent niches, the exact one. i personally hunt down and save the 40 best viral concepts i can find, filtering for posts that already have millions of views, because a post going viral is the market pre-validating the format for you. save the links. then i run those specific posts through Apify to scrape them, pull the on-slide text out, and dump it into plain text. that raw text goes straight into Claude with a simple prompt to clean it up and map out the slide structure, hook slide, build slides, payoff slide. within an hour you've got 40 pre-validated content skeletons with every slide written out, built on hooks that already proved they convert. you didn't guess at anything. you reverse-engineered 40 winners. at the same time i use apify again to scrape a few hundred high-quality background photos off Pinterest that fit the niche. these become your visual layer. you want variety here because it feeds the next phase. phase 2: the uniqueizer here's the problem that kills most people at scale. you cannot post the same 40 ideas over and over, tiktok will instantly flag it as duplicate content and shadowban the account into oblivion. but you also can't sit there manually writing thousands of genuinely new posts, that's tens of hours of labor you don't have. the fix is to generate dozens of unique variations from a single core idea, automatically, for zero cost. i spent about an hour chatting with Claude and built a small custom tool for this. you feed it your scraped Pinterest backgrounds and your cleaned-up text ideas, and it handles all the canvas logic itself, layering text over image, and spits out 20+ distinct variations from one idea. the randomization is what makes each output read as unique to tiktok: it rotates the background photo, shifts text position, swaps fonts, changes the color palette, reorders where possible, tweaks spacing. every one comes out looking like a separate post even though the underlying idea is the same. feed it 40 ideas, get 1,200+ completely distinct slideshow outputs in a few minutes. and because it runs purely on local html/javascript to stitch text and photos together, your operational cost is exactly $0. no image-generation apis, no per-render fees, nothing. it's just your machine doing canvas math. (if people want it, i'll write up exactly how i built this tool in a follow-up post.) phase 3: the tiktok factory now you've got thousands of unique posts sitting in a folder and you need them online. this is the part everyone underestimates. if you do it manually, you're buying physical devices, setting up dedicated clean ips per account, and warming each account for 3-4 days before you post anything, scrolling, liking, watching, behaving like an actual human so the platform trusts the account isn't a bot. skip the warmup and you burn accounts faster than you can make them. if you don't want a wall of physical phones on your desk, you outsource that to a tiktok factory like Minionix. get 10 phones on there and you can push around 1,500 posts a month completely hands-off. that's the leverage, you're managing a pipeline, not a phone. you can absolutely do it yourself by buying old iphones or paying students to post for you, but once you price out the devices, the sim/ip setup, and the babysitting, the hands-off route usually wins on cost per post. run the numbers for your own volume before you commit either way. phase 4: analyze and double down at thousands of posts a month, this stops being a game of luck and turns into a pure numbers game you can actually read. once the pipeline is live and traffic is flowing, monitor the accounts weekly. don't watch individual posts, watch patterns. track which hooks, which visual palettes, which slide lengths, which text placements are pulling the highest views and the highest app conversions. the data will be blunt about it, a handful of formats will carry almost everything. take those winners and feed them straight back into the uniqueizer as your new baseline variables. now every batch you generate is weighted toward what the algorithm already rewarded last week. you tighten the loop every cycle, and each round of 1,200 posts converts better than the last because you keep flooding the feed with more of exactly what's working and quietly killing everything that isn't.

agencymaxxing.

the DIFFERENCE is crazy. 80.3% on swe-bench pro vs 58.6% for gpt 5.5 and 54.2% for gemini 3.1 pro.

the AMOUNT of teens making money with roblox is actually crazy. they are out here building roblox games with ai. launching content rewards campaigns at $0.55 cpm. hit 7.5 million views for $4,000 and cleared 1m in devex payouts. while you're out here paying meta $11 CPM for the same attention.