Daniel Heintzma - I speedran a new app from 0 to $2m/year in 6 months (here's how)

Before the app existed, a co-founder started an Instagram account posting 'how I cook on Hinge' videos: actionable, sometimes humorous prompts people could put on their dating profile. Once some videos did well (after a couple of weeks), they added a link in bio to join the app's preorder. The prompt content later became a feature (prompt feedback) in the app.

In the first couple of weeks of Instagram posting, the video captions asked followers to DM for a dating-profile review. Followers sent profile screenshots and paid about $15; the founders gave feedback manually. This was the proof of willingness to pay before building the AI version.

Daniel, posting on TikTok for the first time, studied what other TikTok slideshow apps did well. He took the most viral videos and slideshows from a TikTok account called something like 'Men's Improvement' and repurposed their text into his own Figma-designed slideshows for Sway's audience.

Designed the slide backgrounds in Figma, then used Canva with an Excel sheet that auto-populated the text onto every slide (bulk create). Each slideshow took seconds; a month's worth of content was batched in one to two hours.

New TikTok account started with aesthetic, fully unbranded carousels (photo backgrounds), testing no CTA vs a CTA on the last slide, to warm up the account. Once a few videos popped and views felt guaranteed, switched to full branding (orange theme) with the app plugged on every slide, e.g. 'five mistakes when flirting over text, dating advice from Sway AI app'.

Worked with a few influencers; the main one, 'Defund Simping', promoted the app hard in his organic videos. Those videos were then repurposed as ads that ran on his page to his familiar audience. Controversial videos that drew lots of comments performed best as ads. Scaling hit a creative shortage because only one influencer produced videos, and dating influencers are scarce since the app competes with their own coaching offers.

Onboarding is one linear path to the paywall: clarify why the user is there, show explainer videos of the problem and how the app solves it (added after users emailed 'what does this app even do?'), label the problem, show other members' dream results, then easy multiple-choice personalization questions. The paywall shows 'your action plan' and an 8-slide carousel of everything included in the subscription.

Tested letting users upload profile screenshots (requiring them to leave to their dating app) before the paywall vs keeping onboarding simple and doing the upload and rating after the paywall. Upload-first risked users leaving and never returning.

After launch they saw users running the profile analysis flow more often than expected, so they added a second paywall selling additional analyses to power users on top of the subscription.

Users pick a look, upload photos of their face, buy a photo pack ($14.99 single or $49.99 for 10), and receive AI-optimized dating photos by email. No cap on how much one user can spend, capturing App Store 'whales' (insight from a co-founder who had built a game).

Copied apps with good LTV that default to an annual plan: the paywall preselects annual so more revenue arrives upfront to fund ad growth. Also tried a weekly plan, which gave much slower growth. Deliberately priced high (monetization over growth), unlike Cal AI's intentionally low prices.

Focus on getting users through Cal AI's 3-day trial without cancelling: fix bugs and redesign the screens people touch all the time when logging a meal (food database, camera, food detail page).

Shipped a groups feature (community feed) where friends log food and stay accountable together, with leaderboards showing longest streaks; later added public topic groups (weight loss, bulking, recipes and meal prep), inspired by Strava and Ladder. Aim: retention plus a network effect that the many Cal AI clones can't copy.

Added streaks so users come back daily to log meals, modeled on highly retentive apps like Duolingo.

Product-led growth features: progress photos users can post to Instagram stories showing weight lost/gained; shareable meal cards showing calories tracked with Cal AI; milestone badges for consecutive days logged (e.g. a full year) to post on stories.

Because Cal AI grows mainly through influencer videos, and every video shows the same three screens (camera, loading screen, food detail page), design work focuses on making those three screens as easy to understand on camera as possible.

Cal AI's main growth is influencer marketing with fitness creators: there are very many, they are reasonably priced, and a calorie tracker doesn't conflict with the coaching they sell. Contrast with Sway, where dating coaches sell profile feedback themselves, and beauty creators, who are expensive because of makeup brand deals.

Cal AI hit a major inflection when it seriously ramped paid ads on top of influencer marketing; same pattern at Sway. No detail on platforms or spend.

Robinhood Crypto went from idea to launch in about a month, with the whole company rallying behind it. The team explored many branding variations to look unlike any existing crypto app (Coinbase looked like a bank; this looked almost like a video game).