Turn Instagram and TikTok workouts into executable gym routines

Hacker News

Save workout videos on Instagram or TikTok. Never actually follow them. At the gym, you end up scrolling through saved posts trying to reconstruct sets and reps from a 30-second clip. So I built FitSaver — an iOS app that imports saved workout videos and converts them into structured, editable routines you can actually execute. It doesn’t generate workouts. It doesn’t try to replace trainers. It just turns unstructured social content into something usable. ⸻ Launched Jan 4. Early numbers: 400 downloads $568 revenue 62% installs from App Store search 13% product page conversion 0 crashes so far The surprising part: Paying users skew heavily 35–55. I assumed this would be a Gen-Z product because of TikTok usage, but older users seem much more motivated by structure and consistency. Some user feedback: “Saving workout ideas was a mess. This solved that.” “There’s nothing worse than scrolling social media in a busy gym.” “Other apps have preset workouts — this lets me customize.” The broader problem I’m exploring: Content discovery is easy. Execution is hard. Curious if others building consumer tools have seen: Organization outperform discovery as a monetizable angle Older demographics convert better than younger “Saved but unused content” as a recurring pattern Happy to answer questions about implementation, conversion, or App Store lessons.

Visit product View on Hacker NewsFeb 25, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: revenue, recurring · Missing: arr, mrr, profit

39%39% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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