I

I built a 3-tap YouTube summarizer for iOS

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I built a 3-tap YouTube summarizer for iOS

How it works: Copy a YouTube link → open app → it auto-detects from clipboard, pulls metadata + transcript, and generates a structured summary (key points + narrative) in seconds. No inputs, no pasting, no prompting. Supports 60+ languages for summaries, the video's language doesn't matter. Why I built: I had an endless queue of educational videos but no time to sit through rambling or clickbait. ChatGPT flow = copy → open → paste → prompt → pray it formats correctly. Existing summarizers felt feature-bloated or unreliable. I wanted something super minimalist: “clipboard → value” on repeat. Decisions: Stack — React Native 0.81 (New Arch) + TypeScript Clipboard trick — Monitors clipboard on app start/resume — no manual paste Caching — Stores transcripts + summaries in AsyncStorage with metadata — fast image library prevents repetitive refetching APIs — YouTube metadata — Third-party transcript provider (exploring Whisper) — OpenAI for summarization Output — Structured prompts → consistent format across 60+ languages — Easter egg: philosophical analysis for songs/poems Time-saved calculation — Uses actual video length (30% of cases) — Fallback = estimated from average speech speed Main headache: Grandfathering legacy users without breaking App Review was harder than I expected. The first version was paid upfront ($14.99 or leave). Got some sales in the first two weeks, then traffic died. After some internal fighting, I switched to free-to-try with IAP. By then I already had thousands of users I’d promised free access forever, plus a dozen who actually paid. I wanted to keep my word and never show them a paywall. RevenueCat’s originalAppVersion looked perfect… until I actually used it.. Turns out it’s not the marketing version (1.0, 2.3), but the build number (the variable name originalAppVersion is so misleading…). And Apple Review/TestFlight always report it as 1.0, so reviewers looked like legacy users and never saw the paywall (5 rejections). Everything worked for me, totally broken for them. My mistake: I auto-entitled anyone with 1.0. First hacky fix: show a review-only paywall. Proper fix: if originalAppVersion === "1.0" (I never had such a build number anyways), treat as non-legacy. Days of digging and testing across devices/envs… finally approved. Current state: 3800+ users and 12 sales within 2 weeks post-launch, 4.9-star rating globally. Now multi-tier subscription after 3 free summaries due to ongoing costs and long-term value. Limitations/Challenges: — iOS only for now. Android clipboard monitoring is restricted—would need a different UX. — Some countries need VPN due to API restrictions; I notify users on app start. — Dependency on third-party transcript API (evaluating OpenAI-based solution). — YouTube's testing AI conversational tools, but it's very truncated, sandboxed only for some users and unlikely to roll out widely as it cuts ad their revenue from watching. Happy to answer questions about implementation or share code snippets if helpful.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios, vpn · Missing: reddit linkedin, podcasting, created
98%98% 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: apple, user, new · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, video, users · Missing: mobile apps, personal, entrepreneurs
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, revenue, subscription · Missing: mrr, profit, saas
29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released
29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, users · Missing: platform, intuitive, reviews
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, paid · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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