I built an API-first Sora watermark removal tool

Hacker News

Hi everyone its aadarsh, When Sora launched, I started generating videos for a side project. Problem: every video had OpenAI's watermark, and I needed clean files for editing. Live at: https://sorastrip.app API docs: https://sorastrip.app/api-docs Existing solutions: - Upload tools: slow (500MB+ transfers per video) - Generic removers: degraded quality - Enterprise APIs: "contact sales" pricing - Most had zero API access So I built SoraStrip with these constraints: 1. Link-based (paste Sora URL, get clean MP4 CDN link) 2. Quality preservation (maintain 4K, original bitrate) 3. Developer-friendly API (RESTful, simple auth) 4. Transparent pricing ($9 = 100 videos/month, $50 = unlimited) Technical bits: - Streaming pipeline (no local storage) - Webhook callbacks for batch jobs - 99.9% uptime SLA on paid tiers - <5 second P95 latency Only ~15% of watermark tools offer APIs, and most charge enterprise rates. I wanted indie devs and agencies to afford automation. Try free (1/day): sorastrip.app Questions about architecture or pricing? AMA! Twitter [x] - https://x.com/aadarsh_nagrath

Visit product View on Hacker NewsOct 25, 2025

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

88%88% 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: openai, apis, open · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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