Prove that video of a UFO was real, by using this

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Seriously though, you see something unbelievable. Grab your phone and record it. Post it online and immediately people start asking "is that real?" And can you blame them? How can anyone prove what they post is real. I’ve been working on a tool to address one of the biggest problems I see with digital content: the inability to prove that it was captured by a real human, on a real device, at a specific time — without manipulation or AI involvement. Witness by Reel Human is a privacy-first camera app that generates cryptographically signed photos and videos. Each file includes an embedded JSON manifest with: - The exact capture time - Device info (not user identity) - The app version and signature metadata The manifest is stored inside the media file (MP4/JPEG) and travels with it, even if shared. The result is a verifiable, human-authored piece of content. What’s working now (POC): - Android and iOS apps (available for testing) - Signed JSON manifests inside every photo/video - No accounts, no tracking, no upload What’s coming: - Public verification portal (in progress) - Registry backend with optional verification logging - Open API for platforms to verify content at scale I’d love your feedback on: - The idea and approach - Security model (crypto choices, manifest design, etc.) - Use cases beyond journalism (legal, education, social media) Site: https://reel-human.com POC App: - Android: https://play.google.com/store/apps/details?id=com.reelhuman.... - iOS: https://testflight.apple.com/join/GzfTsCNF Ask me anything — I’m solo-building this because I think trusted content should be a human right.

Visit product View on Hacker NewsSep 17, 2025

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

89%89% 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: model, apple, google · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

25%25% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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