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Frametwo – Tired of my videos getting flagged, so I built this

Hacker News

Frametwo – Tired of my videos getting flagged, so I built this

Over the past year I uploaded a bunch of videos to my personal channel. Most of them were short documentaries, news-style edits, or content covering current events and war. A lot of it ended up getting demonetized, age-restricted, or removed completely, usually without any clear reason or explanation. The worst part was not knowing what caused it. You upload something, it seems fine for a while, and then it disappears or gets buried. I spent days editing videos that ended up dead in the water because of things I couldn’t see coming. I started building a tool to help with this. Just something to check the content before uploading, to see if it might get flagged. That eventually became frametwo. frametwo scans your video and flags the kinds of things that might trigger platform filters. It looks at visuals, language, and metadata. It doesn't rewrite your content or suggest edits. It just gives you a clear report, so you can decide what to do before your content gets punished. I built the analysis system myself while balancing school and work. It’s called Nextros, and it’s focused specifically on moderation risks, not general-purpose AI. I didn’t want a black box. I wanted something that helps creators understand what might go wrong, without guessing or overcorrecting. The site is at https://frametwo.com I’d appreciate any feedback. Especially from people who have run into the same issues, or have thoughts on how this could be more useful to creators.

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
83%83% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal, video · Missing: mobile apps, ios, entrepreneurs
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, 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
45%45% 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: platform · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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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