Ma

MagnetManager – Turn YouTube videos into lead magnets that convert

Hacker News

MagnetManager – Turn YouTube videos into lead magnets that convert

Hey HN, I’ve been making Ruby on Rails tutorials on YouTube for a while, and realized I was getting views, but not building any kind of audience. No real way to connect with viewers. I also run a small SaaS that funds my bootstrapping efforts. After trying (and getting frustrated with) a bunch of lead magnet tools, I built my own. It's called MagnetManager. You drop in a topic or a YouTube link, pick a template (listicle, guide, summary, etc), and it creates a clean lead magnet you can share and collect emails from. PDFs are hosted for you. You get a link. That’s it. Built with: Ruby on Rails PostgreSQL Stimulus + Hotwire It’s made for solo creators, educators, and small content-driven businesses. You can try it for free at https://magnetmanager.com (14-day trial, no credit card required). Would love feedback — UX, feature gaps, use cases you think I missed. Also curious if anyone else here has dealt with this “content with no capture” problem. Thanks for reading. Happy to answer anything. Phil

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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 HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos
49%49% 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
41%41% 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: host · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
17%17% 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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