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Feedback on my ebook landing page

Hacker News

Feedback on my ebook landing page

I've just launched a landing page for an ebook that I hope to have out sometime in June but I feel like that dog in the meme "I have no idea what I'm doing."[1] I am not a designer (or copywriter) but I've been hanging out on HN long enough to have a good idea where to go for help. I used Bootstrap[2] for a template, Colour Lovers[3] for a palette and Weebly's homepage[4] for inspiration. Weebly's homepage is fantastic but as much as I tried to keep it simple and powerful, like Weebly, I kept getting bogged down in massive amounts copy. I think this is probably where I need to most help. After visiting the site, do you know what the ebook is about? Are the problems its trying to address apparent? Of course any feedback on copy, design, etc. would be greatly appreciated. URL: http://startupprivacy.com [1]http://ihavenoideawhatiamdoing.com/wp-content/uploads/2011/08/dog_science.jpg [2]http://twitter.github.com/bootstrap [3]http://www.colourlovers.com/palette/292482/Terra [4]http://weebly.com

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6points
20comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% 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 · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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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