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I made a book with a hundred UI/UX tips

Hacker News

I made a book with a hundred UI/UX tips

Hello. This year I dived into UI/UX area and at the same time I work as a web developer. I sincerely love making good UI. But at my full time job it's not always possible due to different reasons =\ So I've been posting random UI/UX staff on Twitter, then I made a free book that had 50 tips (posted it here as well). I decided to finish this year by collecting everything I learned and here it is: https://akcium.gumroad.com/l/ui-ux-tips . This time it's a paid one. But, you can check ~20 pages of it, just go to https://akcium.gumroad.com/l/ui-ux-tips-sample , put 0 in the price and download it for free. I tried to illustrate almost every tip and provide some additional links. This is especially useful for devs and founders who cannot afford a designer or just want to make their UI look decent.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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 NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
19%19% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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.

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