Gradienty V2 – Free Tailwind CSS Gradient, Mesh, and Blob Generators

Hacker News

Hey HN, I’m excited to share Gradienty V2, a major overhaul of a tool I launched a year ago. Gradienty was originally designed to help developers and designers generate Tailwind CSS gradients quickly, but based on user feedback and community input, I’ve expanded it significantly. What is Gradienty? Gradienty V2 is a free web tool for generating: CSS gradients (background, text, box shadow) Glassmorphism effects Mesh gradients Customizable blob shapes The goal is to simplify the design process for developers and designers by providing ready-to-use CSS code with just a few clicks. It’s built to be straightforward, without any paywalls or sign-ups, so you can jump right in and start using it immediately. Why should you check it out? Whether you need a quick way to create complex designs for a project or are just looking for some inspiration, Gradienty V2 has you covered. It’s an evolving tool that I’ve continuously refined based on feedback, and I’m always open to suggestions for new features. You can try it out directly here: Gradienty.codes. I’d love to hear your feedback, thoughts, or ideas for improvement!

Visit product View on Hacker NewsOct 14, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

56%56% 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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

9%9% 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.

Incorrect prediction on native model

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