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Create your color palettes in context, not isolation

Hacker News

Create your color palettes in context, not isolation

As a developer working with UI/UX teams, I’ve seen how much of a pain it still is to create accessible, well-balanced color palettes. A colleague of mine (UI/UX designer) mentioned how frustrating it is to: - Generate tints and shades from a brand color - Check WCAG accessibility contrast - Preview how those colors will actually look on buttons and components - Then jump between 2–3 tools just to get something usable So I built a tool to help fix that. 1. Choose a base color 2. Generate automatic tints/shades 3. Get WCAG contrast ratings live (against black/white backgrounds) 4. See automatically suggested complementary colors 5. And drop your palette directly onto real UI components (buttons for now, more coming) to visualise how your palette actually looks in a design system. You get to create your color palettes in context, not isolation Here’s the tool (free, no signup required to get started): https://colorpal-sage.vercel.app/ I'd appreciate feedback from this community on: - Is the new UX clear or confusing? - Is the “component playground” something you’d actually use? - Anything that feels unnecessary or missing? - Anything else? I am genuinely grateful for any insights from designers or developers working with colour systems. Thanks in advance!

Visit product View on Hacker NewsJul 22, 2025

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

64%64% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

39%39% 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 · Missing: mobile apps, ios, personal

39%39% 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

37%37% 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

13%13% 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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