I

I built a tool to describe ~4.3B colors

Hacker News

I built a tool to describe ~4.3B colors

A simple tool I made over the week to explore and learn about different colors. You can select any color with any opacity #000000-FFFFFFFF (~4.3 billion colors/variants), and you can view a dedicated page detailing the color's closest name, conversions to Hex, RGB, CMYK, etc., shades, tints, tones, harmonies, opacities, and WCAG contrast compliance.

Visit product View on Hacker NewsJun 10, 2022

Actual performance

8points

1comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

50%50% 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.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

45%45% 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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

18%18% 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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Javadoc-Themer – Give your boooring javadocs a splash of colors39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Javadoc-Themer – Give your boooring javadocs a splash of colors

What if Dr. Mario had more than 3 colors?44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

What if Dr. Mario had more than 3 colors?

vim-colors-vanilla41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

vim-colors-vanilla

Colors – Experimenting with Construct 236%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Colors – Experimenting with Construct 2

We built a tool for RISC-V alphanumeric shellcoding36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

We built a tool for RISC-V alphanumeric shellcoding

ReflectOps – I built tool for retrospectives36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ReflectOps – I built tool for retrospectives

I built a refund tool for late deliveries – here's what happened47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I built a refund tool for late deliveries – here's what happened

Search for named colors or similar colors52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Search for named colors or similar colors

A simple tool to discover colors48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A simple tool to discover colors

Hex Colors70%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Do you love colors?

Indie Hackers33$100/moeducation

Browse all products Analyze your launch