17871 Color-Names & color tools

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Among others, I am maintaining, what I believe, is the largest collection of unique color-names. To go with it, I created a few tools to help users explore and understand the huge list: Color Picker (Klick the icon): https://codepen.io/meodai/full/mEvZRx/ Full List: https://codepen.io/meodai/full/VMpNdQ/ Color Name distribution: https://codepen.io/meodai/full/zdgXJj/ in different color spaces As well as some tools that are related to color in general and just made a bit nicer by adding unique names to hex values: color harmony generator: https://codepen.io/meodai/full/RerqjG , color space explorer: https://codepen.io/meodai/full/KagxwW , mouse position based color picker: https://codepen.io/meodai/full/bWOBja , color name bingo: https://codepen.io/meodai/full/ZPRQZz I am still missing lots of saturated colors. Any idea, how I could generate color values that are the furthest possible then the ones that are already there? (Something like "reverse nearest vector"?) The repository: https://github.com/meodai/color-names There are different ways to submit new names. Via twitter (automated bot is in the making), google docs form and of course via pull request on the repository. On every push, a list of the new colors is generated: https://raw.githubusercontent.com/meodai/color-names/master/... Also working on a more extensive tool to explore colors in general, that will end up here https://www.color.pizza/ in a not too far future I hope ;) https://www.youtube.com/watch?v=GCGUqAQcfFw Any thoughts, inputs, critiques and of course color name submissions are welcome! I should add that I don't do this for a particular reason other then enjoying the process of coming up with names, building color tools and optimising the whole workflow that surrounds the maintenance and publishing of the list: https://github.com/meodai/color-names/tree/master/scripts

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

77%77% 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

63%63% 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 · Strong signals: users · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, user, new · 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

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.

Incorrect prediction on native model

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