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Top Neovim/iTerm theme pairings (color compatibility scoring)

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Top Neovim/iTerm theme pairings (color compatibility scoring)

I made a list of Neovim/iTerm theme pairings that look cohesive when used together. Instead of picking by eye, I scored them by color palette similarity (nearest-neighbor matching) and balance across backgrounds, highlights, and syntax colors. Top 10 Pairings: • Everblush × nightly.nvim • Tomorrow Night × rusty • Rose Pine × Sakura.nvim • Challenger Deep × DarkScene.vim • Atom One Dark × onepro • Black Metal (Marduk) × nolife.nvim • Mellow × oldtale.nvim • Nvim Dark × monoglow.nvim • Gruvbox Dark × gruvsquirrel.nvim • Cursor Dark × nordic.nvim Links: Video gallery: https://youtube.com/shorts/4803KinmpVw?si=QcoMprBlrDFWVq83 Theme list (TSV with scores): https://github.com/samesense/colors/blob/main/data/end/top10... Methods: https://rpubs.com/samesense/theme_colors Screenshots: https://github.com/samesense/colors/tree/main/data/end/scree... Note: Obvious one-to-one matches (Catppuccin→Catppuccin, Nord→Nord) aren’t included.

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% 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.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
53%53% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: cursor · Missing: mac, agents, macos
16%16% 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 · Missing: web3, chat, crypto
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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