Ghostty Theme Mixer (preview themes and fonts live)

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Quick context: I’ve been messing around with various coding tools and agents (GitHub Copilot, Lovable, Claude Code, Codex, etc.) for about 2 years. Before that I did zero development. I barely knew what GitHub was. I knew a bit of python and SQL but mostly spent my time in excel doing data analysis, sentiment analysis, and NLP. I never really thought of myself as a developer, and I’m not sure shipping this counts either. It’s the first open-source thing I’ve made, and I think might be useful to a few people so wanted to share it on here. I started using Ghostty recently and kept editing ~/.config/ghostty/config, reloading, squinting, editing again every time I wanted to try a theme. Built this over a weekend so you can preview 16 palettes and 4 fonts with a background-tint slider, then copy the config lines. One static HTML file. No build step, no tracking. Site: https://ghosttythemes.com/ Source: https://github.com/mcvalosborne/ghostty-theme-mixer Hope it’s useful.

Visit product View on Hacker NewsApr 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

82%82% 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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

68%68% 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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

17%17% 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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