Gigawatt, a shell prompt that adapts to your terminal theme

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About a year ago, I made myself a shell prompt with Starship. I'm not a huge fan of the bright loud colors in many prompts, so I configured a minimal prompt with subtle colors that fit my terminal theme. The problem is that I actually like changing my theme every now and then. And I use a different theme for my terminal app and e.g. VSCode. A single prompt config couldn't fit into everything. So I built the same prompt from scratch with Swift, and made it automatically adapt to the terminal colors, with proper Lab color interpolation. I used that until a couple of weeks ago, when I wanted pick up Rust. I was quite happy with the hacked-together prompt I had been using for a year, and thought that rewriting it might be a suitable project. I decided to turn this Rust version into an open source project, as I might not be the only one who'd enjoy a fancy powerline prompt without a screaming color palette. The source is available at https://github.com/juuso/gigawatt

Visit product View on Hacker NewsSep 21, 2025

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Indie HackersFits the IH revenue-focused audience · 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, code · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% 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 · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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.

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

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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