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linecast – weather, radar, tides and maps in the terminal

Hacker News

linecast – weather, radar, tides and maps in the terminal

I've been working on linecast, a free and open source collection of six terminal applications: weather, radar, sunshine, moon, tides, and maps. It's as if the Old Farmer's Almanac was built for Minitel. I made it because I'd been using the terminal a lot more over the past couple of years, I'm opinionated about weather forecast IA and design, and I liked the idea of making useful little terminal desk ornaments. The applications use ANSI color and Unicode braille characters rather than graphics. They reflow as the terminal is resized and derive their colors from your current terminal palette, including when the theme changes while they're running. They're mouse friendly where it makes sense. There's no account or required API key. Forecasts and air quality come from Open-Meteo; alerts, tides, and radar use various public sources; Sun and Moon positions are calculated locally; and the maps use OpenStreetMap-derived vector data and public elevation tiles. The code is just the Python stdlib with no package dependencies. If you have uvx, you can run it without installing: uvx linecast weather, uvx linecast radar, uvx linecast maps. It's also installable through Homebrew, uv tool, pipx, or pip. Source: https://github.com/ashuttl/linecast It currently supports macOS and Linux with Python 3.10+. It doesn't work over SSH and I haven't explored Windows support. Hope you enjoy checking it out! Feedback welcome.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, including · Missing: reddit linkedin, podcasting, created
87%87% 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: mac, macos, using · Missing: agents, agent, cursor
77%77% 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
76%76% 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
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
28%28% 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
11%11% 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.

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