Take screenshots of any website from your terminal

Hacker News

Hey HN! I built a small CLI tool to solve my own problem - keeping documentation screenshots up to date. The idea is simple: point it at any URL, pick what you want to capture, and run one command whenever your UI changes. Just run "npx heroshot" - that's it. Opens a visual picker, you click what you want, it saves the config. Run it again anytime to refresh all screenshots. Works with light/dark mode, multiple viewports (desktop, tablet, mobile), and sites behind login. I use it in CI so my docs never show outdated UI. Install options: - npx heroshot (no install needed) - npm install -g heroshot - brew install omachala/heroshot/heroshot - curl -fsSL https://heroshot.sh/install.sh | sh - docker pull heroshot/heroshot It's free and open source. Would love to hear what you think!

Visit product View on Hacker NewsJan 26, 2026

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

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 · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, 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

62%62% 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

36%36% 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

33%33% 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

15%15% 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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