PeekShot – Automate Website Screenshots via Simple REST API

Hacker News

Hey HN! We built PeekShot, a tool to automate website screenshots at scale. It’s aimed at developers, SaaS founders, marketers, and QA teams who need consistent, pixel-perfect captures — without clicking around manually. Why we built it: We kept running into the same problem: getting consistent, high-res screenshots for dashboards, changelogs, QA, and marketing. Existing tools were either flaky, slow, or expensive — so we decided to build our own. Key Features: - REST API → start capturing in <60s - Flexible modes → full-page, viewport, or element-level - Device & viewport simulation → mobile, desktop, custom sizes - High-res output → Retina, HD, custom DPI - Webhook delivery, custom headers, proxy support - Integrates easily with CI/CD, dashboards, analytics pipelines Free Tier: 100 credits, no credit card required. I’d love feedback — happy to share API examples, discuss integrations, or hear how you’d use this!

Visit product View on Hacker NewsAug 28, 2025

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Actual performance

1points

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

90%90% 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 · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · 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.

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

37%37% 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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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.

Correct prediction on native model

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