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ScreenCraft – Screenshot and PDF API Without the Puppeteer Headaches

Hacker News

ScreenCraft – Screenshot and PDF API Without the Puppeteer Headaches

Hey HN, I built a REST API for capturing screenshots and generating PDFs from URLs. Why: I got tired of managing Puppeteer/Playwright in production. Memory leaks, zombie processes, Docker issues. So I wrapped it in an API. Stack: - Node.js + Fastify - Playwright (more stable than Puppeteer in my experience) - Self-hosted on Hetzner Features: - Screenshots: PNG, JPEG, WebP - PDFs: A4, Letter, custom sizes - Full-page capture, custom viewports - Cookie consent auto-accept - Lazy-load handling API example: curl -X POST "https://www.screencraftapi.com/api/v1/screenshots" \ -H "Authorization: Bearer YOUR_KEY" \ -d '{"url": "https://example.com"}' Free tier: 250 requests/month Docs: https://www.screencraftapi.com/docs Would love technical feedback. What's missing? What would make this useful for your projects?

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
43%43% 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
29%29% 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
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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