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Open-source Docker image to run Chrome browsers in your cloud

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Open-source Docker image to run Chrome browsers in your cloud

If you’ve ever worked with web scraping or automation using Puppeteer or Playwright, you know that running Chrome can be tricky. While headless mode (Chrome with no display) often works, some page elements only render in headed mode, forcing you to run Chrome with a display. This usually means bloating your Dockerfile with complex dependencies like xvfb. Updating your server or Lambda container to handle this can increase costs and lead to dependency hell. The Solution: I’ve built a streamlined solution—a Docker image that runs Chrome with all its necessary dependencies and exposes a FastAPI interface. This decouples the browser logic from your main codebase, letting you focus on scraping or automation without worrying about the underlying setup. The container exposes a WebSocket endpoint at ws://localhost:8000/ws, allowing you to connect Playwright or Puppeteer over Chrome DevTools Protocol (CDP). For example: browser = await pw.chromium.connect_over_cdp('ws://localhost:8000/ws') Each connection to the WebSocket spins up a new Chrome session in headed mode, and you can run multiple sessions concurrently. Unlike other solutions, Finic is fully open-source (Apache 2.0 licensed). You can try it by pulling the official Docker image or cloning the repo at Finic on GitHub ( https://github.com/finic-ai/finic ) and running `docker-compose up --build`. This approach simplifies handling Chrome for scraping, saves on cloud costs, and keeps your setup clean. Give it a try!

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, new, using · Missing: mac, agents, macos
90%90% 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: 000, io · Missing: https docs, excited, just released
64%64% 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 · Strong signals: host, interface · Missing: plus, platform, intuitive
42%42% 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
41%41% 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
18%18% 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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