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Generate PDFs at Scale on Kubernetes Using OpenFaaS and Puppeteer

Hacker News

Generate PDFs at Scale on Kubernetes Using OpenFaaS and Puppeteer

I've had a few calls with users recently who are moving from AWS Lambda to bare Kubernetes, and struggling to get this kind of working scaling right for them. So we sat down and put together an example to show how it could be done. Why is this Show HN? It's a code example, it's something we wrote that didn't exist before in this way and you can run it and try it out. We also added a detailed write-up that we're posting here. Disclaimer: we both work at OpenFaaS Ltd and maintain the OSS project full time.

Visit productView on Hacker NewsOct 7, 2022
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos
86%86% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist · Missing: https docs, excited, just released
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users, calls · Missing: plus, platform, intuitive
43%43% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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