Swiftwave – Simple Lightweight Open Source PaaS

Hacker News

Hi HN! I am Tanmoy & today I am releasing stable V2 of Swiftwave. SwiftWave is a self-hosted, lightweight PaaS solution for easily deploying and managing your applications on any server . It's an open-source alternative to Heroku, Netlify, and Render. You can install swiftwave in bare metal, Raspberry Pi, in any VPS(Hetzner, DigitalOcean, Linode, AWS, GCP etc.). Swiftwave and its dependencies are compatible with AMD64, ARM64 and ARMv7 architectures and compatible with Debian/Ubuntu/Raspbian OS/Fedora/CentOS/AlmaLinux/RockyLinux. Checkout https://swiftwave.org/ for more details. If you like the project, consider to give this project a star in Github: https://github.com/swiftwave-org/swiftwave

Visit product View on Hacker NewsMay 30, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

80%80% 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: open · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% 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

36%36% 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

12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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