Open-Source Starter for SaaS Products

Hacker News

For the past few months, I've been thinking about building a few different SaaS products. The problem is that there's a lot of work to do before you even start building your product — adding support for recurring subscriptions, billing, GDPR compliance, authentication, . . . maybe your SaaS needs support for teams, scoped memberships, API key pairs, etc. etc., the list goes on. Introducing Staart — the starter template for SaaS products. Written in Node.js/TypeScript with a beautiful Vue.js/Nuxt.js Progressive Web App (PWA), Staart takes care of all the boilerplate required when building a SaaS product, so you can focus on what really matter — your core product. I'd love to hear what the community thinks! Check it out on Product Hunt: https://www.producthunt.com/posts/staart GitHub: Staart (backend) and Staart UI (frontend) https://github.com/o15y Live demo: https://staart-demo.o15y.com

Visit product View on Hacker NewsJun 25, 2019

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Actual performance

4points

6comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

66%66% 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: saas, recurring, subscription · Missing: arr, mrr, revenue

28%28% 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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