I Cracked the SaaS Code After 7 Tries

Hacker News

Hi, Everyone. After 7 failed startups and countless GitHub commits, I've open-sourced my hard-earned lessons into LeanLaunchPad. It's a curated knowledge base where I deep-dive with bootstrapped SaaS founders, reverse-engineering their success. From choosing between monoliths and microservices to optimizing CAC and reducing churn, we decode the algorithms of successful SaaS businesses. No legacy code here—just cutting-edge strategies for MVP development, Acquiring the first few customers, and scaling your SaaS. Whether you're debugging your tech stack or refactoring your go-to-market strategy, LeanLaunchPad can be your IDE for SaaS success. You can check a Free Case study here: https://www.leanlaunchpad.co/startupPosts/makelogo.ai

Visit product View on Hacker NewsJun 29, 2024

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Actual performance

4points

5comments

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide · 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.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code, open · Missing: mac, agents, macos

14%14% predicted probability of success on Product Hunt, 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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