I Built My Product with Next.js After Getting Scammed by LegalZoom

Hacker News

Hi HN, When I was 16, three years ago, I got overcharged by LegalZoom for setting up an LLC. They made it seem more complicated than it is. You can actually do it yourself using your state's Secretary of State website. I made StateSmart to teach people the whole process. It covers: Understand the costs: Understanding all fees. Agent tips: Help with choosing a registered agent or becoming your own. Step-by-step instructions: Clear guidance on each document you need to file. Compliance: Tips on how to stay compliant with annual reports and filings. Everything online: All instructions and resources are available online. LegalZoom charged me hundreds for something you can do yourself. StateSmart helps you save money and stay in control. I built StateSmart using Next.js, Stripe, and Vercel. Thanks! Antonio

Visit product View on Hacker NewsJun 9, 2024

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

44%44% 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 · Missing: mobile apps, ios, personal

41%41% 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

39%39% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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