SaaSpect – tool to analyze SaaS projects

Hacker News

Hey HN, I'm excited to share that my new project, SaaSpect, is now live! As a software developer passionate about the SaaS space, I've built this toolkit to help investors and VCs easily gather and analyze data on micro SaaS projects. SaaSpect offers a streamlined six-step process, leveraging Gemini AI for in-depth analysis and Firecrawl for necessary web scraping. This combination allows us to retrieve detailed project information, conduct competitive and market analysis, suggest enhancements, prepare questions, and perform investment analysis with precision. While I believe the tool is robust, I acknowledge that some aspects, particularly market analysis, could benefit from further refinement by experts. Your feedback is incredibly important to me as I continue to improve SaaSpect. If you find it useful, an upvote here or on the ProductHunt post would mean the world. Thank you for checking it out! Best,

Visit product View on Hacker NewsJun 30, 2024

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Actual performance

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Launch Intel predictions

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

80%80% 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: new, gemini · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

32%32% 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 · Missing: arr, mrr, revenue

10%10% 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

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

Correct prediction on native model

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