I built a "GitHub for SEOs" to solve the portfolio problem

Hacker News

I built SEOfolio to solve a problem I faced as an SEO: the lack of a dedicated portfolio platform. While designers have Behance and developers have GitHub, We (SEO professionals) waste hours creating case studies with screenshots from multiple tools that quickly become outdated. SEOfolio solves this by: - Creating a centralized portfolio of SEO projects - Displaying client traffic growth with auto-updating visualizations - Documenting strategies and results in one interface - Providing a single link to share during client pitches The platform eliminates the maintenance burden of keeping portfolios current. Would love feedback from fellow SEOs and other professionals.

Visit product View on Hacker NewsApr 22, 2025

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5points

2comments

Did not reach leaderboard

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

70%70% 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.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: visual, single · Missing: mac, agents, macos

35%35% 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

28%28% 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: ios · Missing: mobile apps, personal, entrepreneurs

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

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

18%18% 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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