OpenSEO Studio – a static, BYOK SEO article generator (v1.0)

Hacker News

Hi HN, I’m sharing OpenSEO Studio v1.0, a fully static SEO article generator. The idea is simple: – No backend – No accounts – No tracking – BYOK via OpenRouter – Runs entirely in your browser – Outputs clean Markdown only I built this because most SEO/AI writing tools are SaaS-first, opaque, and over-engineered. I wanted something transparent, cloneable, and easy to self-host. What it does: – Generate long-form SEO articles – Contextual generation options (not hidden in settings) – Export Markdown or download .md files – Ghost / WordPress exports are prepared (UI only for now) What it deliberately does not do: – No user accounts – No database – No server-side storage – No “AI platform” lock-in The project is MIT-licensed and fully client-side. You can verify everything by reading the source. Live demo: https://openseo.studio Repo: https://github.com/Vellis59/openseo-studio Happy to answer questions or get feedback.

Visit product View on Hacker NewsDec 24, 2025

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

75%75% 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: user, context, open · Missing: mac, agents, macos

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

AppSumoStrong fit for a featured deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

35%35% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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