"Blog as a Service" for Next.js Sites

Hacker News

Hi HN I built https://www.next-blog-ai.com because I was tired of wasting time spinning up blogs for my side projects. As a dev, I just wanted an easy way to boost SEO for my sites without constantly writing, formatting, publishing, storage and managing blog content. What it does: - Gives you a full-blown blog for your Next.js site with 5 minutes of setup - Generates SEO-friendly blog posts automatically using AI (but you can also write your own!) using the target audience, keywords, topics, and industry that you provide for your project - Fully automated publishing (you set the schedule – it handles the rest) - Handles sitemap updates, SEO meta tags, and more - Free tier available, great for indie projects and experimentation I’ve launched it on 3 of my own projects already: - https://www.rankresume.io/blog - https://www.ideapulse.io/blog - https://www.germanlanguagepractice.com/blog The AI-generated posts are just one feature — I use it for manual posts too. The goal is to remove all the friction of maintaining a blog so you can focus on shipping your actual product. Would love to hear your thoughts — especially on how you currently tackle blogging for your projects, and what would make this more useful for you. Thanks!

Visit product View on Hacker NewsMay 20, 2025

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Launch Intel predictions

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

83%83% 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: using · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, 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

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

16%16% 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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