AI Hug – The 6th Website Built by a Newbie Developer

Hacker News

----Background---- I'm primarily a tech support guy, originally with no connection to development. But in the last couple of years, AI has advanced so rapidly that even a novice like me can leverage its power to build websites. Due to economic pressures lately, I've been exploring ways to make money overseas, which is why I'm learning web development. This site is my 6th project. Of my first 5 sites, three were built with WordPress, which didn't really involve much development. The fourth site was made using JS, HTML, Tailwind, and Cloudflare, but as a practice exercise, I only created a simple static page. For the fifth site, I jumped straight into using the trendy Next.js framework. With the help of third-party APIs, I built a site for automatic image expansion. However, it hasn't gained traction yet. Oddly enough, despite adding many backlinks, only the one from a site was picked up by Google Search Console. ----Main Point---- After reviewing my experience with these previous sites, I've realized that it's not necessary to develop all the features before launching. Instead, it's better to start with a landing page, focus on SEO, and then develop additional features once the site's Google ranking improves. With my previous site ( https://aiexpandimage.org ), I finished the landing page in just a few hours, but spent a good week or two developing the features. Currently, the average Google ranking is around 64, so it'll take time to gradually add backlinks and promote the site. I've wisened up now. For my new site ( https://ai-hug.org ), I've only created a landing page. I'm focusing on SEO first, and I'll develop the core features and payment system once the traffic starts coming in.

Visit product View on Hacker NewsSep 11, 2024

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

96%96% 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: google, new, using · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, 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

49%49% 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: google, way · Missing: mobile apps, ios, personal

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

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: make money · 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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