Over the past few months I’ve been working on an AI tools discovery platform. While the idea itself (a catalog of AI tools) may sound straightforward, the technical challenges of building, scaling, and optimizing such a site turned out to be much more interesting than I expected. I wanted to share some lessons learned, in case it’s useful to others building large data-driven web apps.
Some of the key challenges & solutions:
1.Managing 100+ AI tools data
Building a clean data structure for tools with categories, pricing models, ratings, and use cases.
Automated ingestion pipeline: a custom crawler that fetches new tools and updates existing ones.
2.Search and filtering at scale
Implemented full-text search with Supabase (Postgres + pgvector).
Learned a lot about indexing, query optimization, and real-time search performance.
3.SEO and structured data
Implemented schema.org for each tool to improve discoverability.
Handled multilingual SEO (EN/中文) with Next.js i18n routing.
4.Performance & UX
Next.js 14 App Router + dynamic routes for tools and categories.
Tailwind for responsive design, optimized images with Next.js Image.
Load time optimizations for mobile users.
5.Content system (Blog)
Added a blog engine for reviews, tutorials, comparisons, and industry news.
Built with Supabase for real-time analytics: article views, shares, user reading behavior.
Takeaways
Building a “directory” is less about listing links and more about solving scaling, data quality, and UX problems.
Next.js 14 + Supabase turned out to be a powerful combo for real-time + SEO-friendly apps.
Crawlers and structured data are underrated but crucial when dealing with fast-moving ecosystems like AI.
1 comment
[ 2.5 ms ] story [ 15.3 ms ] threadOver the past few months I’ve been working on an AI tools discovery platform. While the idea itself (a catalog of AI tools) may sound straightforward, the technical challenges of building, scaling, and optimizing such a site turned out to be much more interesting than I expected. I wanted to share some lessons learned, in case it’s useful to others building large data-driven web apps.
Some of the key challenges & solutions:
1.Managing 100+ AI tools data
Building a clean data structure for tools with categories, pricing models, ratings, and use cases.
Automated ingestion pipeline: a custom crawler that fetches new tools and updates existing ones.
2.Search and filtering at scale
Implemented full-text search with Supabase (Postgres + pgvector).
Learned a lot about indexing, query optimization, and real-time search performance.
3.SEO and structured data
Implemented schema.org for each tool to improve discoverability.
Handled multilingual SEO (EN/中文) with Next.js i18n routing.
4.Performance & UX
Next.js 14 App Router + dynamic routes for tools and categories.
Tailwind for responsive design, optimized images with Next.js Image.
Load time optimizations for mobile users.
5.Content system (Blog)
Added a blog engine for reviews, tutorials, comparisons, and industry news.
Built with Supabase for real-time analytics: article views, shares, user reading behavior.
Takeaways
Building a “directory” is less about listing links and more about solving scaling, data quality, and UX problems.
Next.js 14 + Supabase turned out to be a powerful combo for real-time + SEO-friendly apps.
Crawlers and structured data are underrated but crucial when dealing with fast-moving ecosystems like AI.
If you’re curious, the live version is here: https://www.toolsverse.tools/
I’d love feedback from anyone who has worked on large catalogs, content-heavy sites, or SEO/data-driven projects.