Over the last few months, I have built and launched a free semantic search tool for GitHub called SemHub. In this blog post, I share what I’ve learned and why I’ve failed, so that other builders can learn from my experience. This blog post runs long and I have sign-posted each section. I have marked the sections that I consider the particularly insightful with an asterisk (*).

I have also summarized my key lessons here:

1. Default to pgvector, avoid premature optimization.
2. You probably can get away with shorter embeddings if you’re using Matryoshka embedding models.
3. Filtering with vector search may be harder than you expect.
4. If you love full stack TypeScript and use AWS, you’ll love SST. One day, I wish I can recommend Cloudflare in equally strong terms too.
5. Building is only half the battle. You have to solve a big enough problem and meet your users where they’re at.

Read in full here: