Life & fun · February 22, 2026
Semantic search over Hacker News, built on pgvector
I built https://ask.rivestack.io — a semantic search engine over Hacker News posts. Instead of keyword matching, it finds results by meaning, so you can search things like "best way to handle authentication in microservices" and get relevant threads even if they don't contain those exact words. How it works: Indexed HN posts and comments into PostgreSQL with pgvector (HNSW index) Embeddings generated with OpenAI's embedding model Queries run as nearest-neighbor vector searches — typical response under 50ms The whole thing runs on a single Postgres instance, no separate vector DB I…
What it does
In the maker’s words, at launch
I built https://ask.rivestack.io — a semantic search engine over Hacker News posts. Instead of keyword matching, it finds results by meaning, so you can search things like "best way to handle authentication in microservices" and get relevant threads even if they don't contain those exact words. How it works: Indexed HN posts and comments into PostgreSQL with pgvector (HNSW index) Embeddings generated with OpenAI's embedding model Queries run as nearest-neighbor vector searches — typical response under 50ms The whole thing runs on a single Postgres instance, no separate vector DB I built this partly because I wanted a better way to search HN, and partly to dogfood my own project — Rivestack (https://rivestack.io), a managed PostgreSQL service with pgvector baked in. I wanted to see how pgvector holds up with a real dataset at a reasonable scale. A few things I learned along the way: HNSW vs IVFFlat matters a lot at this scale. HNSW gave me much better recall with acceptable index build times. Storing embeddings alongside relational data in the same DB simplifies things enormously — no syncing between a vector store and your main DB. pgvector has gotten surprisingly fast in recent versions. For most use cases, you really don't need a dedicated vector database. The search is free to use. Rivestack has a free tier too if anyone wants to try something similar. Happy to answer questions about the architecture, pgvector tuning, or anything else.
Does the same job
all alternatives →- DFDeepHN – Full-text search of 30M Hacker News posts and linked webpages2021 · deephn.org · ▲324
- ASA search engine based on social signals2013 · buzzsumo.com · ▲46
We built a search engine that shows you the most engaging stories/topics being shared across Twitter, Facebook, Linkedin, and Google+. We crawled over 15 million articles the past 3 months, retrieved the total number of Facebook likes, tweets, Google+’s etc and built a search index around it. Here's what our infrastructure looks like: Rails/Redis: We use the Sidekiq gem as a message queue. We have hundreds of workers that do the crawling, data mining, and number crunching. ElasticSearch: We built the search index using ElasticSearch, with the data imported from our Postgres…
- VAVpuna AI Search – A semantic search platform2025 · aisearch.vpuna.com · ▲9
Dear HN Community, I am a long time fan and first-time contributor. I just launched a developer focused semantic search platform and wanted to share it with the community. The idea is simple: upload structured or unstructured documents, select the fields you want to index and tag as metadata, and instantly get a clean search API you can use in your own app. Here is what it currently supports: - Manage your own tenants and projects - Upload .json and .txt files (support for .pdf, .docx, .xlsx, .yml, etc. coming soon) - Expose 3 APIs: search, upload document (embeddings), and delete document -…
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