Qdrant

A search-and-inference database from scratch in pure Zig

DuckDB is a good one, we're working on that especially for the serverless/lakehouse stuff we've got planned for the next release! I believe we originally had qdrant in our benchmarks but ran into an explosion of testing requirements for each provider and different licensing checks for each but I can drum up those numbers!

A search-and-inference database from scratch in pure Zig

Could you add qdrant and duckdb to the benchmarks?

Ask HN: Who wants to be hired? (September 2026)

Location: Jakarta, Indonesia (UTC+7) Remote: Yes — remote for 4 years, currently for a London company Willing to relocate: Yes for the right role (would need visa sponsorship) Technologies: Go, Python, TypeScript, PostgreSQL/Supabase, GCP (Pub/Sub, Cloud Run, Cloud Scheduler, Workflows, Secret Manager), Terraform, Docker, OpenTelemetry, Qdrant, Redis Résumé/CV: https://nawafilhusnul.com Email: nawafilhusnul@gmail.com I rebuild pipelines other people have stopped looking at. The one I'm proudest of: a regulatory-document ingestion platform that had been dark for two months, across 100+ ingestion configurations. I rebuilt it event-driven (Pub/Sub, Cloud Run, Terraform, Postgres) and — more usefully — gave it freshness SLOs and validation checks, so a silent fai…

Ask HN: Who wants to be hired? (September 2026)

Location: India Remote: Yes Willing to relocate: Bengaluru / open to the right opportunity Technologies: Python, TypeScript, FastAPI, Next.js, LangGraph, LangChain, RAG, MCP, Qdrant, pgvector, PostgreSQL, Docker, AWS Portfolio: https://namangupta.dev GitHub: https://github.com/namanxdev Email: [naman@namangupta.dev](mailto:naman@namangupta.dev) AI Systems Engineer working on production LLM systems, RAG, agents, and evaluation. Currently building multi-agent and retrieval systems handling 500+ daily queries. Experience with chunking, embeddings, reranking, vector databases, Precision@k / Recall@k / MRR evaluation, latency optimization, and production deployments. Built MCPHub, AgentMesh, and a RAG evaluation harness. Looking for full-time remote AI&#…

Show HN: EmbedFlow –> Upgrade embedding models without re-embedding your corpus

I was playing around with embedding models and I realized the moving between embedding models on a large corpus can cause a huge backfill, as all of the previous documents would have to be re embedded. embedflow tries to forgo that; it takes candidate documents from the previous index, and reranks them in realtime with the new model. It supports faiss, qdrant, and pgvector people who have migrated large production vector indexes, would this be helpful for you?