A developer built an AI-powered reverse hiring platform using Next.js, Supabase, and OpenAI. The platform flips the traditional hiring model by requiring companies to find candidates, disclosing salar…
A tutorial shows developers how to give AI agents persistent long-term memory using Postgres with the pgvector extension and OpenAI embeddings, eliminating the need for external vector databases. The …
Google's Gemini embedding models underwent a major pricing restructuring between 2024 and 2026, with the cost per million tokens increasing tenfold from $0.02 for the legacy text-embedding-004 to $0.2…
A developer tested HyDE (Hypothetical Document Embeddings) against standard retrieval on three query types and found it improved conceptual searches but failed on internal company policy and exact pro…
ForthWrite, an AI email assistant that learns a user's writing voice from every edit they send, launched on Hacker News. The tool uses retrieval-augmented generation (RAG) with Gmail Sent folder impor…
A developer optimized pgvector performance for the vibe-memory semantic memory service, reducing query time from 800ms to 15ms. Switching from the default IVFFlat index to HNSW provided a 5x improveme…
A developer advocates using PostgreSQL with the pgvector extension for semantic search instead of dedicated vector databases like Pinecone or Weaviate. The approach reduces infrastructure complexity b…
A developer built CVChatly, an automated career coach, using RAG and Agentic Workflows to eliminate LLM hallucinations. The architecture uses a vector database for factual retrieval and a LangGraph-ba…
A production RAG system handling 12,000 queries per day suffered an estimated $40,000 in losses over three weeks due to silent errors caused by vector embedding drift. The root cause was an embedding …