Ask HN: How Are You Doing RAG Locally?
by hacker-news-community
Simple retrieval often outperforms complex vector infrastructure—BM25, SQLite FTS5, and grep handle most local RAG use cases better than dedicated vector databases.
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(6)Anthropic Economic Index: New Building Blocks for Understanding AI Use
by anthropic
AI's productivity gains shrink dramatically when you account for task reliability—Anthropic's new 'economic primitives' reveal that complex tasks show greater speedup but lower success rates, and AI disproportionately affects higher-skilled work.
Building docfind: Fast Client-Side Search with Rust and WebAssembly
by joao-moreno
A client-side search engine built entirely in Rust and WebAssembly can deliver sub-millisecond queries without external dependencies—Copilot made the unfamiliar stack approachable.
AI Solves Erdős Problem #728
by terence-tao
The breakthrough in AI-assisted mathematics isn't just solving problems—it's the emerging capability to rapidly write and rewrite expositions of solutions.
AI-Powered Search
by trey-grainger, doug-turnbull, max-irwin
The holy grail for AI-powered search lies at the intersection of semantic search, personalized search, and domain-aware recommendations—systems that understand the domain, the user, and can match arbitrary queries to any content.
How to Implement a Cosine Similarity Function in TypeScript for Vector Comparison
by alexander-opalic
Cosine similarity measures the angle between two vectors—not their magnitude—making it the essential building block for semantic search, AI recommendations, and any application comparing text embeddings.