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Semble is a fast code search library for AI agents that uses ~98% fewer tokens than grep+read, runs on CPU with no external dependencies, and integrates via MCP or CLI.
This paper introduces CoREB, a contamination-limited multitask benchmark for code search that evaluates text-to-code, code-to-text, and code-to-code retrieval with fine-tuned reranking capabilities.
OpenAI released text-embedding-ada-002, a unified embedding model that consolidates five previous models into one with superior performance, 4x longer context (8192 tokens), smaller dimensionality (1536), and 99.8% lower pricing than previous Davinci embeddings.
OpenAI introduces a new embeddings API endpoint that converts text and code into numerical vector representations for semantic search, clustering, and classification tasks. The models achieve state-of-the-art results on standard benchmarks including a 20% relative improvement in code search performance.
OpenAI presents a contrastive pre-training approach for generating high-quality text and code embeddings at scale without supervision, achieving state-of-the-art results on linear-probe classification, semantic search, and code search benchmarks.
Sourcebot has launched an open-source MCP (Model Context Protocol) server that connects AI coding agents like Cursor, Claude Code, and Copilot to an entire codebase for search, file reading, and reference resolution. It supports OAuth 2.0 and API key authorization with a quick 1-minute install.