Search Results for "postgres"

...It is especially useful for founders, builders, and “vibe coders” who want to move quickly without spending time on DevOps or backend setup. Butterbase also emphasizes open building blocks and reduced lock-in through a Postgres-backed architecture. Its main value is giving AI-assisted development workflows a ready backend that agents can operate directly.

...The platform supports a wide variety of document types, includes data isolation features for teams, and layers in security measures such as RBAC, row-level security in Postgres, strong content security policy settings, and minimal container builds.

GBrain is an open-source AI memory system designed to give autonomous agents persistent, structured, and scalable long-term memory across interactions and workflows. It operates by transforming large collections of markdown documents, personal notes, and external data into a searchable knowledge base backed by PostgreSQL and vector embeddings, enabling both semantic and keyword-based retrieval. The system is tightly integrated with agent frameworks such as OpenClaw and Hermes, allowing AI...

pg-aiguide is a tool designed to enhance AI-assisted development with PostgreSQL by providing structured knowledge and skills directly to coding agents. It acts as a bridge between database documentation and AI tools, enabling more accurate generation of SQL queries and database interactions. The system integrates with Claude Code through an MCP server, allowing agents to access curated PostgreSQL knowledge in real time. It focuses on improving developer productivity by reducing errors and...

database.build is an innovative in-browser development environment that allows users to instantly create and interact with fully functional PostgreSQL databases directly within their browser, powered by WebAssembly technology. It eliminates the need for traditional database setup by running a lightweight version of PostgreSQL locally using PGlite, enabling developers to experiment, prototype, and analyze data without relying on external servers. Each database instance is paired with a large...

...The project consolidates the typical steps of a RAG pipeline—including embedding generation, document retrieval, reranking, and text generation—into a single query executed within the Postgres ecosystem. By leveraging PostgresML and vector extensions such as pgvector, Korvus eliminates the need for external microservices typically used for AI search architectures, reducing both system complexity and latency. The architecture enables machine learning operations to occur directly in the database, minimizing data transfer between services and improving overall performance for large datasets.

...The repo stores the full text of his essays (chunked), uses embeddings (e.g. via OpenAI embeddings) to allow semantic search over that corpus, and hosts a chat interface that combines retrieval results with LLM-based answering — enabling RAG (retrieval-augmented generation) over a fixed dataset. The app uses a Postgres database (with pgvector) hosted on Supabase for its embedding store, making the backend relatively simple and accessible, and the frontend is again built with Next.js/TypeScript for a modern responsive UI. By pulling together search and chat, it creates a useful tool both for readers who want to revisit or explore Paul Graham’s ideas thematically, and for learners or researchers who want to query specific essays or concepts quickly.