Search Results for "llm"

...The system runs as an intelligent hardware agent runtime inside the local network. It discovers devices, tracks their state, and controls real hardware through adapters. Anima uses an LLM-powered brain to interpret the environment, user intent, memory, and device-specific skills before planning actions. It currently supports Mi Home and MIoT devices while providing a dashboard, REST API, and CLI for observation, debugging, control, and extension.

OpenClaw Opik Observability Plugin is an open-source plugin designed to add observability and monitoring capabilities to OpenClaw autonomous AI agents by exporting operational traces to the Opik observability platform. The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a...

Grafana OSS is the leading open-source platform for visualization and observability. It enables teams to query, visualize, alert on, and explore telemetry data from multiple sources in a single interface. With support for 100+ data source plugins—including Prometheus, Loki, Elasticsearch, InfluxDB, SQL/NoSQL databases, and OpenTelemetry—Grafana helps teams correlate metrics, logs, and traces across applications and infrastructure. Users can build interactive dashboards with rich...

FreeAskInternet is a self-hosted search and answer application that combines web results with large language models. A local SearXNG instance sends queries to multiple search engines and returns a broader collection of sources. The backend crawls selected pages, supplies their content to a configured model, and streams a synthesized response to the chat interface. It supports several hosted model services as well as custom local backends such as Ollama and llama.cpp. The web interface is...

...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.